Smart-Thinking

[](https://mseep.ai/app/leghis-smart-thinking) # Smart-Thinking [](https://smithery.ai/server/@Leghis/smart-thinking) [](https://www.npmjs.com/package/smart-thinking-mcp) [](https://opensource.org/licenses/MIT) [](https://www.typescriptlang.org/) [](https://github.com/Leghis/smart-thinking-mcp) [](https://github.com/Leghis/smart-thinking-mcp) [](https://github.com/Leghis/smart-thinking-mcp) Smart-Thinking is a Model Context Protocol (MCP) server that delivers graph-based, multi-step reasoning without relying on external AI APIs. Everything happens locally: similarity search, heuristic-based scoring, verification tracking, memory, and visualization all run in a deterministic pipeline designed for transparency and reproducibility. ## Core Capabilities - Graph-first reasoning that connects thoughts with rich relationships (supports, contradicts, refines, contextual links, and more). - Local TF-IDF + cosine similarity engine powering memory lookups and graph expansion without third-party embedding services. - Heuristic quality evaluation that scores confidence, relevance, and quality using transparent rules instead of LLM calls. - Verification workflow with detailed statuses and calculation tracing to surface facts, guardrails, and uncertainties. - Persistent sessions that can be resumed across runs, keeping both the reasoning graph and verification ledger in sync. ## Reasoning Flow 1. **Session bootstrap** – `ReasoningOrchestrator` initializes a session, restores any saved graph state, and prepares feature flags. 2. **Pre-verification** – deterministic guards inspect the incoming thought, perform light-weight calculation checks, and annotate the payload. 3. **Graph integration** – the thought is inserted into `ThoughtGraph`, linking to context, prior thoughts, and relevant memories. 4. **Heuristic evaluation** – `QualityEvaluator` and `MetricsCalculator` compute weighted scores and traces that explain the decision path. 5. **Verification feedback** – statuses from `VerificationService` and heuristic traces are attached to the node and propagated across connections. 6. **Persistence & response** – updates are written to `MemoryManager`/`VerificationMemory`, and a structured MCP response is returned with a timeline of reasoning steps. Each step is logged with structured metadata so you can visualize the reasoning fabric, audit decisions, and replay sessions deterministically. ## Installation Smart-Thinking ships as an npm package compatible with Windows, macOS, and Linux. ### Global install (recommended) ```bash npm install -g smart-thinking-mcp ``` ### Run with npx ```bash npx -y smart-thinking-mcp ``` ### Install via Smithery ```bash npx -y @smithery/cli install @Leghis/smart-thinking --client claude ``` ### From source ```bash git clone https://github.com/Leghis/Smart-Thinking.git cd Smart-Thinking npm install npm run build npm link ``` > Need platform-specific configuration details? See `GUIDE_INSTALLATION.md` for step-by-step instructions covering Windows, macOS, Linux, and Claude Desktop integration. ## Quick Tour - `smart-thinking-mcp` — start the MCP server (globally installed package). - `npx -y smart-thinking-mcp` — launch without a global install. - `npm run start` — execute the built server from source. - `npm run demo:session` — run the built-in CLI walkthrough that feeds sample thoughts through the reasoning pipeline and prints the resulting timeline. The demo script showcases how the orchestrator adds nodes, evaluates heuristics, and records verification feedback step by step. ## MCP Client Compatibility Smart-Thinking is validated across the most popular MCP clients and operating systems. Use the new connector mode (`--mode=connector` or `SMART_THINKING_MODE=connector`) when a client only accepts the `search` and `fetch` tools required by ChatGPT connectors.[^openai-mcp] | Client | Transport | Notes | | --- | --- | --- | | **ChatGPT Connectors & Deep Research** | HTTP + SSE | Deploy with `SMART_THINKING_MODE=connector node build/index.js --transport=http --host 0.0.0.0 --port 8000`. Point ChatGPT to `https://<host>/sse` and keep only `search`/`fetch` enabled, aligning with OpenAI’s remote MCP guidance.[^openai-mcp] | | **OpenAI Codex CLI & Agents SDK** | Streamable HTTP / SSE | Configure the Codex agent with `http://localhost:3000/mcp` or `http://localhost:3000/sse` and set `SMART_THINKING_MODE=connector` when only knowledge retrieval is needed.[^openai-agents] | | **Claude Desktop / Claude Code** | stdio | Add `"command": "smart-thinking-mcp"` (or an `npx` command) to `claude_desktop_config.json`. Full toolset is available.[^mcp-clients] | | **Cursor IDE** | stdio / SSE / Streamable HTTP | Add the server to `~/.cursor/mcp.json` or the project `.cursor/mcp.json`. Cursor supports prompts, roots, elicitation, and streaming.[^cursor-mcp] | | **Cline (VS Code)** | stdio | Place the command in `~/Documents/Cline/MCP/smart-thinking.json` or use the in-app marketplace to register the toolset.[^mcp-clients] | | **Kilo Code** | stdio | Register via the MCP marketplace and run the server locally; Smart-Thinking exposes deterministic tooling for autonomous edits.[^mcp-clients] | > Need a minimal deployment footprint? Combine `--transport=http --mode=connector` with a reverse proxy (ngrok, fly.io, render, etc.) so remote clients can consume the server without exposing the full toolset. ## Configuration & Feature Flags - `feature-flags.ts` toggles advanced behaviours such as external integrations (disabled by default) and verbose tracing. - `config.ts` aligns platform-specific paths and verification thresholds. - `memory-manager.ts` and `verification-memory.ts` store session graphs, metrics, and calculation results using deterministic JSON snapshots. ## Development Workflow ```bash npm run build # Compile TypeScript sources npm run lint # ESLint across src/ npm run test # Jest test suite npm run test:coverage # Jest coverage report npm run watch # Incremental TypeScript compilation ``` See `TRANSFORMATION_PLAN.md` for the full transformation history and the checklist that drives ongoing hardening. ## Quality & Support - Deterministic heuristics and verification eliminate dependency on remote LLMs. - Coverage targets: ≥80 % on persistence modules, ≥60 % branch coverage across orchestrator logic. - CI recommendations: run `npm run lint` and `npm run test:coverage` before each release candidate. ## Contributing Contributions are welcome. Please open an issue or pull request describing the change, and run the quality checks above before submitting. ## License [MIT](./LICENSE) [^openai-mcp]: OpenAI, “Building MCP servers for ChatGPT and API integrations,” highlights that connectors require `search` and `fetch` tools for remote use. (https://platform.openai.com/docs/mcp) [^openai-agents]: OpenAI Agents SDK documentation on MCP transports (stdio, SSE, streamable HTTP). (https://openai.github.io/openai-agents-python/mcp/) [^mcp-clients]: Model Context Protocol client catalogue listing Claude, Cline, Kilo Code, and other MCP-compatible applications. (https://modelcontextprotocol.io/clients) [^cursor-mcp]: Cursor documentation for configuring MCP servers via stdio/SSE/HTTP transports. (https://cursor.com/docs/context/mcp)