Command output too long. The first 14346 bytes:

```
{
  "items": [
    {
      "assignees": [
        "peterdonaghey"
      ],
      "content": {
        "body": "## Summary\n\nThe UPC query agent currently produces four outputs: SQL query, **SQL explanation**, CSV results, and CSV analysis. The SQL explanation field is no longer needed and should be removed from the agent prompt/output, the API, and the frontend UI.\n\nDB columns in Snowflake (`core.results.sql_explanation`) and DynamoDB attributes can remain — just set them to `null` going forward for backward compatibility. Changing live table shapes causes unnecessary headaches.\n\n---\n\n## Scope\n\n### 1. Agent — `agents/upc_query_agent.py`\n- Remove `sql_explanation: str` field from `UpcQueryAgentSuccess` Pydantic model\n- Update system/user prompt to no longer instruct the LLM to produce an explanation\n\n### 2. API — `api/routes/query.py`\n- Remove any read/log of `sql_result.output.sql_explanation`\n- Remove `sql_explanation` from the payload passed to the CSV analysis agent\n- Remove it from the combined result dict returned to the client\n\n### 3. API — `api/routes/history.py`\n- Remove `\"sql_explanation\": result.get('sql_explanation')` from the history row serializer\n\n### 4. Data types / dataclasses\n- `agents/utils/query_result_handler.py` — remove `sql_explanation: str` from `QueryResult` dataclass; stop writing it to DynamoDB (or write `None`)\n- `agents/utils/agent_log_handler.py` — keep `AgentLog.sql_explanation: Optional[str]` as nullable for backward compat; ensure INSERT still works with `NULL`\n\n### 5. Sync scripts (backward compat — no column removal)\n- `infrastructure/sync/dynamodb_to_snowflake_sync.py` — keep the column mapping but pass `None`/`NULL`\n- `infrastructure/migration/sync_snowflake_to_dynamodb.py` — no change needed (existing rows can keep their data)\n\n### 6. Frontend — `frontend/src/types/index.ts`\n- Make `QueryResponse.sql_explanation` and `QueryHistoryItem.sql_explanation` optional (`string | undefined` or remove)\n\n### 7. Frontend — `frontend/src/components/Query/QueryResults.tsx`\n- Remove the entire \"SQL Explanation\" section (lines ~362–417)\n\n### 8. Frontend — `frontend/src/components/Results/QueryDetail.tsx`\n- Remove `sql_explanation: query.sql_explanation` from props passed to `QueryResults`\n\n### 9. CSV analysis agent prompt — `scripts/seed_all_prompts.py`\n- Update `CSV_ANALYSIS_SYSTEM` prompt to remove mention of \"Query explanation\" from the input it expects\n\n### 10. Docs / dev notes\n- `.dev/query_feedback_cheatsheet.md` — update DynamoDB attribute table to note field is now deprecated/null\n\n---\n\n## Out of scope\n- No DDL changes to Snowflake `core.results` table\n- No DynamoDB schema changes (schemaless — existing items keep their data)\n- No changes to any migration scripts beyond null-safety\n\n---\n\n## Acceptance criteria\n- LLM is no longer prompted to produce a SQL explanation\n- No `sql_explanation` appears in any API response\n- SQL Explanation section is gone from the UI (both live query results and history detail views)\n- All existing tests pass; no type errors\n- Snowflake/DynamoDB columns still exist but receive `NULL`/`None` for new writes",
        "number": 42,
        "repository": "Point-Topic/upc_query_agent",
        "title": "Remove SQL explanation from UPC query agent, API, and UI",
        "type": "Issue",
        "url": "https://github.com/Point-Topic/upc_query_agent/issues/42"
      },
      "id": "PVTI_lADOBNsuRs4BL8K8zgoMCT0",
      "labels": [
        "enhancement"
      ],
      "linked pull requests": [
        "https://github.com/Point-Topic/upc_query_agent/pull/44"
      ],
      "repository": "https://github.com/Point-Topic/upc_query_agent",
      "status": "Done",
      "title": "Remove SQL explanation from UPC query agent, API, and UI"
    },
    {
      "assignees": [
        "peterdonaghey"
      ],
      "content": {
        "body": "## summary\n\nMCP tool calls now display immediately in the frontend as they are triggered, showing input parameters and an executing state. previously, tool cells only appeared when results returned.\n\n## problem\n\nwhen the model called an MCP tool, the user saw nothing until the tool finished executing. for slow tools (database queries, web searches), this meant several seconds of blank space with no feedback.\n\nadditionally, once partially working:\n- duplicate tool cells appeared (one from `PartStartEvent`, another from `FunctionToolCallEvent`)\n- input parameters were garbled (unparsed string split into character array)\n- only one of the two cells got populated with the result\n\n## solution\n\n### agent-in-task architecture\nthe pydantic-ai `agent.iter()` loop now runs in a separate `asyncio.Task`. this is critical because the SSE generator blocks during tool execution — if the agent ran inline, the `mcp_tool_callback` would enqueue events that could never be drained. by running in a task, the callback puts to `sse_queue` immediately and the generator drains in parallel.\n\n### single source of truth for tool_call events\nthe `mcp_tool_callback` (invoked by `process_tool_call` hook BEFORE tool execution) is the **sole** place that sends `tool_call` events to the frontend for MCP tools. removed duplicate emissions from `PartStartEvent` and `FunctionToolCallEvent` handlers.\n\n### cleanup\n- removed dead `streaming_messages` list and `event_stream_handler()` function\n- removed unused imports (`AgentStreamEvent`, `TextPartDelta`, `ToolCallPartDelta`, `ThinkingPartDelta`, `RunContext`, `AsyncIterable`, `httpx`)\n- consolidated `tool_call_index_container` mutable wrapper → direct `nonlocal tool_call_index`\n- moved `import re` from loop body to top-level\n- fixed silent fallback in `_expand_env_value` (both agents) — now crashes on missing env vars instead of passing literal `${VAR_NAME}` strings\n\n## files changed\n\n- `api/routes/chat.py` — agent-in-task architecture, fixed event handlers, dead code removal\n- `api/routes/pt_agent.py` — ported same architecture from chat agent\n- `agents/chat_agent.py` — fixed `_expand_env_value` silent fallback, removed unused imports\n- `agents/pt_agent.py` — same fixes, removed duplicate `mcp_process_tool_call` import\n- `docs/MCP_TOOL_STREAMING_FLOW.md` — updated to reflect actual architecture\n\n## key technical decisions\n\n- **why agent-in-task?** the SSE generator blocks on `anext(stream)` during tool execution. `mcp_tool_callback` fires and enqueues, but nothing can drain until tool finishes. running in a task lets both happen concurrently.\n- **why remove PartStartEvent tool_call?** `event.part.args` at `PartStartEvent` time is an unparsed string, not a dict — causes garbled display.\n- **why remove FunctionToolCallEvent tool_call?** pydantic-ai emits `FunctionToolCallEvent` for ALL tools in a batch BEFORE any execution (discovered by reading `_agent_graph.py` source). dedup checks fail because the callback hasn't fired yet.\n\n## references\n\n- pydantic-ai #2497 (event_stream_handler pattern)\n- pydantic-ai #640 (MCP streaming)\n- pydantic-ai #2700, #2355, #2818 (MCP stdio lifecycle issues)\n- datastud.dev MCP streaming article",
        "number": 40,
        "repository": "Point-Topic/upc_query_agent",
        "title": "MCP tool streaming: immediate display, agent-in-task architecture, cleanup",
        "type": "Issue",
        "url": "https://github.com/Point-Topic/upc_query_agent/issues/40"
      },
      "id": "PVTI_lADOBNsuRs4BL8K8zgn3Q_s",
      "labels": [
        "enhancement"
      ],
      "linked pull requests": [
        "https://github.com/Point-Topic/upc_query_agent/pull/41"
      ],
      "repository": "https://github.com/Point-Topic/upc_query_agent",
      "status": "Done",
      "title": "MCP tool streaming: immediate display, agent-in-task architecture, cleanup"
    },
    {
      "assignees": [
        "peterdonaghey"
      ],
      "content": {
        "body": "## Overview\nHuman-supervised AI for GBS (Global Broadband Statistics) data entry. Jolanta and colleagues have done this manually for years. The agent is helpful when supervised but not ready to run autonomously.\n\n**Full outline:** `.dev/pt-systems-agent-gbs-tariffs-task-outline.md` (in notes repo)\n\n## Sub-tasks\n- [x] GBS tools — Point-Topic/point-topic-mcp#63\n- [x] Add Brave Search MCP — Point-Topic/upc_query_agent#38\n- [ ] File-to-sources integration — Point-Topic/upc_query_agent#39\n\n## Dependencies\n- GBS tools → prerequisite for file-to-sources (shares create_source)\n- Brave Search — independent, quick config change\n\n## Workflow (high level)\n1. See gaps → list what exists for period, summary of coverage\n2. Collect info → Jolanta provides sources or agent searches web\n3. Parse & create → agent calls tools to add Statistics (GBS) and Sources\n4. Repeat",
        "number": 37,
        "repository": "Point-Topic/upc_query_agent",
        "title": "PT Systems Agent + GBS Pipeline",
        "type": "Issue",
        "url": "https://github.com/Point-Topic/upc_query_agent/issues/37"
      },
      "id": "PVTI_lADOBNsuRs4BL8K8zgnpqXM",
      "repository": "https://github.com/Point-Topic/upc_query_agent",
      "status": "In progress",
      "title": "PT Systems Agent + GBS Pipeline"
    },
    {
      "assignees": [
        "peterdonaghey"
      ],
      "content": {
        "body": "## pt-mail — Inbound Email Pipeline\n\nOutlook forwards to `name@inbound.point-topic.com` → Mailchimp webhook → Snowflake PT_MAIL. Fetch via point-topic-mcp (execute_query) or bash script (GET API + AWS token).\n\n### Flow\n\n```mermaid\nflowchart TB\n    subgraph team[\"PT Team\"]\n        A1[Outlook]\n        A2[Outlook]\n    end\n    subgraph mailchimp[\"Mailchimp\"]\n        C[\"MX inbound.point-topic.com\"]\n        D[Webhook]\n    end\n    subgraph server[\"rapidswitch-server\"]\n        E[Flask webhook]\n        F[(Snowflake PT_MAIL)]\n        G[\"GET /emails\"]\n    end\n    subgraph fetch[\"Fetch\"]\n        H1[\"point-topic-mcp execute_query\"]\n        H2[\"bash script + AWS token\"]\n    end\n    A1 --> C\n    A2 --> C\n    C --> D --> E --> F\n    G --> F\n    H1 --> F\n    H2 -->|Bearer| G\n```\n\n### Auth\n- **Webhook**: Verify X-Mandrill-Signature (HMAC-SHA1, key from Mandrill route settings)\n- **Fetch (mcp)**: Snowflake creds (devs already have)\n- **Fetch (bash)**: Bearer from AWS Secrets `point-topic/inbound-email-api-key` — requires AWS CLI auth (gates to PT devs)\n\n### Opt-in\nNot automatic. Each person enables Outlook forwarding to `firstname.lastname@inbound.point-topic.com` (Settings → Email → Forwarding). \n\nScreenshot: \n\n<img width=\"861\" height=\"267\" alt=\"Screenshot showing the settings screen on the Outlook web UI. Settings > Email > Forwarding > Enable forwarding SELECTED, Forward my email to: peter.donaghey@inbound.point-topic.com, Kerep a copy of forwarded messages SELECTED\" src=\"https://github.com/user-attachments/assets/0c9cc78a-642c-4ef6-9303-b467cfcbc0de\" />\n\n### Repo structure\n```\npt-mail/\n├── README.md\n├── server/ (app.py, requirements.txt)\n├── sql/ (create_table.sql)\n├── scripts/ (setup_mailchimp.py, fetch_my_emails.sh)\n└── .env.example\n```\n\n### Implementation details\n\n**Webhook payload** (form-urlencoded): `mandrill_events` = JSON array. Each item: `event: \"inbound\"`, `msg`: `email` (to addr), `from_email`, `from_name`, `subject`, `text`, `html`, `to` (array). Accept empty-array test POST (signed with `test-webhook`) → return 200.\n\n**Snowflake**: Account `fz24086.eu-west-1`, database `PT_MAIL`, schema `PUBLIC`. Table `inbound_emails`: id (uuid), received_at (timestamp_ntz), to_email, from_email, from_name, subject, text_body, html_body (variant), message_id. Server env: SNOWFLAKE_ACCOUNT, SNOWFLAKE_USER, SNOWFLAKE_PASSWORD, SNOWFLAKE_WAREHOUSE, SNOWFLAKE_DATABASE=PT_MAIL, INBOUND_API_KEY.\n\n**GET /emails**: Params `to_email` (required), `limit` (default 20). Auth: `Authorization: Bearer <token>`.\n\n**Mailchimp setup**: API `inbound/add-domain` (domain: inbound.point-topic.com), `inbound/add-route` (pattern: `*`, url: webhook URL). MX values — Web UI only (Inbound → DNS Settings), add to PT DNS.\n\n**Server**: rapidswitch-server (ssh rapidswitch-server). Nginx + gunicorn. Webhook URL TBD (e.g. https://mcp.point-topic.com/inbound-webhook or new subdomain).\n\n**fetch_my_emails.sh**: `aws secretsmanager get-secret-value --secret-id point-topic/inbound-email-api-key --query SecretString --output text` → curl GET with Bearer. Example: `fetch_my_emails oliver.johnson` → to_email=oliver.johnson@inbound.point-topic.com.\n\n**requirements.txt**: flask, snowflake-connector-python\n\n### Build order\n1. sql/create_table.sql\n2. server/app.py (POST /inbound-webhook, GET /emails)\n3. Mailchimp: add domain, route, MX in DNS\n4. AWS Secrets (API key)\n5. scripts/fetch_my_emails.sh\n6. Deploy (gunicorn, nginx, systemd)\n7. README",
        "number": 1,
        "repository": "Point-Topic/pt-mail",
        "title": "PT_MAIL blueprint — implementation plan",
        "type": "Issue",
        "url": "https://github.com/Point-Topic/pt-mail/issues/1"
      },
      "id": "PVTI_lADOBNsuRs4BL8K8zgm_Mg0",
      "repository": "https://github.com/Point-Topic/pt-mail",
      "status": "In review",
      "title": "PT_MAIL blueprint — implementation plan"
    },
    {
      "assignees": [
        "peterdonaghey"
      ],
      "content": {
        "body": "as per chat, one-shot agent appears unaware the `ontology` exists - this wasnt the case before hmmm\n\nhttps://agent.point-topic.com/results/query_efaadfd485b341ab",
        "number": 36,
        "repository": "Point-Topic/upc_query_agent",
        "title": "Agent (one shot) doesnt know ontology exists",
        "type": "Issue",
        "url": "https://github.com/Point-Topic/upc_query_agent/issues/36"
      },
      "id": "PVTI_lADOBNsuRs4BL8K8zgnps4E",
      "linked pull requests": [
        "https://github.com/Point-Topic/upc_query_agent/pull/62"
      ],
      "repository": "https://github.com/Point-Topic/upc_query_agent",
      "status": "Done",
      "title": "Agent (one shot) doesnt know ontology exists"
    },
    {
      "assignees": [
        "peterdonaghey"
      ],
      "content": {
```