🧠 ThinkMCP

A local-first research agent that shows its work — built end-to-end on the Model Context Protocol.

Qwen 3 thinks, plans, searches, self-critiques, and writes reports — entirely on your machine. No cloud LLM. No API bill. Every thought and tool call streamed live to the screen.

What it does

Ask a research question. The agent plans an approach, searches the web, ArXiv, and GitHub, remembers what it finds, critiques its own draft, and delivers a sourced answer — while the UI renders every reasoning step and tool invocation the moment it happens. Export any session as Markdown or JSON.

The same 13-tool MCP server also plugs straight into Claude Desktop, Cursor, or VS Code.

Engineering highlights

These are deliberate design decisions, not defaults:

🔌 The agent is a real MCP client — not a wrapper. It spawns the server as a subprocess, discovers tools over the protocol (tools/list), and invokes them with tools/call (agent/mcp_client.py). Zero hardcoded tool schemas; add a tool to the server and the agent picks it up automatically. This is the exact integration path Claude Desktop uses.
🏠 Local-first inference. The reasoning loop runs on Qwen 3 via Ollama (agent/thinking_agent.py), capturing the model's native thinking stream — with automatic fallback to parsing inline <think> tags for instruct-only variants. Works fully offline except optional web search.
📡 True live streaming. The agent emits events (thinking, tool_call, tool_result) through a callback; the Streamlit UI drains them from a thread-safe queue and renders mid-run — no waiting for completion (app.py).
💾 Durable memory. remember/recall are SQLite-backed (server/tools/memory.py), so findings survive restarts and are shared between the UI agent and any connected MCP host.
🚦 Two transports, one server. stdio for desktop hosts, Streamable HTTP for remote/multi-client deployments — selected at launch, not forked code.
✅ Tested and linted in CI. 31 pytest cases covering tools, agent helpers, and server registration, plus ruff — on Python 3.10 and 3.12 for every push.
📊 Measured, not assumed. The reasoning tools are evaluated with a blind ablation benchmark (two agent hosts, independent LLM judge) — and the honest result, including where the scaffolding doesn't help, is published below.

The 13 tools

Category	Tools	Notes
Research	`web_search` · `fetch_url` · `search_papers` · `search_code`	Tavily, raw HTTP, ArXiv, GitHub
Reasoning	`think` · `critique` · `plan`	deterministic, instant, reproducible
Memory	`remember` · `recall` · `list_memory`	persisted in SQLite
Actions	`write_report` · `create_summary` · `compare`	markdown deliverables

Architecture

flowchart LR
    subgraph UI["Streamlit UI (app.py)"]
        Q[Query] --> A
        A -. "live events:<br/>thinking · tool_call · tool_result" .-> Q
    end
    subgraph Agent["Thinking Agent (agent/)"]
        A[Ollama · Qwen 3<br/>native thinking + tool calls]
    end
    subgraph Server["ThinkMCP Server (server/)"]
        T1[Research tools]
        T2[Reasoning tools]
        T3[Memory · SQLite]
        T4[Action tools]
    end
    A -- "MCP over stdio<br/>tools/list · tools/call" --> Server
    C[Claude Desktop / Cursor / VS Code] -- "MCP (stdio or HTTP)" --> Server

How the agent loop works

Connect to the ThinkMCP server as an MCP client and discover all tools over the protocol.
Send the query to Qwen 3 with the discovered tool schemas and thinking enabled.
Capture the thinking stream → emit it to the UI as it arrives.
Execute each requested tool through MCP, feed results back to the model.
Repeat until the model answers (or the iteration cap trips), self-critiquing along the way.

Quickstart

1. Install Ollama and a Qwen 3 model (any tool-capable variant):

ollama pull qwen3:8b

2. Install ThinkMCP:

git clone https://github.com/Gh-Novel/ThinkMCP-Agentic-Tools
cd ThinkMCP-Agentic-Tools
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

3. (Optional) keys for web/code search — everything else needs none:

export TAVILY_API_KEY="tvly-..."   # web search — tavily.com
export GITHUB_TOKEN="ghp_..."      # code search — a zero-scope token works

4. Run:

streamlit run app.py

Opens at http://localhost:8501 — pick your model in the sidebar (installed models are auto-detected) and run a query.

Defaults are env-overridable: THINKMCP_MODEL=qwen3:14b, OLLAMA_HOST=http://...

Use the server from Claude Desktop / Cursor / VS Code

Claude Desktop — ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "thinkmcp": {
      "command": "python",
      "args": ["/absolute/path/to/thinkmcp/server/mcp_server.py"],
      "env": {
        "TAVILY_API_KEY": "tvly-...",
        "GITHUB_TOKEN": "ghp_...",
        "THINKMCP_REPORTS_DIR": "/absolute/path/to/thinkmcp/reports"
      }
    }
  }
}

Cursor — Settings → MCP → Add MCP Server, paste the same block. VS Code — add the same server (with "type": "stdio") to .vscode/mcp.json.

Restart the host and ThinkMCP appears under connected servers.

Remote HTTP server & Docker

# Streamable HTTP transport — MCP endpoint at http://host:8000/mcp
python -m transport.http_transport --host 0.0.0.0 --port 8000

docker build -t thinkmcp .

# Streamlit UI (talks to Ollama on the host machine)
docker run -p 8501:8501 \
  -e OLLAMA_HOST="http://host.docker.internal:11434" \
  -e TAVILY_API_KEY="tvly-..." \
  thinkmcp

# Or run the MCP HTTP server instead
docker run -p 8000:8000 thinkmcp \
  python -m transport.http_transport --port 8000

Benchmark: do the reasoning tools actually help?

An ablation study over a fixed 6-question set (retrieval, comparison, explanation tasks — all answerable without API keys):

Arm A sees all 13 tools and is prompted to plan / think / self-critique.
Arm B runs against a server started with THINKMCP_DISABLE_REASONING=1, which hides think/critique/plan from tools/list — the model can't see them, not merely can't call them.
Same turn budget (16) for both arms. Two agent hosts were tested: the local Qwen 3 agent, and Claude Sonnet connected to the same server over MCP.
Every answer pair was judged blind by Claude Sonnet in both orderings; an arm wins a question only if the judge picks it both times. Inconsistent picks count as ties.

Agent host	With reasoning tools	Without (ablated)	Ties
Qwen 3 VL 8B (local, Ollama)	1/6	3/6	2/6
Claude Sonnet (over MCP)	1/6	1/6	4/6

The honest read: the heuristic reasoning tools did not improve judged answer quality. What the data did show:

They change behavior, not prose quality: with plan_tool, the 8B agent averaged fewer tool calls (9.3 vs 11.8) and avoided tool-thrashing on the worst question (15 calls vs 45 for the ablated arm).
A frontier model neither needs nor is hurt by the scaffolding (4/6 ties) — and it skipped the tools entirely on pure-knowledge questions, answering in 1 turn.
The benchmark surfaced a real agent bug that's now fixed: runs that exhaust the iteration budget used to return nothing; the agent now forces a final answer from the evidence it gathered.

This is exactly why critique/plan being deterministic heuristics matters: they add structure and traceability, but no new intelligence — which motivates the LLM-backed critique mode on the roadmap. Negative results that point at the next experiment are worth more than inflated ones.

Reproduce (full per-question data lands in benchmarks/results*.json):

python benchmarks/run_benchmark.py                 # local arms (Ollama)
python benchmarks/run_benchmark_claude.py          # Sonnet arms (needs Claude Code CLI)
python benchmarks/judge_with_claude.py             # blind judging (needs Claude Code CLI)

Caveats: n=6, one run per arm, LLM-as-judge. This is a directional ablation, not a paper.

Tests & quality

pytest          # 31 tests: tools, agent helpers, MCP server registration
ruff check .    # lint

Both run in GitHub Actions on Python 3.10 and 3.12 for every push and PR.

Configuration

Env var	Default	Purpose
`OLLAMA_HOST`	`http://localhost:11434`	Ollama server URL
`THINKMCP_MODEL`	`qwen3:8b`	Default agent model
`THINKMCP_NUM_CTX`	`16384`	Context window for the agent loop
`THINKMCP_MEMORY_DB`	`./thinkmcp_memory.db`	SQLite memory location
`THINKMCP_REPORTS_DIR`	`./reports`	Where `write_report` saves files
`TAVILY_API_KEY`	—	Enables `web_search`
`GITHUB_TOKEN`	—	Enables `search_code` (GitHub's code-search API requires auth; a token with no scopes is enough)
`THINKMCP_DISABLE_REASONING`	—	Set `1` to hide `think`/`critique`/`plan` (benchmark ablation)

Project layout

thinkmcp/
├── server/
│   ├── mcp_server.py          # FastMCP server, all 13 tools registered
│   └── tools/
│       ├── research.py        # web_search, fetch_url, search_papers, search_code
│       ├── reasoning.py       # think, critique, plan
│       ├── memory.py          # remember, recall, list_memory (SQLite-backed)
│       └── actions.py         # write_report, create_summary, compare
├── agent/
│   ├── mcp_client.py          # real MCP stdio client (tools/list + tools/call)
│   └── thinking_agent.py      # Ollama (Qwen 3) agentic loop with thinking
├── transport/
│   ├── stdio_transport.py     # stdio launcher (Claude Desktop)
│   └── http_transport.py      # Streamable HTTP launcher (remote)
├── tests/                     # pytest suite
├── benchmarks/                # reasoning-tools ablation benchmark + results
├── app.py                     # Streamlit UI with live-streaming trace
├── pyproject.toml
└── Dockerfile

Design notes

critique and plan are deterministic heuristics by choice — instant, free, and reproducible. The LLM does the reasoning; these tools provide structure and a self-check ritual without burning tokens or adding nondeterminism.
Thinking capture degrades gracefully: native thinking stream when the model supports it, inline <think>-tag parsing when it doesn't.

Roadmap

Benchmark: answer quality with vs. without the reasoning tools on a fixed question set — done, results above
LLM-backed critique mode (local, via Ollama) — the benchmark's null result makes this the obvious next experiment
Semantic memory recall (embeddings over the SQLite store)
One-command hosted demo

License

MIT

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🧠 ThinkMCP

What it does

Engineering highlights

The 13 tools

Architecture

How the agent loop works

Quickstart

Use the server from Claude Desktop / Cursor / VS Code

Remote HTTP server & Docker

Benchmark: do the reasoning tools actually help?

Tests & quality

Configuration

Project layout

Design notes

Roadmap

License

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🧠 ThinkMCP

What it does

Engineering highlights

The 13 tools

Architecture

How the agent loop works

Quickstart

Use the server from Claude Desktop / Cursor / VS Code

Remote HTTP server & Docker

Benchmark: do the reasoning tools actually help?

Tests & quality

Configuration

Project layout

Design notes

Roadmap

License