Run 0bedea8a-aaed-42d7-9ce1-b123b382d783

Raw output

{"type":"result","subtype":"success","is_error":false,"api_error_status":null,"duration_ms":60976,"duration_api_ms":59397,"ttft_ms":4065,"ttft_stream_ms":3567,"time_to_request_ms":44,"num_turns":6,"result":"{\n  \"current_state_summary\": \"Iron Rod has a complete API-first RAG core (ingest, hybrid retrieval, tier-aware scoring, optional cross-encoder rerank, citation/grounding/hallucination guardrails, eval harness) in packages/core, a FastAPI service exposing /v1/answer plus health/stats/version in packages/api, and a click-based operator CLI (`lds-rag`) with ingest/index/search/answer/eval/stats commands. Unit tests cover the guardrail and tiering modules.\",\n  \"gap_analysis\": \"The definition of done calls for a thin CLI/MCP wrapper. The CLI exists, but no MCP server is implemented anywhere in the repo — packages/api/__init__.py references an MCP wrapper that calls the endpoints, but the actual server, tool definitions, and tests are absent. This is the only verifiable, code-level gap remaining; the RAG core, tier-aware retrieval, anti-hallucination guardrails, API, and test suite are all in place.\",\n  \"completion_percent\": 90,\n  \"estimated_hours_remaining\": 6,\n  \"confidence\": \"medium\",\n  \"tasks_to_mark_complete\": [],\n  \"proposed_tasks\": [\n    {\n      \"title\": \"Implement thin MCP server wrapping the /v1/answer API\",\n      \"description\": \"Add the missing MCP wrapper promised by the definition of done and referenced in packages/api/__init__.py. Create an MCP server (e.g. packages/mcp or a module under api) exposing the RAG capabilities as MCP tools — at minimum an `answer` tool and a `search` tool — that call the existing FastAPI /v1/answer (and retrieval) surface rather than re-implementing the pipeline. Keep it thin: forward query/tier/source-class/top_k params, surface the structured Answer (text, citations, refusal status) back through MCP. Register an entry point/console script and document usage. Include unit tests mocking the HTTP/core layer to verify tool registration, parameter passthrough, and refusal/citation passthrough.\",\n      \"estimated_minutes\": 360,\n      \"subtasks\": [\n        { \"title\": \"Create MCP server module with answer + search tool definitions\", \"estimated_minutes\": 150 },\n        { \"title\": \"Wire tools to call the API/core answer pipeline and map Answer -> MCP response\", \"estimated_minutes\": 90 },\n        { \"title\": \"Add entry point / console script and packaging metadata\", \"estimated_minutes\": 30 },\n        { \"title\": \"Unit tests for tool registration, param passthrough, and refusal/citation handling\", \"estimated_minutes\": 90 }\n      ]\n    }\n  ]\n}","stop_reason":"end_turn","session_id":"6a26cb00-4980-4245-a90a-9815457548de","total_cost_usd":0.5776775,"usage":{"input_tokens":9840,"cache_creation_input_tokens":34687,"cache_read_input_tokens":230165,"output_tokens":2661,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":34687,"ephemeral_5m_input_tokens":0},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1209,"cache_read_input_tokens":49177,"cache_creation_input_tokens":1131,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1131},"type":"message"}],"speed":"standard"},"modelUsage":{"claude-opus-4-8":{"inputTokens":9840,"outputTokens":2661,"cacheReadInputTokens":230165,"cacheCreationInputTokens":34687,"webSearchRequests":0,"costUSD":0.5776775,"contextWindow":1000000,"maxOutputTokens":64000}},"permission_denials":[],"terminal_reason":"completed","fast_mode_state":"off","uuid":"a1470acd-8208-4ce4-a92d-74f42b748614"}

Parsed result

{
  "confidence": "medium",
  "operational": {
    "kind": "none",
    "drift": "n/a",
    "score": null,
    "checks": [],
    "status": "not_applicable",
    "latest_tag": "v0.1.0",
    "deployed_version": null
  },
  "gap_analysis": "The definition of done calls for a thin CLI/MCP wrapper. The CLI exists, but no MCP server is implemented anywhere in the repo — packages/api/__init__.py references an MCP wrapper that calls the endpoints, but the actual server, tool definitions, and tests are absent. This is the only verifiable, code-level gap remaining; the RAG core, tier-aware retrieval, anti-hallucination guardrails, API, and test suite are all in place.",
  "proposed_tasks": [
    {
      "title": "Implement thin MCP server wrapping the /v1/answer API",
      "subtasks": [
        {
          "title": "Create MCP server module with answer + search tool definitions",
          "estimated_minutes": 150
        },
        {
          "title": "Wire tools to call the API/core answer pipeline and map Answer -> MCP response",
          "estimated_minutes": 90
        },
        {
          "title": "Add entry point / console script and packaging metadata",
          "estimated_minutes": 30
        },
        {
          "title": "Unit tests for tool registration, param passthrough, and refusal/citation handling",
          "estimated_minutes": 90
        }
      ],
      "description": "Add the missing MCP wrapper promised by the definition of done and referenced in packages/api/__init__.py. Create an MCP server (e.g. packages/mcp or a module under api) exposing the RAG capabilities as MCP tools — at minimum an `answer` tool and a `search` tool — that call the existing FastAPI /v1/answer (and retrieval) surface rather than re-implementing the pipeline. Keep it thin: forward query/tier/source-class/top_k params, surface the structured Answer (text, citations, refusal status) back through MCP. Register an entry point/console script and document usage. Include unit tests mocking the HTTP/core layer to verify tool registration, parameter passthrough, and refusal/citation passthrough.",
      "estimated_minutes": 360
    }
  ],
  "completion_percent": 90,
  "current_state_summary": "Iron Rod has a complete API-first RAG core (ingest, hybrid retrieval, tier-aware scoring, optional cross-encoder rerank, citation/grounding/hallucination guardrails, eval harness) in packages/core, a FastAPI service exposing /v1/answer plus health/stats/version in packages/api, and a click-based operator CLI (`lds-rag`) with ingest/index/search/answer/eval/stats commands. Unit tests cover the guardrail and tiering modules.",
  "tasks_to_mark_complete": [],
  "estimated_hours_remaining": 6
}