Elite Reasoning MCP

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<h3>Make any LLM think harder, reason better, and never repeat mistakes.</h3>

<p> <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-blue.svg?style=flat-square" alt="MIT License" /></a> <a href="https://pypi.org/project/elite-reasoning-mcp/"><img src="https://img.shields.io/pypi/v/elite-reasoning-mcp?style=flat-square&color=blue" alt="PyPI" /></a> <a href="https://pypi.org/project/elite-reasoning-mcp/"><img src="https://img.shields.io/pypi/dm/elite-reasoning-mcp?style=flat-square&color=green&label=downloads" alt="Downloads" /></a> <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/pypi/pyversions/elite-reasoning-mcp?style=flat-square&logo=python&logoColor=white" alt="Python 3.11+" /></a> <a href="https://glama.ai/mcp/servers/Snehgabani/elite-reasoning-mcp"><img src="https://glama.ai/mcp/servers/Snehgabani/elite-reasoning-mcp/badges/score.svg" alt="Glama Score" /></a> <a href="https://github.com/Snehgabani/elite-reasoning-mcp/actions"><img src="https://img.shields.io/github/actions/workflow/status/Snehgabani/elite-reasoning-mcp/ci.yml?style=flat-square&label=CI" alt="CI" /></a> <a href="https://github.com/Snehgabani/elite-reasoning-mcp/stargazers"><img src="https://img.shields.io/github/stars/Snehgabani/elite-reasoning-mcp?style=flat-square&color=yellow" alt="Stars" /></a> </p>

<p> <b>66 tools</b> · <b>Zero API costs</b> · <b>132KB</b> · <b>Works with any model</b> </p>

<p> <a href="#quick-install">Install</a> · <a href="docs/TOOLS.md">All 66 Tools</a> · <a href="docs/ARCHITECTURE.md">Architecture</a> · <a href="CONTRIBUTING.md">Contributing</a> </p>

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The Problem

AI coding assistants make the same mistakes over and over. They forget context between sessions. They don't stress-test their own reasoning. The quality of output varies wildly between prompts.

Elite Reasoning MCP intercepts every prompt and runs it through a reasoning pipeline — classifying intent, checking past mistakes, routing to the right tools, and tracking quality — before the LLM generates a single token.

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How It Works

Your Prompt
    │
    ├── Intent Classifier      →  debug / build / design / deploy (13 types)
    ├── Complexity Scorer      →  1–5, adjusts reasoning depth
    ├── Anti-Pattern Checker   →  "You made this mistake before..."
    ├── Prevention Rules       →  Custom auto-triggered safeguards
    ├── MCP & Skill Router     →  Routes to the best available tools
    └── Pre-flight Checklist   →  Per-task reasoning steps
    │
    ▼
Execution Plan → LLM follows it → Better output

Everything runs locally. No API calls. No cloud. A single SQLite file stores anti-patterns, decisions, quality scores, and calibration data across sessions.

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Quick Install

pip (Recommended)

pip install elite-reasoning-mcp

From Source (macOS / Linux)

git clone https://github.com/Snehgabani/elite-reasoning-mcp.git ~/.elite-reasoning
cd ~/.elite-reasoning && bash scripts/install.sh

Windows (PowerShell)

git clone https://github.com/Snehgabani/elite-reasoning-mcp.git $env:USERPROFILE\.elite-reasoning
cd $env:USERPROFILE\.elite-reasoning; .\scripts\install.ps1

Docker

docker run -v elite-brain:/data/brain ghcr.io/snehgabani/elite-reasoning-mcp

Requirements: Python 3.11+ and uv (auto-installed by the installer).

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What's Inside

66 Tools, 7 Categories

Category	Tools	What They Do
Core Pipeline	orchestrate_request_tool assess_confidence reasoning_preflight	Intent classification, complexity scoring, pre-flight checklists
Quality & Memory	record_mistake check_anti_patterns record_quality_score get_quality_trend	Anti-pattern database, quality tracking, trend analysis
Decision Making	decision_council_review record_decision search_decisions	5-perspective adversarial review, decision logging
Risk Analysis	fmea_analysis swiss_cheese_audit smoke_test_gate bias_scan	Failure mode analysis, defense layer audit, bias detection
Calibration	calibration_predict calibration_resolve calibration_score	Prediction tracking with Brier scores
Learning	record_hypothesis five_whys after_action_review socratic_challenge	Root cause analysis, hypothesis testing, self-challenge
Autonomous	autonomous_scan self_diagnose predictive_prevention	Self-monitoring, proactive issue detection

Full reference: docs/TOOLS.md

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Key Capabilities

<table> <tr> <td width="50%">

Anti-Pattern Memory

Stores every mistake with root cause and fix. The LLM checks this database before acting — so it never makes the same mistake twice.

</td> <td width="50%">

Decision Council

Five adversarial perspectives review every major decision: Security, Scalability, Simplicity, User Impact, and Future Self.

</td> </tr> <tr> <td width="50%">

Confidence Calibration

Tracks prediction accuracy with Brier scores. You learn when to trust the LLM's confidence — and when to doubt it.

</td> <td width="50%">

Cross-Session Memory

Knowledge persists in a local SQLite database. Context compounds across conversations instead of resetting.

</td> </tr> <tr> <td width="50%">

FMEA Risk Analysis

Failure Mode and Effects Analysis before you build. Catch what can go wrong before it does.

</td> <td width="50%">

Custom Prevention Rules

Auto-triggered safeguards for your workflow. "Always check X before doing Y" — enforced automatically.

</td> </tr> </table>

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IDE Support

Works with any MCP-compatible client via stdio transport:

IDE	Config File
Cursor	~/.cursor/mcp.json
Claude Desktop	App Settings → MCP
VS Code + Continue	.continue/config.json
Windsurf	~/.codeium/windsurf/mcp_config.json
Antigravity	~/.gemini/config/mcp_config.json

<details> <summary><b>Manual Configuration</b></summary>

Add to your MCP config:

{
  "mcpServers": {
    "elite-reasoning": {
      "command": "bash",
      "args": ["-c", "cd ~/.elite-reasoning && uv run python -m core.integration.mcp_server"]
    }
  }
}

On Windows, use scripts/run_elite_mcp.bat instead:

{
  "mcpServers": {
    "elite-reasoning": {
      "command": "cmd",
      "args": ["/c", "%USERPROFILE%\\.elite-reasoning\\scripts\\run_elite_mcp.bat"]
    }
  }
}

</details>

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Architecture

elite-reasoning-mcp/
├── core/
│   ├── integration/
│   │   └── mcp_server.py          # FastMCP server, tool registration
│   ├── memory/
│   │   ├── persistent_store.py    # SQLite — 15 tables, 32 indexes
│   │   ├── graph_store.py         # Knowledge graph
│   │   └── embedding.py           # Semantic search (optional)
│   ├── tools/
│   │   ├── orchestration.py       # Intent classification & routing
│   │   ├── reasoning_amplifier.py # Calibration, council, preflight
│   │   ├── adaptive.py            # Learning & user modeling
│   │   ├── analysis.py            # Risk, FMEA, confidence
│   │   ├── auditing.py            # Quality & anti-patterns
│   │   └── planning.py            # Goals & benchmarks
│   └── identity/
│       └── user_profile.py        # Per-user configuration
├── schemas/                       # 66 JSON tool schemas
├── scripts/                       # Installers & launchers
└── Dockerfile                     # Container support

Full architecture docs: docs/ARCHITECTURE.md

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Performance

Metric	Value
Package size	132KB
Startup time	< 2s
Per-tool latency	< 50ms
Storage	Local SQLite
API costs	$0
Network calls	Zero

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FAQ

<details> <summary><b>Does this work with weak / open-source models?</b></summary> <br/> Yes. The pipeline runs before the model generates output, so even weaker models get structured reasoning plans, anti-pattern checks, and quality tracking. The tools amplify whatever model you're using. </details>

<details> <summary><b>Will it slow down my responses?</b></summary> <br/> No. Each tool call takes < 50ms. The orchestrator adds one tool call per prompt. Total overhead is negligible. </details>

<details> <summary><b>How is this different from sequential-thinking?</b></summary> <br/> Sequential-thinking gives the LLM a scratchpad for multi-step reasoning. Elite Reasoning MCP goes further: it classifies intent, checks past mistakes, routes to specialized tools, tracks quality over time, and builds persistent memory across sessions. They're complementary — use both. </details>

<details> <summary><b>Is my data private?</b></summary> <br/> 100%. Everything runs locally. The SQLite database is on your machine. No telemetry, no API calls, no cloud. Your code and prompts never leave your computer. </details>

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Contributing

We welcome contributions. See CONTRIBUTING.md for guidelines.

Good first issues:

• Add new reasoning tools
• Improve the intent classifier
• Write tests
• Add benchmarks for output quality with/without the pipeline

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Security

All data stays local. Zero network calls. No telemetry. See SECURITY.md for the full policy.

Found a vulnerability? Email snehgabani@users.noreply.github.com — don't open a public issue.

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License

MIT — use it however you want.

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