{ "checker": { "claims": [ { "claim": "list_strategies returns the research strategies including langgraph-agent", "command": " tools/call {\"tool\": \"list_strategies\", \"arguments\": {}}", "evidence": "langgraph-agent\\\",\\n \\\"label\\\": \\\"LangGraph Agent (Autonomous agentic research)\\\",\\n \\\"description\\\": \\\"Agentic research where the LLM autonomously decides what to search, which engines to use, and when to synthesize. Supports all search engines as tools.\\\"\\n }\\n ]\\n}\"}], \"structuredContent\": {\"result\": {\"status\": \"success\", \"strategies\": [{\"name\": \"source-based\", \"label\": \"Source-Based (Best for small <16,000 context window)\", \"description\": \"Comprehensive research with inline citations. Focuses on finding and extracting information from authoritative sources.\"}, {\"name\": \"focus", "expected": "strategy list includes langgraph-agent", "input": "{\"tool\": \"list_strategies\", \"arguments\": {}}", "kind": "claim", "outcome": "Real input produced the expected result: langgraph-agent", "source": "| `list_strategies` | List research strategies | instant | None |", "source_checked": true, "source_file": "README.md", "verdict": "yes" }, { "claim": "list_search_engines lists available search engines including arxiv", "command": " tools/call {\"tool\": \"list_search_engines\", \"arguments\": {}}", "evidence": "\"name\": \"arxiv\", \"description\": \"Search papers uploaded to ArXiv.\", \"strengths\": [\"scientific papers\", \"academic research\", \"physics\", \"computer science\", \"mathematics\", \"statistics\", \"machine learning\", \"preprints\"], \"weaknesses\": [\"non-academic topics\", \"consumer products\", \"news\", \"general information\"], \"requires_api_key\": false, \"is_local\": false}, {\"name\": \"brave\", \"description\": \"Search the web using the Brave search engine.\", \"strengths\": [\"privacy-focused web search\", \"product information\", \"reviews\", \"recent content\", \"news\", \"broad coverage\"], \"weaknesses\": [\"requires API key with u", "expected": "engine list includes arxiv", "input": "{\"tool\": \"list_search_engines\", \"arguments\": {}}", "kind": "claim", "outcome": "Real input produced the expected result: \"name\": \"arxiv\"", "source": "| `list_search_engines` | List available search engines | instant | None |", "source_checked": true, "source_file": "README.md", "verdict": "yes" } ], "cycle_checks": [ { "claim": "Subscriptions refresh on a daily/weekly/custom schedule", "command": "", "evidence": "Scheduling is an APScheduler background job per logged-in user with an encrypted per-user database; triggering one cycle by hand needs a running web app with a user session, which was not set up in this guest. No cycle was run.", "expected": "", "input": "", "kind": "cycle", "method": "", "outcome": "Scheduling is an APScheduler background job per logged-in user with an encrypted per-user database; triggering one cycle by hand needs a running web app with a user session, which was not set up in this guest. No cycle was run.", "schedule": "", "source": "src/local_deep_research/scheduler/background.py", "verdict": "blocked" } ], "details": { "argv": [ "" ], "kind": "mcp", "scope": "product operation", "serverInfo": { "name": "local-deep-research", "version": "1.30.0" }, "tool_result": { "content": [ { "text": "{\n \"status\": \"success\",\n \"strategies\": [\n {\n \"name\": \"source-based\",\n \"label\": \"Source-Based (Best for small <16,000 context window)\",\n \"description\": \"Comprehensive research with inline citations. Focuses on finding and extracting information from authoritative sources.\"\n },\n {\n \"name\": \"focused-iteration\",\n \"label\": \"Focused Iteration - Quick (Minimal text output)\",\n \"description\": \"Fast & precise Q&A with iterative search. Good for complex queries requiring specific answers.\"\n },\n {\n \"name\": \"focused-iteration-standard\",\n \"label\": \"Focused Iteration - Comprehensive (Needs >16,000 context window)\",\n \"description\": \"Detailed long-form output with citations. Uses standard citation handler for comprehensive answers.\"\n },\n {\n \"name\": \"topic-organization\",\n \"label\": \"Topic Organization (Clusters by topic)\",\n \"description\": \"Clusters sources into topics with lead texts. Organizes research by themes for structured output.\"\n },\n {\n \"name\": \"langgraph-agent\",\n \"label\": \"LangGraph Agent (Autonomous agentic research)\",\n \"description\": \"Agentic research where the LLM autonomously decides what to search, which engines to use, and when to synthesize. Supports all search engines as tools.\"\n }\n ]\n}", "type": "text" } ], "isError": false, "structuredContent": { "result": { "status": "success", "strategies": [ { "description": "Comprehensive research with inline citations. Focuses on finding and extracting information from authoritative sources.", "label": "Source-Based (Best for small <16,000 context window)", "name": "source-based" }, { "description": "Fast & precise Q&A with iterative search. Good for complex queries requiring specific answers.", "label": "Focused Iteration - Quick (Minimal text output)", "name": "focused-iteration" }, { "description": "Detailed long-form output with citations. Uses standard citation handler for comprehensive answers.", "label": "Focused Iteration - Comprehensive (Needs >16,000 context window)", "name": "focused-iteration-standard" }, { "description": "Clusters sources into topics with lead texts. Organizes research by themes for structured output.", "label": "Topic Organization (Clusters by topic)", "name": "topic-organization" }, { "description": "Agentic research where the LLM autonomously decides what to search, which engines to use, and when to synthesize. Supports all search engines as tools.", "label": "LangGraph Agent (Autonomous agentic research)", "name": "langgraph-agent" } ] } } }, "tool_result_checked": true, "tool_schema_bytes": 11348, "tools": [ "quick_research", "detailed_research", "generate_report", "analyze_documents", "search", "list_search_engines", "list_strategies", "get_configuration" ], "working": true }, "evidence_strength": "readme_checked", "example_checks": [ { "claim": "Benchmark CLI runs a SimpleQA benchmark", "command": "", "deferred": false, "evidence": "Needs a configured LLM provider and search network access; not available in this guest. Installed package and confirmed entry points only (ldr-mcp and ldr-web). Ran the MCP discovery tools instead.", "expected": "documented command completes successfully", "input": "", "kind": "example", "outcome": "Needs a configured LLM provider and search network access; not available in this guest. Installed package and confirmed entry points only (ldr-mcp and ldr-web). Ran the MCP discovery tools instead.", "source": "README.md Benchmarking section", "verdict": "blocked" }, { "claim": "Python API quick_query / LDRClient research", "command": "", "deferred": false, "evidence": "Needs an LLM provider and a logged-in LDR user; not configured in this guest.", "expected": "documented command completes successfully", "input": "", "kind": "example", "outcome": "Needs an LLM provider and a logged-in LDR user; not configured in this guest.", "source": "README.md Python API section", "verdict": "blocked" } ], "status": "PASS" }, "deviations": [ "README installs from PyPI; this run installed the pinned source checkout with pip install \".[mcp]\" so the tested code matches the pinned commit.", "python3 -m venv failed at first (ensurepip missing); fixed by installing python3.12-venv with sudo apt-get.", "The checker path in the task was /inputs/coldpass-preflight.py, which does not exist in this guest; ran instead.", "Examples (benchmark CLI, Python API) and the cycle check were not run: they need an LLM provider and a logged-in user. Recorded as blocked with reasons.", "Used absolute venv path instead of bare ldr-mcp because the venv was not activated." ], "fault_owner": null, "main_blocker": null, "operation": "Install local-deep-research from the pinned source and call its MCP stdio server (ldr-mcp) tools list_strategies and list_search_engines, reading back real results", "operation_scope": "Linux Ubuntu 24.04, Python 3.12.3, venv at Covers install and the MCP discovery tools only. Not covered: LLM-backed research (quick_research etc.), live search engine results (Wikipedia was rate-limited, returned 0 results), the web UI, and the subscription scheduler, which need an LLM provider and a logged-in user.", "outcome": "working", "plain_failure": null, "steps": [ "git init; git remote add origin https://github.com/LearningCircuit/local-deep-research; git fetch --depth 1 origin 7c274b6f72355aeef9ef1a51396363414307534f; git reset --mixed FETCH_HEAD", "sudo apt-get install -y python3.12-venv (needed for python3 -m venv)", "python3 -m venv ", " install \".[mcp]\" (run in the checkout; README documents: pip install \"local-deep-research[mcp]\")", " (stdio MCP server); tools/call list_strategies and list_search_engines via an MCP stdio client" ], "stuck_step": null, "suggested_fix": "None for the tested path. To test research end-to-end, configure an LLM provider (for example Ollama) and a search engine, then rerun the research tools." }