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Project Intelligence MCP Server

by b-bhupendra
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Project Intelligence MCP Server (Central Pinecone DB + Dual-Ollama Consensus)

A serverless-ready Model Context Protocol (MCP) platform built with FastAPI, backed by Pinecone DB as the central cloud knowledge & vector repository, designed for instant deployment on Vercel and seamless RAG analysis with Gemini Spark, Cursor, and Claude.

🏗️ Architecture Overview

 ┌────────────────────────────────────────────────────────────┐
 │ Local Workstation                                          │
 │  • Local Directory Scanner (~/Desktop, ~/projects)         │
 │  • Git Status Inspector (dirty files, unpushed commits)    │
 │  • Context Extractor (README, manifests, code entry)       │
 │                                                            │
 │  🤖 Dual-Ollama Multi-Temperature Consensus Engine        │
 │     - qwen2.5:7b   (T = 0.0, 0.5, 0.9)                     │
 │     - llama3.2:7b  (T = 0.0, 0.5, 0.9)                     │
 │     - Consensus Voter & Hallucination Filter               │
 │                                                            │
 │  • Embeddings Generator (nomic-embed-text, 768-dim)       │
 └─────────────────────────────┬──────────────────────────────┘
                               │ Upsert Vectors & Metadata
                               ▼
 ┌────────────────────────────────────────────────────────────┐
 │ Central Repository: Pinecone DB (Cloud Vector Store)       │
 │  • Dense Code & Doc Embeddings                             │
 │  • Rich Metadata (git status, priority, tech stack, branch)│
 └─────────────────────────────┬──────────────────────────────┘
                               │ Query & Metadata Filter
                               ▼
 ┌────────────────────────────────────────────────────────────┐
 │ Vercel Serverless MCP Platform                             │
 │  • FastAPI Backend (/mcp JSON-RPC 2.0 endpoint)            │
 │  • Zero-state, scale-to-zero, instant response             │
 └─────────────────────────────┬──────────────────────────────┘
                               │ MCP Protocol
                               ▼
 ┌────────────────────────────────────────────────────────────┐
 │ AI Clients (Gemini Spark, Cursor, Claude Desktop)          │
 │  • Semantic Code & Project RAG                             │
 │  • Project Prioritization & Git Sync Alerting              │
 │  • Deep Architectural Audits                               │
 └────────────────────────────────────────────────────────────┘

Related MCP server: Code Project Brain

🛠️ MCP Tools for Gemini Spark & LLMs

Tool Name

Description

Key Parameters

query_projects_rag

Semantic RAG Search: Queries Pinecone DB for relevant code snippets, architecture docs, and summaries matching natural language queries.

query (str), top_k (int), filter_synced (bool)

list_projects

Project Inventory: Lists tracked repositories with primary languages, branches, sync states, and priority scores.

dirty_only (bool), language (str), limit (int)

get_project_context

Project Dossier: Retrieves verified Dual-Ollama consensus summary, tech stack, and key files for a specific project.

project_name (str)

check_git_sync

Sync Inspector: Identifies repos with uncommitted files or unpushed commits needing backup to GitHub.

project_name (optional str)

prioritize_projects

Urgency Ranking: Ranks projects by calculated urgency (unpushed work, recent changes).

top_n (int)

analyze_project_deep_dive

Deep Multi-Chunk Analysis: Delivers multi-chunk context tailored for Gemini Spark to analyze architecture, dependencies, or recommend next steps.

project_name (str), analysis_focus (str)

🚀 Quick Start

1. Local Scanner & Dual-Ollama Consensus

Run the local scanner to evaluate your local repositories, run consensus across qwen2.5:7b & llama3.2:latest, and upsert vectors to Pinecone:

# Optional: Set your Pinecone credentials
export PINECONE_API_KEY="your-pinecone-api-key"
export PINECONE_INDEX_NAME="project-intelligence"

# Run scanner across your project directories
python scanner/scanner.py /path/to/your/projects

2. Run Local MCP Server

uvicorn api.index:app --reload --port 8000

3. Deploy to Vercel

  1. Push changes to GitHub:

    git add .
git commit -m "feat: Project Intelligence MCP with Pinecone DB & Dual-Ollama consensus"
git push origin main

  2. In Vercel Project Settings > Environment Variables:

    • PINECONE_API_KEY: Your Pinecone API key

    • PINECONE_INDEX_NAME: project-intelligence (or your chosen index name)

    • SYNC_SECRET: Secret token for scanner synchronization

🤖 Connecting to Gemini Spark / Cursor / Claude

  • Server URL: https://fastapi-mcp-server.vercel.app/mcp

  • Protocol: HTTP JSON-RPC 2.0

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