Metadata-Version: 2.4
Name: stirlingpdf-agent
Version: 1.0.1
Summary: Agent package for communicating with Stirling PDF via REST APIs.
Author-email: Audel Rouhi <knucklessg1@gmail.com>
License: MIT
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Environment :: Console
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Requires-Python: <3.15,>=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: agent-utilities[mcp]>=1.0.0
Provides-Extra: mcp
Requires-Dist: agent-utilities[mcp]>=1.0.0; extra == "mcp"
Provides-Extra: agent
Requires-Dist: agent-utilities[agent,logfire]>=1.0.0; extra == "agent"
Provides-Extra: all
Requires-Dist: agent-utilities[agent,logfire,mcp]>=1.0.0; extra == "all"
Provides-Extra: test
Requires-Dist: pytest-xdist>=3.6.0; extra == "test"
Requires-Dist: pytest; extra == "test"
Requires-Dist: pytest-asyncio; extra == "test"
Requires-Dist: pytest-cov; extra == "test"
Dynamic: license-file

# Stirling PDF Agent
## CLI or API | MCP | Agent

![PyPI - Version](https://img.shields.io/pypi/v/stirlingpdf-agent)
![MCP Server](https://badge.mcpx.dev?type=server 'MCP Server')
![PyPI - Downloads](https://img.shields.io/pypi/dd/stirlingpdf-agent)
![GitHub Repo stars](https://img.shields.io/github/stars/Knuckles-Team/stirlingpdf-agent)
![GitHub forks](https://img.shields.io/github/forks/Knuckles-Team/stirlingpdf-agent)
![GitHub contributors](https://img.shields.io/github/contributors/Knuckles-Team/stirlingpdf-agent)
![PyPI - License](https://img.shields.io/pypi/l/stirlingpdf-agent)
![GitHub last commit (by committer)](https://img.shields.io/github/last-commit/Knuckles-Team/stirlingpdf-agent)
![GitHub pull requests](https://img.shields.io/github/issues-pr/Knuckles-Team/stirlingpdf-agent)
![GitHub closed pull requests](https://img.shields.io/github/issues-pr-closed/Knuckles-Team/stirlingpdf-agent)
![GitHub issues](https://img.shields.io/github/issues/Knuckles-Team/stirlingpdf-agent)
![GitHub top language](https://img.shields.io/github/languages/top/Knuckles-Team/stirlingpdf-agent)
![GitHub repo size](https://img.shields.io/github/repo-size/Knuckles-Team/stirlingpdf-agent)

*Version: 1.0.1*

> **Documentation** — Installation, deployment, usage across the MCP, API, and CLI
> interfaces, and guidance for provisioning the Stirling PDF service are maintained in
> the [official documentation](https://knuckles-team.github.io/stirlingpdf-agent/).

---

## 📚 Table of Contents
- [Overview](#overview)
- [Key Features](#key-features)
- [Installation](#installation)
- [Quick Start & Usage Examples](#quick-start--usage-examples)
- [MCP Server Mode](#mcp-server-mode)
  - [Available MCP Tools](#available-mcp-tools)
  - [MCP Configuration Examples](#mcp-configuration-examples)
- [Agent Mode](#agent-mode)
  - [Running the Agent CLI](#running-the-agent-cli)
  - [Docker Compose Orchestration](#docker-compose-orchestration)
- [Environment Variables Reference](#environment-variables-reference)
- [Security & Governance](#security--governance)
- [Developer & Contribute Guidelines](#contribute)
- [Documentation](#documentation)

---

## Overview

**Stirling PDF Agent** is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Stirling PDF via REST APIs. It provides seamless capability to manipulate, edit, and overlay PDFs (e.g. adding watermarks) programmatically or using large language models.

---

## Key Features

- **Consolidated Action-Routed MCP Tools:** Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping methods into optimized, togglable tool modules.
- **Enterprise-Grade Security:** Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking.
- **Integrated Graph Agent:** Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI).
- **Native Telemetry & Tracing:** Out-of-the-box OpenTelemetry exports and native Langfuse tracing.

---

## Installation

Pick the extra that matches what you want to run:

| Extra | Installs | Use when |
|-------|----------|----------|
| `stirlingpdf-agent[mcp]` | Slim MCP server only (`agent-utilities[mcp]` — FastMCP/FastAPI) | You only run the **MCP server** (smallest install / image) |
| `stirlingpdf-agent[agent]` | Full agent runtime (`agent-utilities[agent,logfire]` — Pydantic AI + the epistemic-graph engine) | You run the **integrated agent** |
| `stirlingpdf-agent[all]` | Everything (`mcp` + `agent` + `logfire`) | Development / both surfaces |

```bash
# MCP server only (recommended for tool hosting — slim deps)
uv pip install "stirlingpdf-agent[mcp]"

# Full agent runtime (Pydantic AI + epistemic-graph engine)
uv pip install "stirlingpdf-agent[agent]"

# Everything (development)
uv pip install "stirlingpdf-agent[all]"      # or: python -m pip install "stirlingpdf-agent[all]"
```

### Container images (`:mcp` vs `:agent`)

One multi-stage `docker/Dockerfile` builds two right-sized images, selected by `--target`:

| Image tag | Build target | Contents | Entrypoint |
|-----------|--------------|----------|------------|
| `knucklessg1/stirlingpdf-agent:mcp` | `--target mcp` | `stirlingpdf-agent[mcp]` — **slim**, no engine/`pydantic-ai`/`dspy`/`llama-index`/`tree-sitter` | `stirlingpdf-mcp` |
| `knucklessg1/stirlingpdf-agent:latest` | `--target agent` (default) | `stirlingpdf-agent[agent]` — **full** agent runtime + epistemic-graph engine | `stirlingpdf-agent` |

```bash
docker build --target mcp   -t knucklessg1/stirlingpdf-agent:mcp    docker/   # slim MCP server
docker build --target agent -t knucklessg1/stirlingpdf-agent:latest docker/   # full agent
```

`docker/mcp.compose.yml` runs the slim `:mcp` server; `docker/agent.compose.yml` runs the
agent (`:latest`) with a co-located `:mcp` sidecar.

### Knowledge-graph database (`epistemic-graph`)

The **full agent** (`[agent]` / `:latest`) embeds the **epistemic-graph** engine (pulled in
transitively via `agent-utilities[agent]`). For production — or to share one knowledge graph
across multiple agents — run **epistemic-graph as its own database container** and point the
agent at it instead of embedding it. Deployment recipes (single-node + Raft HA), connection
config, and the full database architecture (with diagrams) are documented in the
[epistemic-graph deployment guide](https://knuckles-team.github.io/epistemic-graph/deployment/).
The slim `[mcp]` server does **not** require the database.

---

## Quick Start & Usage Examples

Using the underlying Stirling PDF Client wrapper directly in Python:

```python
from stirlingpdf_agent.api_client import StirlingPdfApi

# Initialize the Stirling PDF client
client = StirlingPdfApi(
    base_url="http://localhost:8080",
    token="your-stirling-pdf-api-key",
    verify=True
)

# Example action: Add a watermark to an existing PDF
response = client.add_watermark(
    filepath="input.pdf",
    watermarkText="CONFIDENTIAL",
    percentOfPage=30,
    opacity=0.5,
    rotation=45
)

# Save output PDF bytes
with open("watermarked_output.pdf", "wb") as f:
    f.write(response.data)
```

---

## MCP Server Mode

This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

### Available MCP Tools

The table below is auto-generated from the MCP server — do not edit by hand.

<!-- MCP-TOOLS-TABLE:START -->

#### Condensed action-routed tools (default — `MCP_TOOL_MODE=condensed`)

| MCP Tool | Toggle Env Var | Description |
|----------|----------------|-------------|
| `pdf_action` | `PDFTOOL` | Execute any Stirling PDF API action dynamically. |

#### Verbose 1:1 API-mapped tools (`MCP_TOOL_MODE=verbose` or `both`)

<details>
<summary>1 per-operation tools — one per public API method (click to expand)</summary>

| MCP Tool | Toggle Env Var | Description |
|----------|----------------|-------------|
| `stirlingpdf_add_watermark` | `WATERMARK_CLIENTTOOL` | Add a watermark to a PDF file. |

</details>

_1 action-routed tool(s) (default) · 1 verbose 1:1 tool(s). Each is enabled unless its `<DOMAIN>TOOL` toggle is set false; `MCP_TOOL_MODE` selects the surface (`condensed` default · `verbose` 1:1 · `both`). Auto-generated — do not edit._
<!-- MCP-TOOLS-TABLE:END -->

---

### Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

- **CLI Arguments:** Pass `--tools` or `--toolsets` (or their disabled counterparts `--disabled-tools` and `--disabled-toolsets`) during startup.
- **Environment Variables:** Define standard environment variables:
  - `MCP_ENABLED_TOOLS` / `MCP_DISABLED_TOOLS`
  - `MCP_ENABLED_TAGS` / `MCP_DISABLED_TAGS`
- **HTTP SSE Request Headers:** Pass custom headers during transport initialization:
  - `x-mcp-enabled-tools` / `x-mcp-disabled-tools`
  - `x-mcp-enabled-tags` / `x-mcp-disabled-tags`
- **HTTP SSE Request Query Parameters:** Append query parameters directly to your transport connection URL:
  - `?tools=tool1,tool2`
  - `?tags=tag1`

When query strings or parameters are supplied, an LLM-free **Knowledge Graph resolution layer** (using `DynamicToolOrchestrator`) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

---

### MCP Configuration Examples

<!-- MCP-CONFIG-EXAMPLES:START -->

> **Install the slim `[mcp]` extra.** All examples install `stirlingpdf-agent[mcp]` — the
> MCP-server extra that pulls only the FastMCP / FastAPI tooling (`agent-utilities[mcp]`).
> It deliberately **excludes** the heavy agent runtime (`pydantic-ai`, the epistemic-graph
> engine, `dspy`, `llama-index`), so `uvx` / container installs are far smaller. Use the
> full `[agent]` extra only when you need the integrated Pydantic AI agent.

#### stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

```json
{
  "mcpServers": {
    "stirlingpdf-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "stirlingpdf-agent[mcp]",
        "stirlingpdf-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "condensed",
        "PDFTOOL": "True",
        "STIRLINGPDF_AGENT_VERIFY": "True",
        "STIRLINGPDF_API_KEY": "",
        "STIRLINGPDF_TOKEN": "",
        "STIRLINGPDF_URL": "http://localhost:8080"
      }
    }
  }
}
```

#### Streamable-HTTP Transport (networked / production)

```json
{
  "mcpServers": {
    "stirlingpdf-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "stirlingpdf-agent[mcp]",
        "stirlingpdf-mcp",
        "--transport",
        "streamable-http",
        "--port",
        "8000"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "MCP_TOOL_MODE": "condensed",
        "PDFTOOL": "True",
        "STIRLINGPDF_AGENT_VERIFY": "True",
        "STIRLINGPDF_API_KEY": "",
        "STIRLINGPDF_TOKEN": "",
        "STIRLINGPDF_URL": "http://localhost:8080"
      }
    }
  }
}
```

Alternatively, connect to a pre-deployed Streamable-HTTP instance by `url`:

```json
{
  "mcpServers": {
    "stirlingpdf-mcp": {
      "url": "http://localhost:8000/stirlingpdf-mcp/mcp"
    }
  }
}
```

Deploying the Streamable-HTTP server via Docker:

```bash
docker run -d \
  --name stirlingpdf-mcp-mcp \
  -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e HOST=0.0.0.0 \
  -e PORT=8000 \
  -e MCP_TOOL_MODE=condensed \
  -e PDFTOOL=True \
  -e STIRLINGPDF_AGENT_VERIFY=True \
  -e STIRLINGPDF_API_KEY="" \
  -e STIRLINGPDF_TOKEN="" \
  -e STIRLINGPDF_URL=http://localhost:8080 \
  knucklessg1/stirlingpdf-agent:mcp
```

_Auto-generated from the code-read env surface (`MCP_TOOL_MODE` + package vars) — do not edit._
<!-- MCP-CONFIG-EXAMPLES:END -->

<!-- BEGIN GENERATED: additional-deployment-options -->
### Additional Deployment Options

`stirlingpdf-agent` can also run as a **local container** (Docker / Podman / `uv`) or be
consumed from a **remote deployment**. The
[Deployment guide](https://knuckles-team.github.io/stirlingpdf-agent/deployment/) has full, copy-paste
`mcp_config.json` for all four transports — **stdio**, **streamable-http**,
**local container / uv**, and **remote URL**:

- **Local container / uv** — launch the server from `mcp_config.json` via `uvx`,
  `docker run`, or `podman run`, or point at a local streamable-http container by `url`.
- **Remote URL** — connect to a server deployed behind Caddy at
  `http://stirlingpdf-mcp.arpa/mcp` using the `"url"` key.
<!-- END GENERATED: additional-deployment-options -->

## Agent Mode

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the **Agent Control Protocol (ACP)** and interacts seamlessly with the **Agent Web UI (AG-UI)** and Terminal interface.

### Running the Agent CLI
To start the interactive command-line agent:

```bash
# Set credentials
export STIRLINGPDF_URL="http://localhost:8080"
export STIRLINGPDF_API_KEY="your-api-key"

# Run the agent server
stirlingpdf-agent --provider openai --model-id gpt-4o
```

---

### Docker Compose Orchestration

```yaml
version: '3.8'

services:
  stirlingpdf-agent-mcp:
    image: knucklessg1/stirlingpdf-agent:latest
    container_name: stirlingpdf-agent-mcp
    hostname: stirlingpdf-agent-mcp
    restart: always
    env_file:
      - .env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=streamable-http
    ports:
      - "8000:8000"

  stirlingpdf-agent-agent:
    image: knucklessg1/stirlingpdf-agent:latest
    container_name: stirlingpdf-agent-agent
    hostname: stirlingpdf-agent-agent
    restart: always
    depends_on:
      - stirlingpdf-agent-mcp
    env_file:
      - .env
    command: [ "stirlingpdf-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9004
      - MCP_URL=http://stirlingpdf-agent-mcp:8000/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9004:9004"
```

---

## Environment Variables

<!-- ENV-VARS-TABLE:START -->

#### Package environment variables

| Variable | Example | Description |
|----------|---------|-------------|
| `HOST` | `0.0.0.0` |  |
| `PORT` | `8000` |  |
| `TRANSPORT` | `stdio` | options: stdio, streamable-http, sse |
| `ENABLE_OTEL` | `True` |  |
| `OTEL_EXPORTER_OTLP_ENDPOINT` | `http://localhost:8080/api/public/otel` |  |
| `OTEL_EXPORTER_OTLP_PUBLIC_KEY` | `pk-...` |  |
| `OTEL_EXPORTER_OTLP_SECRET_KEY` | `sk-...` |  |
| `OTEL_EXPORTER_OTLP_PROTOCOL` | `http/protobuf` |  |
| `EUNOMIA_TYPE` | `none` | options: none, embedded, remote |
| `EUNOMIA_POLICY_FILE` | `mcp_policies.json` |  |
| `EUNOMIA_REMOTE_URL` | `http://eunomia-server:8000` |  |
| `PDFTOOL` | `True` |  |
| `STIRLINGPDF_URL` | `http://localhost:8080` |  |
| `STIRLINGPDF_API_KEY` | — |  |
| `STIRLINGPDF_TOKEN` | — | alternate to STIRLINGPDF_API_KEY (bearer token) |
| `STIRLINGPDF_AGENT_VERIFY` | `True` |  |
| `STIRLINGPDF_SSL_VERIFY` | `True` | alternate to STIRLINGPDF_AGENT_VERIFY (TLS cert verification) |

#### Inherited agent-utilities variables (apply to every connector)

| Variable | Example | Description |
|----------|---------|-------------|
| `MCP_TOOL_MODE` | `condensed` | Tool surface: `condensed` | `verbose` | `both` |
| `MCP_ENABLED_TOOLS` | — | Comma-separated tool allow-list |
| `MCP_DISABLED_TOOLS` | — | Comma-separated tool deny-list |
| `MCP_ENABLED_TAGS` | — | Comma-separated tag allow-list |
| `MCP_DISABLED_TAGS` | — | Comma-separated tag deny-list |
| `MCP_CLIENT_AUTH` | — | Outbound MCP auth (`oidc-client-credentials` for fleet calls) |
| `OIDC_CLIENT_ID` | — | OIDC client id (service-account auth) |
| `OIDC_CLIENT_SECRET` | — | OIDC client secret (service-account auth) |
| `DEBUG` | `False` | Verbose logging |
| `PYTHONUNBUFFERED` | `1` | Unbuffered stdout (recommended in containers) |
| `MCP_URL` | `http://localhost:8000/mcp` | URL of the MCP server the agent connects to |
| `PROVIDER` | `openai` | LLM provider for the agent |
| `MODEL_ID` | `gpt-4o` | Model id for the agent |
| `ENABLE_WEB_UI` | `True` | Serve the AG-UI web interface |

_17 package + 14 inherited variable(s). Auto-generated from `.env.example` + the shared agent-utilities set — do not edit._
<!-- ENV-VARS-TABLE:END -->
 Reference

Stirling PDF Agent utilizes both package-specific environment configurations and standard security settings inherited from the `agent-utilities` system core.

### Stirling PDF Agent Configs
- **`PDFTOOL`** (bool, default: `True`): Toggles the dynamic PDF action tool registration.
- **`STIRLINGPDF_URL`** (str, default: `http://localhost:8080`): The base endpoint of the external Stirling PDF API service.
- **`STIRLINGPDF_API_KEY`** (str): API connection token/secret used to authenticate REST requests.
- **`STIRLINGPDF_AGENT_VERIFY`** (bool, default: `True`): Toggles SSL certificate verification during REST requests.

### Inherited agent-utilities Configs
- **`TRANSPORT`** (str, default: `stdio`): Server transport type. Options: `stdio`, `sse`, `streamable-http`.
- **`HOST`** (str, default: `0.0.0.0`): Network host interface to bind the HTTP server.
- **`PORT`** (int, default: `8000`): Port to listen on.
- **`ENABLE_OTEL`** (bool, default: `False`): Enables OpenTelemetry tracing integration.
- **`ALLOWED_CLIENT_REDIRECT_URIS`** (str): Comma-separated list of approved redirect URLs for authentication loops.
- **`AUTH_TYPE`** (str): Server authentication mode configurations.
- **`EUNOMIA_TYPE`** (str, default: `none`): Policy configuration enforcement. Options: `none`, `embedded`, `remote`.
- **`EUNOMIA_POLICY_FILE`** (str): Path to local JSON configuration policy maps.
- **`EUNOMIA_REMOTE_URL`** (str): Target URL for remote auth policy coordination.
- **`OAUTH_BASE_URL`** (str): Base OAuth service endpoint.
- **`OAUTH_UPSTREAM_AUTH_ENDPOINT`** (str): Upstream OAuth service authorization endpoint.
- **`OAUTH_UPSTREAM_CLIENT_ID`** (str): Client application identity ID.
- **`OAUTH_UPSTREAM_CLIENT_SECRET`** (str): Client secret credential token.
- **`OAUTH_UPSTREAM_TOKEN_ENDPOINT`** (str): Remote OAuth token resolution endpoint.

---

## Security & Governance

Built directly upon the enterprise-ready [`agent-utilities`](https://github.com/Knuckles-Team/agent-utilities) core, standard security parameters are fully supported:

- **Eunomia Policies:** Fine-grained, policy-driven tool authorization. Supports `none`, local `embedded` (`mcp_policies.json`), or centralized `remote` modes.
- **OIDC Token Delegation:** Compliant with RFC 8693 token exchange for flowing authenticating user credentials from Web UI / ACP → Agent → MCP.
- **Scoped Credentials:** Execution context runs restricted to the specific caller identity.

| Feature Guard | Functionality | Status |
|---------------|---------------|--------|
| **Tool Guard** | Sensitivity inspection with human-in-the-loop validation | Enabled by default |
| **Prompt Injection Defense** | Input scanning, repetition monitoring, and recursive loop blocks | Enabled by default |
| **Context Safety Guard** | Stuck-loop detectors and contextual overflow preemptive alerts | Enabled by default |

---

## Contribute

Contributions are welcome! Please ensure code quality by executing local checks before submitting pull requests:
- Format code using `ruff format .`
- Lint code using `ruff check .`
- Validate type-safety with `mypy .`
- Execute test suites using `pytest`

---

## Documentation

The complete documentation is published as the
[official documentation site](https://knuckles-team.github.io/stirlingpdf-agent/) and is
the recommended reference for installation, deployment, and day-to-day operation.

| Page | Contents |
|---|---|
| [Installation](https://knuckles-team.github.io/stirlingpdf-agent/installation/) | pip, source, extras, prebuilt Docker image |
| [Deployment](https://knuckles-team.github.io/stirlingpdf-agent/deployment/) | run the MCP and agent servers, Compose, Caddy + Technitium, env config |
| [Usage](https://knuckles-team.github.io/stirlingpdf-agent/usage/) | the MCP tools, the `StirlingPdfApi` client, the CLI |
| [Backing Platform](https://knuckles-team.github.io/stirlingpdf-agent/platform/) | deploy Stirling PDF with Docker |
| [Overview](https://knuckles-team.github.io/stirlingpdf-agent/overview/) | the agent-package pattern and tool routing |
| [Concepts](https://knuckles-team.github.io/stirlingpdf-agent/concepts/) | concept registry (`CONCEPT:STIRLINGPDF-*`) |

`AGENTS.md` is the canonical contributor/agent guidance.


<!-- BEGIN agent-os-genesis-deploy (generated; do not edit between markers) -->

## Deploy with `agent-os-genesis`

This package can be provisioned for you — skill-guided — by the **`agent-os-genesis`**
universal skill (its *single-package deploy mode*): it picks your install method, seeds
secrets to OpenBao/Vault (or `.env`), trusts your enterprise CA, registers the MCP
server, and verifies it — the same machinery that stands up the whole Agent OS, narrowed
to just this package. Ask your agent to **"deploy `stirlingpdf-agent` with agent-os-genesis"**.

| Install mode | Command |
|------|---------|
| Bare-metal, prod (PyPI) | `uvx stirlingpdf-mcp` · or `uv tool install stirlingpdf-agent` |
| Bare-metal, dev (editable) | `uv pip install -e ".[all]"` · or `pip install -e ".[all]"` |
| Container, prod | deploy `knucklessg1/stirlingpdf-agent:latest` via docker-compose / swarm / podman / podman-compose / kubernetes |
| Container, dev (editable) | deploy `docker/compose.dev.yml` (source-mounted at `/src`; edits live on restart) |

Secrets are read-existing + seeded via `vault_sync` — you are only prompted for what's missing.

<!-- END agent-os-genesis-deploy -->
