# AI-Code-Review Integration Feasibility Analysis

**Date:** 2026-02-22
**Project:** mcp-vector-search
**Issue:** GitHub Issue #89 from `bobmatnyc/ai-code-review`

## Executive Summary

**Verdict:** Feasible integration, but Issue #89 proposes significant architectural overlap. Recommend **Phase 1 Alternative** that leverages existing capabilities rather than wholesale integration.

**Key Findings:**
- mcp-vector-search already has sophisticated analysis infrastructure (`analyze` command)
- ai-code-review repository not found in expected locations
- Existing LLM client supports tool calling (foundation for prompt-driven analysis)
- Knowledge graph provides architectural context unavailable in traditional static analysis
- SARIF output format already implemented
- Chat system provides interactive analysis interface

**Recommendation:** Extend existing analysis capabilities with specialized review prompts rather than importing external prompt system as submodule.

---

## 1. Current State: mcp-vector-search Analysis Capabilities

### 1.1 Existing `analyze` Command

**Location:** `src/mcp_vector_search/cli/commands/analyze.py`

**Current Capabilities:**

#### Complexity Analysis
- **Collectors:**
  - `CognitiveComplexityCollector` - measures cognitive load
  - `CyclomaticComplexityCollector` - control flow complexity
  - `NestingDepthCollector` - nesting levels
  - `ParameterCountCollector` - function parameters
  - `MethodCountCollector` - class methods

- **Quick Mode:** Cognitive + Cyclomatic only (lines 467-472)
- **Full Mode:** All 5 collectors (lines 480-488)

#### Code Smell Detection
- **Built-in Smells** (lines 632-644):
  - Long Method
  - Deep Nesting
  - Long Parameter List
  - God Class
  - Complex Method

#### Output Formats (lines 162-168, 333-346)
```python
valid_formats = ["console", "json", "sarif", "markdown"]
```

**SARIF Support:** Fully implemented in `analysis/reporters/sarif.py` (378 lines)
- SARIF 2.1.0 compliant
- GitHub Actions integration ready
- Rule-based reporting with fingerprints

#### Dead Code Analysis
- **Command:** `analyze dead-code` (lines 1156-1282)
- **Capabilities:**
  - Call graph analysis
  - Entry point detection
  - Confidence scoring (high/medium/low)
  - Custom entry points support

#### Baseline Comparison
- **Features** (lines 194-230):
  - Save baseline: `--save-baseline <name>`
  - Compare: `--compare-baseline <name>`
  - Force overwrite: `--force`
  - List baselines: `--list-baselines`

#### Filters (lines 188-199)
- Changed files only: `--changed-only`
- Baseline branch comparison: `--baseline main`
- Language filter: `--language python`
- Path filter: `--path src/core`

### 1.2 Chat System Analysis Tools

**Location:** `src/mcp_vector_search/cli/commands/chat.py`

**Tool:** `analyze_code` (lines 779-791, 1112-1190)

```python
{
    "type": "function",
    "function": {
        "name": "analyze_code",
        "description": "Get code quality metrics for the project. Returns complexity, code smells, and recommendations.",
        "parameters": {
            "focus": {
                "type": "string",
                "description": "Analysis focus: 'summary', 'complexity', 'smells', or 'all'",
                "default": "summary",
            },
        },
    },
}
```

**Focus Modes:**
- `summary` - Project overview (files, functions, average complexity, health grade)
- `complexity` - Top 5 complexity hotspots
- `smells` - Top 10 code smells with severity
- `all` - Full JSON export (first 5000 chars)

**Integration:** Chat system can already invoke analysis programmatically through tool calls (lines 1439-1551).

### 1.3 LLM Client Capabilities

**Location:** `src/mcp_vector_search/core/llm_client.py`

**Key Features:**

#### Provider Support (lines 34-54)
```python
DEFAULT_MODELS = {
    "openai": "gpt-4o-mini",
    "openrouter": "anthropic/claude-opus-4.5",
    "bedrock": "anthropic.claude-3-5-sonnet-20241022-v2:0",
}

THINKING_MODELS = {
    "openai": "gpt-4o",
    "openrouter": "anthropic/claude-opus-4.5",
    "bedrock": "anthropic.claude-opus-4-20250514-v1:0",
}
```

#### Tool Calling (lines 937-1030)
```python
async def chat_with_tools(
    self, messages: list[dict[str, Any]], tools: list[dict[str, Any]]
) -> dict[str, Any]:
```

**Status:** Fully implemented for OpenAI and OpenRouter, Bedrock falls back to regular chat (line 957-961).

**Relevance:** This is the foundation for prompt-driven analysis. We can define new analysis "tools" that execute specialized review prompts.

#### Streaming Support (lines 704-882)
```python
async def stream_chat_completion(messages) -> AsyncIterator[str]
async def _bedrock_stream_chat_completion(messages) -> AsyncIterator[str]
```

### 1.4 Knowledge Graph Capabilities

**Location:** `src/mcp_vector_search/cli/commands/kg.py`

**Architecture Understanding:**

#### Call Graph Analysis (lines 1115-1173)
```bash
mcp-vector-search kg calls "SemanticSearchEngine.search"
```
- Functions called by target
- Functions calling target
- Bidirectional call analysis

#### Inheritance Tree (lines 1176-1234)
```bash
mcp-vector-search kg inherits "BaseModel"
```
- Parent classes
- Child classes
- Full inheritance hierarchy

#### Knowledge Graph Queries (lines 629-684)
```bash
mcp-vector-search kg query "entity_name" --hops 2
```

#### Interactive Visualization (lines 1237-1970)
- D3.js force-directed graph
- Interactive expansion (double-click nodes)
- Relationship filtering
- Cross-entity patterns

**Unique Value:** Knowledge graph provides **architectural context** that traditional static analysis lacks:
- Call chains reveal feature boundaries
- Import patterns show coupling
- Inheritance trees expose design hierarchy
- Documentation links connect specs to implementation

---

## 2. Current State: ai-code-review Project

### 2.1 Repository Status

**Expected Location:** `~/Duetto/repos/ai-code-review`
**Status:** **NOT FOUND**

**Search Results:**
```bash
$ ls -la ~/Duetto/repos/ 2>/dev/null | grep -i "ai-code-review"
# No output

$ find ~/Duetto -type d -name "*ai*code*review*" 2>/dev/null
# No results
```

**Implication:** Cannot directly inspect prompt system structure. Analysis must proceed based on Issue #89 description.

### 2.2 Inferred Prompt System (from Issue #89)

**Proposed Architecture:**
1. **Git Submodule:** Add ai-code-review as `vendor/ai-code-review/`
2. **Prompt Library:** 15+ specialized review types:
   - Security review
   - Architectural review
   - Performance review
   - Code quality review
   - Accessibility review
   - Documentation review
   - Test coverage review
   - Dependency review
   - Error handling review
   - Concurrency review
   - Database query review
   - API design review
   - Configuration review
   - Logging/monitoring review
   - Compliance review

3. **Prompt Template Format:** (assumed structure)
   ```
   {category}/
     ├── security.prompt
     ├── architecture.prompt
     ├── performance.prompt
     └── ...
   ```

4. **Integration Point:** Issue #89 proposes:
   ```bash
   mcp-vector-search analyze code-review --type security --format sarif
   ```

### 2.3 Missing Information

Without access to ai-code-review repository, we cannot verify:
- Prompt template format (Markdown? JSON? Custom DSL?)
- Variable interpolation mechanism
- Context passing strategy
- Output parsing requirements
- Prompt versioning scheme
- Maintenance model

---

## 3. Integration Points Analysis

### 3.1 Vector Search Enhancement

**Current Search:** `search_code` tool (chat.py, lines 973-1052)
```python
async def _tool_search_code(
    query: str,
    limit: int,  # max 15 results
    search_engine: Any,
    database: Any,
    project_root: Path,
    config: Any,
    session: EnhancedChatSession | None = None,
) -> str:
```

**Enhancement Opportunity:**
- Specialized review prompts could use `search_code` to find security patterns, performance bottlenecks, etc.
- Knowledge graph context already added for top 3 results (lines 1016-1046)

**Example Integration:**
```python
# Security Review Prompt
security_context = await search_code(
    query="authentication authorization secrets api_key password",
    limit=15,
    include_kg_context=True,
)

# Pass to LLM with security review instructions
response = await llm_client.chat_with_tools(
    messages=[
        {"role": "system", "content": SECURITY_REVIEW_PROMPT},
        {"role": "user", "content": f"Review this code:\n{security_context}"},
    ],
    tools=analysis_tools,
)
```

### 3.2 Knowledge Graph Intelligence

**Current KG Integration:** Chat system already integrates KG context (chat.py, lines 1017-1046)

```python
if hasattr(search_engine, "_kg") and search_engine._kg is not None:
    kg = search_engine._kg
    if not kg._initialized:
        await kg.initialize()

    # Get basic relationships
    related = await kg.find_related(entity_id, max_hops=1)
```

**Enhancement Opportunity:**
- **Architectural Review:** Use KG to trace feature boundaries
  - "Find all functions in the authentication flow"
  - KG call graph reveals complete call chain

- **Dependency Review:** Use KG import relationships
  - "What external dependencies does payment processing use?"
  - KG tracks transitive imports

- **Security Review:** Use KG to find sensitive data flows
  - "Trace user input from controller to database"
  - KG follows CALLS → CONTAINS → REFERENCES chains

**Example KG-Enhanced Review:**
```python
# Architectural Review with KG
def analyze_feature_boundary(feature_entry_point: str):
    # Get call graph (what this calls, what calls this)
    calls = await kg.get_call_graph(feature_entry_point)

    # Get inheritance tree (class hierarchy)
    hierarchy = await kg.get_inheritance_tree(feature_entry_point)

    # Find cross-cutting concerns (shared utilities)
    related = await kg.find_related(feature_entry_point, max_hops=3)

    # Pass comprehensive context to LLM
    return await llm_client.generate_answer(
        query="Analyze this feature's boundaries and coupling",
        context=format_kg_context(calls, hierarchy, related),
    )
```

### 3.3 CLI Command Design

**Option 1: New Top-Level Command** (Issue #89 Proposal)
```bash
mcp-vector-search analyze code-review --type security --format sarif
```

**Problems:**
- Ambiguous: Is this `analyze code-review` or separate command?
- Overlaps with existing `analyze` command
- Doesn't leverage existing `analyze` infrastructure

**Option 2: Subcommand of `analyze`** (Better)
```bash
mcp-vector-search analyze review --type security --format sarif
mcp-vector-search analyze review --type architecture --output report.md
mcp-vector-search analyze review --type performance --changed-only
```

**Advantages:**
- Consistent with existing `analyze complexity`, `analyze dead-code`
- Reuses existing filters: `--changed-only`, `--baseline main`, `--path src/`
- Reuses output formats: `--format sarif|json|markdown`
- Natural namespace: `analyze` for all analysis commands

**Option 3: Chat Integration** (Most Natural)
```bash
# Interactive REPL
$ mcp-vector-search chat
> do a security review of the auth module
> analyze architectural coupling in the payment system
> check for performance issues in the database layer

# Single query
$ mcp-vector-search chat "security review the authentication code"
```

**Advantages:**
- Leverages existing chat infrastructure
- Natural language interface (no need for `--type` flags)
- Can ask follow-up questions
- Iterative refinement
- Already integrated with analysis tools

### 3.4 Output Format Compatibility

**Existing Formats:**

1. **SARIF** (`analysis/reporters/sarif.py`)
   - Already SARIF 2.1.0 compliant
   - GitHub Actions integration ready
   - Extensible rule system
   - Supports custom severity levels

2. **Markdown** (`analyze --format markdown`)
   - Generates two files: analysis report + fixes report
   - Human-readable
   - Git-friendly

3. **JSON** (`analyze --json` or `--format json`)
   - Machine-readable
   - Supports `--include-context` for LLM consumption
   - Enhanced export with thresholds

**Extension Strategy:**
Add review-specific SARIF rules:
```python
# In sarif.py, extend _get_smell_description()
descriptions = {
    # Existing smells
    "Long Method": "...",
    "Deep Nesting": "...",

    # New review findings
    "Security: Hardcoded Secret": "...",
    "Architecture: Circular Dependency": "...",
    "Performance: N+1 Query": "...",
}
```

---

## 4. Gap Analysis

### 4.1 What Already Exists

| Feature | Status | Location |
|---------|--------|----------|
| Code complexity analysis | ✅ Implemented | `analyze.py` (lines 412-820) |
| Code smell detection | ✅ Implemented | `smells.py`, `analyze.py` (lines 632-644) |
| SARIF output | ✅ Implemented | `sarif.py` (378 lines) |
| Markdown output | ✅ Implemented | `markdown.py` |
| JSON output | ✅ Implemented | `analyze.py` (lines 682-724) |
| Baseline comparison | ✅ Implemented | `analyze.py` (lines 766-793) |
| Changed-only analysis | ✅ Implemented | `analyze.py` (lines 494-550) |
| Dead code detection | ✅ Implemented | `analyze.py` (lines 1284-1418) |
| LLM client with tool calling | ✅ Implemented | `llm_client.py` |
| Chat system | ✅ Implemented | `chat.py` |
| Knowledge graph | ✅ Implemented | `kg.py`, `knowledge_graph.py` |
| Vector search | ✅ Implemented | `search.py` |

### 4.2 What's Missing (from Issue #89 Proposal)

| Feature | Status | Priority |
|---------|--------|----------|
| Specialized review prompts | ❌ Not implemented | High |
| Review type categorization | ❌ Not implemented | High |
| Multi-file cross-analysis | ⚠️ Partial (KG provides this) | Medium |
| Review report templates | ❌ Not implemented | Medium |
| ai-code-review submodule | ❌ Not found | Low (see alternatives) |
| Interactive review refinement | ✅ Chat system supports this | - |
| Context-aware analysis | ✅ Vector search + KG | - |

### 4.3 Genuine New Work

1. **Review Prompt Library**
   - 15+ specialized review types
   - Prompt templates with variable interpolation
   - Context assembly logic

2. **Review Orchestration**
   - Multi-pass analysis (gather context → analyze → refine)
   - Result aggregation across files
   - Confidence scoring for findings

3. **Review-Specific SARIF Rules**
   - Security rule definitions
   - Architecture pattern rules
   - Performance anti-pattern rules

4. **Review Report Generator**
   - Executive summary
   - Prioritized findings
   - Actionable recommendations
   - Links to relevant code

---

## 5. Feasibility Assessment

### 5.1 Technical Feasibility: ✅ FEASIBLE

**Foundation Strengths:**
- ✅ LLM client with tool calling (foundation for prompt orchestration)
- ✅ Vector search for context gathering
- ✅ Knowledge graph for architectural understanding
- ✅ Existing analysis infrastructure (complexity, smells)
- ✅ SARIF output format already implemented
- ✅ Chat system for interactive analysis

**Technical Risks:**
- ⚠️ **Prompt Engineering:** Specialized review prompts require domain expertise
  - Mitigation: Iterative refinement, user feedback

- ⚠️ **Context Window Limits:** LLMs have token limits (e.g., 200K for Claude Opus 4)
  - Mitigation: Chunk analysis, summarization, prioritized context

- ⚠️ **LLM Cost:** Deep reviews with 30 tool iterations (chat.py, line 1470) can be expensive
  - Mitigation: Quick vs. deep review modes, caching, prompt optimization

### 5.2 Integration Feasibility: ⚠️ REQUIRES ALTERNATIVE APPROACH

**Issue #89 Proposal:**
1. Add ai-code-review as git submodule (`vendor/ai-code-review/`)
2. Import 15+ specialized review prompts
3. Create new CLI command: `analyze code-review --type <review_type>`

**Problems:**
1. ❌ **ai-code-review repository not found** in expected locations
2. ⚠️ **Submodule maintenance burden:** Synchronizing external prompt library
3. ⚠️ **Unclear prompt format:** Cannot inspect without repository access
4. ⚠️ **Tight coupling:** Changes in ai-code-review break mcp-vector-search

**Alternative Approach:** (Recommended)
1. ✅ **Embed review prompts directly** in mcp-vector-search
   - Location: `src/mcp_vector_search/prompts/reviews/`
   - Version control: Track prompts in main repo
   - Maintenance: Full control, no external dependencies

2. ✅ **Leverage existing chat system** for natural language reviews
   - Example: `mcp-vector-search chat "security review the auth module"`
   - Benefit: No new CLI syntax, uses existing infrastructure

3. ✅ **Extend analyze command** if structured reviews needed
   - Example: `mcp-vector-search analyze review --type security`
   - Benefit: Consistent with existing `analyze` subcommands

### 5.3 Timeline Feasibility: ⚠️ 4 WEEKS OPTIMISTIC

**Issue #89 Estimate:** 4 weeks

**Breakdown:**

| Phase | Tasks | Realistic Estimate |
|-------|-------|-------------------|
| **Phase 1: Foundation** | Review prompt library (15 types), prompt template engine, basic CLI integration | 2 weeks |
| **Phase 2: Integration** | Vector search context assembly, KG intelligence integration, tool calling orchestration | 2 weeks |
| **Phase 3: Output** | Review-specific SARIF rules, markdown templates, result prioritization | 1 week |
| **Phase 4: Polish** | Error handling, documentation, examples, testing | 1 week |
| **Total** | | **6 weeks** |

**Factors:**
- ✅ **Accelerators:** Existing infrastructure (analyze, chat, LLM client, KG)
- ⚠️ **Decelerators:** Prompt engineering iteration, testing across multiple languages, handling edge cases
- ❌ **Blockers:** ai-code-review repository unavailable (requires alternative approach)

**Revised Estimate:** 6 weeks for full implementation, 4 weeks for Phase 1 MVP.

---

## 6. Recommended Phase 1 Scope (MVP)

### 6.1 Goals

1. **Prove Value:** Demonstrate AI-powered code review with vector search + KG context
2. **Minimal Disruption:** Extend existing chat system, no new CLI commands
3. **Quick Iteration:** Start with 3 review types, expand based on feedback

### 6.2 Implementation Plan

#### Week 1-2: Review Prompt Library

**Deliverable:** 3 specialized review prompts

1. **Security Review**
   - Prompt: Check for hardcoded secrets, insecure APIs, SQL injection, XSS
   - Context: Vector search for "password", "api_key", "secret", "auth", "security"
   - KG enhancement: Trace data flows from user input to storage

2. **Architecture Review**
   - Prompt: Identify circular dependencies, high coupling, unclear boundaries
   - Context: Knowledge graph call chains, import relationships
   - KG enhancement: Visualize feature module structure

3. **Performance Review**
   - Prompt: Find N+1 queries, inefficient loops, blocking I/O
   - Context: Vector search for "query", "loop", "async", "await"
   - KG enhancement: Trace call chains in hot paths

**Location:**
```
src/mcp_vector_search/prompts/reviews/
  ├── security.py
  ├── architecture.py
  └── performance.py
```

**Format:**
```python
# security.py
SECURITY_REVIEW_PROMPT = """You are a security expert reviewing code for vulnerabilities.

Focus Areas:
- Hardcoded secrets (API keys, passwords, tokens)
- Authentication/authorization bypasses
- SQL injection vulnerabilities
- XSS vulnerabilities
- Insecure cryptography
- Sensitive data exposure

Context:
{search_results}

Instructions:
1. Identify security issues with severity (Critical/High/Medium/Low)
2. Provide specific file:line locations
3. Suggest concrete fixes
4. Prioritize by exploitability and impact

Output Format:
- **Issue:** [Brief description]
- **Severity:** [Critical/High/Medium/Low]
- **Location:** [file:line]
- **Evidence:** [Code snippet]
- **Fix:** [Specific remediation]
- **SARIF Rule:** [security-hardcoded-secret]
"""

async def run_security_review(
    project_root: Path,
    search_engine: SemanticSearchEngine,
    llm_client: LLMClient,
) -> list[ReviewFinding]:
    # Gather context
    context = await search_engine.search(
        query="password secret api_key token authentication authorization",
        limit=15,
    )

    # Run LLM analysis
    response = await llm_client.generate_answer(
        query="Perform security review",
        context=format_context(context),
        system_prompt=SECURITY_REVIEW_PROMPT,
    )

    # Parse findings
    return parse_review_findings(response)
```

#### Week 2: Chat Integration

**Deliverable:** Chat system recognizes review intent

**Enhancement:** Extend `_get_tools()` in `chat.py`:

```python
# Add to _get_tools() around line 880
{
    "type": "function",
    "function": {
        "name": "code_review",
        "description": "Perform specialized code review (security, architecture, performance)",
        "parameters": {
            "review_type": {
                "type": "string",
                "enum": ["security", "architecture", "performance"],
                "description": "Type of review to perform",
            },
            "scope": {
                "type": "string",
                "description": "Scope to review (e.g., 'auth module', 'payment system')",
            },
        },
    },
}
```

**Usage:**
```bash
$ mcp-vector-search chat
> Security review the authentication code
[Assistant uses code_review tool with review_type="security", scope="authentication"]

> Check for architectural coupling in the payment system
[Assistant uses code_review tool with review_type="architecture", scope="payment"]
```

#### Week 3-4: SARIF Integration

**Deliverable:** Review findings exportable to SARIF

**Enhancement:** Extend `sarif.py`:

```python
# Add review-specific rule descriptions
REVIEW_RULE_DESCRIPTIONS = {
    "security-hardcoded-secret": {
        "name": "Hardcoded Secret",
        "description": "Secret credential hardcoded in source code",
        "severity": "error",
        "help": "Move secrets to environment variables or secret management service",
    },
    "architecture-circular-dependency": {
        "name": "Circular Dependency",
        "description": "Circular import or dependency cycle detected",
        "severity": "warning",
        "help": "Refactor to extract shared logic into separate module",
    },
    "performance-n-plus-one-query": {
        "name": "N+1 Query Problem",
        "description": "Database queries in loop causing performance issues",
        "severity": "warning",
        "help": "Use batch loading or eager loading to reduce queries",
    },
}
```

**Command:**
```bash
mcp-vector-search chat "security review" --output security-review.sarif
```

### 6.3 Success Criteria

1. ✅ **3 review types working:** Security, Architecture, Performance
2. ✅ **Chat integration:** Natural language review requests
3. ✅ **SARIF output:** GitHub-compatible report format
4. ✅ **KG enhancement:** Reviews use knowledge graph context
5. ✅ **Vector search:** Contextual code gathering for reviews
6. ✅ **Documentation:** Examples and usage guide

### 6.4 Out of Scope (Phase 1)

- ❌ Separate CLI command (use chat system)
- ❌ All 15 review types (start with 3)
- ❌ Multi-pass refinement (single-pass MVP)
- ❌ Custom report templates (use SARIF + Markdown)
- ❌ Review automation/CI integration (manual execution first)

---

## 7. Alternative: Extend Existing Analyze Command

### 7.1 CLI Design

```bash
# New subcommand under analyze
mcp-vector-search analyze review --type security
mcp-vector-search analyze review --type architecture --path src/auth
mcp-vector-search analyze review --type performance --changed-only
```

### 7.2 Implementation Strategy

**Location:** Extend `src/mcp_vector_search/cli/commands/analyze.py`

**Add new subcommand:**

```python
@analyze_app.command(name="review")
def code_review(
    review_type: str = typer.Argument(
        ...,
        help="Review type: security, architecture, performance",
    ),
    project_root: Path | None = typer.Option(
        None,
        "--project-root",
        "-p",
        help="Project root directory",
    ),
    path: Path | None = typer.Option(
        None,
        "--path",
        help="Specific path to review",
    ),
    changed_only: bool = typer.Option(
        False,
        "--changed-only",
        help="Review only uncommitted changes",
    ),
    output_format: str = typer.Option(
        "console",
        "--format",
        help="Output format: console, json, sarif, markdown",
    ),
    output_file: Path | None = typer.Option(
        None,
        "--output",
        help="Output file path",
    ),
) -> None:
    """Perform AI-powered code review with vector search + KG context.

    Examples:
        mcp-vector-search analyze review security
        mcp-vector-search analyze review architecture --path src/core
        mcp-vector-search analyze review performance --changed-only --format sarif
    """
    asyncio.run(
        run_code_review(
            review_type=review_type,
            project_root=project_root or Path.cwd(),
            path_filter=path,
            changed_only=changed_only,
            output_format=output_format,
            output_file=output_file,
        )
    )
```

### 7.3 Advantages

- ✅ Consistent with existing `analyze` commands
- ✅ Reuses filters: `--changed-only`, `--path`, `--baseline`
- ✅ Reuses output formats: `--format sarif|json|markdown`
- ✅ Familiar UX for existing users

### 7.4 Disadvantages

- ⚠️ Less flexible than chat (requires `--type` flag)
- ⚠️ No interactive refinement
- ⚠️ More rigid command structure

---

## 8. Comparison: Chat vs. CLI Extension

| Aspect | Chat System | CLI Extension |
|--------|-------------|---------------|
| **User Experience** | Natural language, conversational | Structured flags, explicit |
| **Flexibility** | High (free-form queries) | Medium (predefined types) |
| **Iterative Refinement** | ✅ Easy (ask follow-ups) | ❌ Requires re-running command |
| **Automation** | ⚠️ Harder (parsing chat output) | ✅ Easy (structured output) |
| **Learning Curve** | Low (just ask questions) | Medium (learn flags) |
| **CI/CD Integration** | ⚠️ Complex | ✅ Straightforward |
| **Explainability** | ✅ High (conversational) | ⚠️ Lower (just findings) |
| **Implementation Effort** | Low (extend existing) | Medium (new command + orchestration) |

**Recommendation:** Start with **Chat Integration** (Phase 1), add **CLI Extension** in Phase 2 for automation use cases.

---

## 9. Key Technical Risks

### 9.1 Prompt Engineering Quality

**Risk:** Review prompts produce low-quality, noisy findings

**Mitigation:**
- Iterative refinement with user feedback
- Start with well-defined review types (security, performance)
- Use few-shot examples in prompts
- Implement confidence scoring

**Example Prompt Improvement:**

```python
# BAD: Vague, generates false positives
"Find security issues in this code"

# GOOD: Specific, with examples and output format
"""Find hardcoded secrets using these patterns:
- API keys: /api[_-]?key\s*=\s*['"][^'"]+['"]/i
- Passwords: /password\s*=\s*['"][^'"]+['"]/i
- Tokens: /token\s*=\s*['"][^'"]+['"]/i

For each finding:
1. Extract exact code snippet
2. Identify file and line number
3. Explain why it's a security risk
4. Suggest specific fix (e.g., "Move to .env file")

Format:
**Issue:** Hardcoded API Key
**Severity:** Critical
**Location:** auth.py:42
**Evidence:** `API_KEY = "sk-live-abc123def456"`
**Fix:** Store in environment variable: `API_KEY = os.getenv("API_KEY")`
"""
```

### 9.2 Context Window Limitations

**Risk:** Large codebases exceed LLM token limits

**Mitigation:**
- Chunked analysis (review module-by-module)
- Prioritized context (most relevant files first)
- Summarization of low-priority code
- Progressive refinement (broad → narrow)

**Example Strategy:**

```python
async def review_large_codebase(project_root: Path, review_type: str):
    # Step 1: Broad search to identify hotspots
    hotspots = await search_engine.search(
        query=get_review_keywords(review_type),
        limit=50,
    )

    # Step 2: Cluster by file/module
    clusters = cluster_by_module(hotspots)

    # Step 3: Review each cluster independently
    findings = []
    for cluster in clusters:
        cluster_findings = await review_cluster(cluster, review_type)
        findings.extend(cluster_findings)

    # Step 4: Aggregate and deduplicate
    return deduplicate_findings(findings)
```

### 9.3 LLM Cost and Latency

**Risk:** Deep reviews with 30 tool calls expensive and slow

**Mitigation:**
- Quick mode: Single-pass review (30 seconds, $0.10)
- Deep mode: Multi-pass with refinement (2 minutes, $0.50)
- Caching: Reuse context across reviews
- Streaming: Show progress as findings discovered

**Cost Model:**

| Review Mode | Token Usage | Latency | Cost (Claude Opus 4) |
|-------------|-------------|---------|----------------------|
| Quick (3 tool calls) | ~50K tokens | 30 seconds | $0.10 |
| Standard (10 calls) | ~150K tokens | 1 minute | $0.30 |
| Deep (30 calls) | ~400K tokens | 2-3 minutes | $0.80 |

### 9.4 False Positive Rate

**Risk:** Reviews flag too many non-issues, users lose trust

**Mitigation:**
- Confidence scoring (High/Medium/Low)
- Severity filtering (Critical/High only)
- Contextual validation (KG confirms suspicion)
- Incremental rollout (start with high-confidence only)

**Example Confidence Scoring:**

```python
class ReviewFinding:
    issue: str
    severity: str  # Critical, High, Medium, Low
    confidence: str  # High, Medium, Low
    location: str
    evidence: str
    fix: str

    def should_report(self, threshold: str = "Medium") -> bool:
        """Filter findings by confidence threshold."""
        levels = {"High": 3, "Medium": 2, "Low": 1}
        return levels[self.confidence] >= levels[threshold]
```

---

## 10. Implementation Roadmap

### Phase 1: MVP (4 weeks)

**Goal:** Prove value with 3 review types via chat

**Week 1-2: Prompt Library**
- [ ] Create `src/mcp_vector_search/prompts/reviews/` directory
- [ ] Implement security review prompt
- [ ] Implement architecture review prompt
- [ ] Implement performance review prompt
- [ ] Add review finding parser

**Week 3: Chat Integration**
- [ ] Add `code_review` tool to chat system
- [ ] Implement review orchestration logic
- [ ] Add KG context enhancement
- [ ] Test with sample projects

**Week 4: Output Integration**
- [ ] Extend SARIF reporter with review rules
- [ ] Add markdown review report template
- [ ] Implement confidence scoring
- [ ] Write documentation and examples

**Deliverables:**
- ✅ 3 working review types
- ✅ Chat integration: `mcp-vector-search chat "security review"`
- ✅ SARIF output: `--output security-review.sarif`
- ✅ Documentation with examples

### Phase 2: CLI Extension (2 weeks)

**Goal:** Add structured CLI for automation

**Week 5-6: Analyze Subcommand**
- [ ] Add `analyze review` subcommand
- [ ] Implement filter integration (--changed-only, --path)
- [ ] Add batch review mode (review multiple types)
- [ ] CI/CD examples and documentation

**Deliverables:**
- ✅ `mcp-vector-search analyze review security`
- ✅ Filter support: `--changed-only`, `--baseline main`
- ✅ Batch mode: `--type security,architecture,performance`
- ✅ GitHub Actions workflow example

### Phase 3: Advanced Features (4 weeks)

**Goal:** Multi-pass refinement, additional review types

**Week 7-8: Multi-Pass Refinement**
- [ ] Implement iterative review (find → verify → refine)
- [ ] Add cross-file analysis (trace dependencies)
- [ ] Implement review summarization
- [ ] Add priority ranking

**Week 9-10: Additional Review Types**
- [ ] Accessibility review
- [ ] Error handling review
- [ ] Documentation review
- [ ] Test coverage review

**Deliverables:**
- ✅ 7 total review types
- ✅ Multi-pass refinement mode
- ✅ Cross-file dependency tracing
- ✅ Review summary dashboard

---

## 11. Recommendations

### 11.1 Phase 1 (MVP) - Immediate Action

**Approach:** Embed review prompts directly, extend chat system

**Rationale:**
1. ✅ **No external dependencies** - ai-code-review repo not found
2. ✅ **Fastest time to value** - leverages existing chat system
3. ✅ **Lowest risk** - no new CLI syntax or breaking changes
4. ✅ **User-friendly** - natural language interface

**Action Items:**
1. Create `src/mcp_vector_search/prompts/reviews/` directory
2. Write 3 specialized review prompts (security, architecture, performance)
3. Add `code_review` tool to chat system
4. Extend SARIF reporter with review-specific rules
5. Document usage and examples

**Timeline:** 4 weeks (realistic), 6 weeks (conservative)

### 11.2 Phase 2 - CLI Extension

**Approach:** Add `analyze review` subcommand for automation

**Rationale:**
1. ✅ **CI/CD integration** - structured output for pipelines
2. ✅ **Batch processing** - review multiple types at once
3. ✅ **Familiar UX** - consistent with existing `analyze` commands

**Action Items:**
1. Add `analyze review` subcommand
2. Integrate with existing filters (--changed-only, --baseline)
3. Create GitHub Actions workflow example
4. Document automation use cases

**Timeline:** 2 weeks

### 11.3 What NOT to Do

❌ **Do NOT add ai-code-review as submodule**
- Repository not found
- External dependency maintenance burden
- Unclear prompt format
- Tight coupling risk

❌ **Do NOT create new top-level CLI command**
- Ambiguous: `analyze code-review` vs separate command?
- Doesn't leverage existing infrastructure
- Inconsistent with project patterns

❌ **Do NOT implement all 15 review types in Phase 1**
- Diminishing returns (80/20 rule)
- Start with highest-value: security, architecture, performance
- Add more based on user feedback

---

## 12. Conclusion

### 12.1 Feasibility Verdict: ✅ FEASIBLE (with modifications)

**Key Findings:**
1. ✅ mcp-vector-search has strong foundation (analyze, chat, LLM client, KG)
2. ✅ No blocking technical dependencies
3. ⚠️ Original Issue #89 approach (ai-code-review submodule) not viable
4. ✅ Alternative approach (embedded prompts + chat extension) is superior

### 12.2 Recommended Path Forward

**Phase 1 MVP (4 weeks):**
- Embed 3 review prompts directly (security, architecture, performance)
- Extend chat system with `code_review` tool
- Leverage vector search + KG for context
- SARIF output for findings

**Phase 2 CLI (2 weeks):**
- Add `analyze review` subcommand
- CI/CD integration examples
- Batch review mode

**Phase 3 Advanced (4 weeks):**
- Multi-pass refinement
- 4 additional review types
- Cross-file dependency tracing

**Total Timeline:** 10 weeks (full implementation)
**MVP Timeline:** 4 weeks (prove value)

### 12.3 Biggest Technical Risks

1. **Prompt Engineering Quality** - Mitigation: Iterative refinement, few-shot examples
2. **Context Window Limits** - Mitigation: Chunked analysis, prioritized context
3. **False Positive Rate** - Mitigation: Confidence scoring, severity filtering
4. **LLM Cost** - Mitigation: Quick vs deep modes, caching

### 12.4 Next Steps

1. ✅ **Get user feedback** on Phase 1 scope (3 review types vs more)
2. ✅ **Prototype security review prompt** (1-2 days)
3. ✅ **Test with real codebase** (validate quality and performance)
4. ✅ **Iterate on prompt** based on findings
5. ✅ **Implement chat integration** (Week 3 of Phase 1)

---

## Appendix A: GitHub Issue #89 Original Proposal

**Title:** "feat: Comprehensive AI-powered code analysis with specialized review prompts and vector-enhanced context"

**Key Elements:**
1. Add ai-code-review as submodule
2. Import 15+ specialized review type prompts
3. New CLI command: `mcp-vector-search analyze code-review --type <review_type>`
4. Vector search provides relevant code context
5. Knowledge graph provides architectural understanding
6. Enhanced chat: `mcp-vector-search chat "do a security review of the auth module"`

**Analysis:**
- ✅ Good idea: Specialized review prompts
- ✅ Good idea: Vector search + KG context
- ✅ Good idea: Enhanced chat for reviews
- ⚠️ Needs modification: Submodule approach (repo not found)
- ⚠️ Needs modification: CLI command design (use existing `analyze` structure)

---

## Appendix B: Project Structure

```
mcp-vector-search/
├── src/mcp_vector_search/
│   ├── cli/commands/
│   │   ├── analyze.py          # Complexity, dead-code analysis
│   │   ├── chat.py              # Interactive LLM-powered chat
│   │   └── kg.py                # Knowledge graph operations
│   ├── core/
│   │   ├── llm_client.py        # OpenAI/OpenRouter/Bedrock client
│   │   ├── knowledge_graph.py   # KuzuDB graph database
│   │   └── search.py            # Semantic vector search
│   ├── analysis/
│   │   ├── collectors/          # Complexity collectors
│   │   │   └── smells.py        # Code smell detection
│   │   └── reporters/
│   │       ├── sarif.py         # SARIF 2.1.0 output
│   │       ├── markdown.py      # Markdown reports
│   │       └── console.py       # Console output
│   └── prompts/                 # ← NEW: Review prompts
│       └── reviews/             # ← NEW: Specialized reviews
│           ├── __init__.py
│           ├── security.py
│           ├── architecture.py
│           └── performance.py
└── docs/
    └── research/                # Research documents
        └── ai-code-review-integration-feasibility-2026-02-22.md  # This document
```

---

**Document Version:** 1.0
**Last Updated:** 2026-02-22
**Author:** Research Agent (mcp-vector-search)
**Status:** Complete - Ready for Review
