Find relevant Smart‑Thinking memories fast. Fetch full entries by ID to get complete context. Spee…
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.
💡 Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)
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.
ReasoningOrchestrator initializes a session, restores any saved graph state, and prepares feature flags.ThoughtGraph, linking to context, prior thoughts, and relevant memories.QualityEvaluator and MetricsCalculator compute weighted scores and traces that explain the decision path.VerificationService and heuristic traces are attached to the node and propagated across connections.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.
Smart-Thinking ships as an npm package compatible with Windows, macOS, and Linux.
Need platform-specific configuration details? See
GUIDE_INSTALLATION.mdfor step-by-step instructions covering Windows, macOS, Linux, and Claude Desktop integration.
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.
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.1
| 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.1 |
| 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.2 |
| Claude Desktop / Claude Code | stdio | Add "command": "smart-thinking-mcp" (or an npx command) to claude_desktop_config.json. Full toolset is available.3 |
| 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.4 |
| Cline (VS Code) | stdio | Place the command in ~/Documents/Cline/MCP/smart-thinking.json or use the in-app marketplace to register the toolset.3 |
| Kilo Code | stdio | Register via the MCP marketplace and run the server locally; Smart-Thinking exposes deterministic tooling for autonomous edits.3 |
Need a minimal deployment footprint? Combine
--transport=http --mode=connectorwith a reverse proxy (ngrok, fly.io, render, etc.) so remote clients can consume the server without exposing the full toolset.
For registry scanners and fallback metadata extraction, Smart-Thinking also exposes:
GET /.well-known/mcp/server-card.jsonfeature-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.ToolIntegrator.executePython, executeJavaScript) and external tool calls return a local fallback result.FeatureFlags.externalLlmEnabled and FeatureFlags.externalEmbeddingEnabled remain disabled by default, so no remote LLM/embedding provider is required.See docs/modernisation-smart-thinking-v12-plan.md for the modernization checklist and rollout tracking.
80.47% statements, 81.59% lines, 84.34% functions, 63.48% branches.npm run lint and npm run test:coverage before each release candidate.Contributions are welcome. Please open an issue or pull request describing the change, and run the quality checks above before submitting.
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) ↩ ↩2
OpenAI Agents SDK documentation on MCP transports (stdio, SSE, streamable HTTP). (https://openai.github.io/openai-agents-python/mcp/) ↩
Model Context Protocol client catalogue listing Claude, Cline, Kilo Code, and other MCP-compatible applications. (https://modelcontextprotocol.io/clients) ↩ ↩2 ↩3
Cursor documentation for configuring MCP servers via stdio/SSE/HTTP transports. (https://cursor.com/docs/context/mcp) ↩
Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.
[](https://allmcps.com/mcp/leghis-smart-thinking)<a href="https://allmcps.com/mcp/leghis-smart-thinking"><img src="https://allmcps.com/api/badge/leghis-smart-thinking?style=directory" alt="Leghis Smart Thinking on AllMCPs" /></a>