Smart-Thinking

An advanced MCP server for multi-dimensional, adaptive, and collaborative reasoning.

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Smart-Thinking

smithery badge npm version License: MIT TypeScript Platform: Windows Platform: macOS Platform: Linux

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)

npm install -g smart-thinking-mcp

Run with npx

npx -y smart-thinking-mcp

Install via Smithery

npx -y @smithery/cli install @Leghis/smart-thinking --client claude

From source

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.1

ClientTransportNotes
ChatGPT Connectors & Deep ResearchHTTP + SSEDeploy 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 SDKStreamable HTTP / SSEConfigure 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 CodestdioAdd "command": "smart-thinking-mcp" (or an npx command) to claude_desktop_config.json. Full toolset is available.3
Cursor IDEstdio / SSE / Streamable HTTPAdd the server to ~/.cursor/mcp.json or the project .cursor/mcp.json. Cursor supports prompts, roots, elicitation, and streaming.4
Cline (VS Code)stdioPlace the command in ~/Documents/Cline/MCP/smart-thinking.json or use the in-app marketplace to register the toolset.3
Kilo CodestdioRegister 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=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

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

Footnotes

  1. 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

  2. OpenAI Agents SDK documentation on MCP transports (stdio, SSE, streamable HTTP). (https://openai.github.io/openai-agents-python/mcp/) ↩

  3. Model Context Protocol client catalogue listing Claude, Cline, Kilo Code, and other MCP-compatible applications. (https://modelcontextprotocol.io/clients) ↩ ↩2 ↩3

  4. Cursor documentation for configuring MCP servers via stdio/SSE/HTTP transports. (https://cursor.com/docs/context/mcp) ↩

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