Consulting MCP Server

MCP server that exposes two AI pipelines — RAG Pipeline and Document Intelligence — as 8 composable tools for any MCP-compatible client.

This is the integration layer, not the intelligence layer. The intelligence lives in the pipeline repos. This server makes it consumable through a standard protocol.

Architecture

<p align="center"> <img src="docs/mcp-architecture.svg" alt="MCP Server Architecture" width="700" /> </p>

Tools

RAG Pipeline

Tool	Description
rag_query	Single-pass RAG: retrieve + generate grounded answer with citations
rag_agent_query	Multi-agent RAG for complex, multi-part questions (slower, more thorough)
rag_index	Re-index a corpus directory into the vector store (destructive)

Document Intelligence

Tool	Description
doc_classify	Classify a document by type (SOW, Contract, Project Plan, etc.)
doc_extract	Full single-doc pipeline: classify + extract structured fields + validate
doc_assess	Multi-document assessment with cross-document analysis and narrative
doc_types	List available document types and schemas (no API call)

Utility

Tool	Description
health	Server health check: API key, vector store, schemas, pipeline status

Quick Start

Prerequisites

• Python 3.12+
• Both pipeline repos cloned locally:
  • rag-pipeline
  • doc-intelligence
• ANTHROPIC_API_KEY set in environment or .env

Setup

git clone https://github.com/Brinkv3/consulting-mcp-server.git
cd consulting-mcp-server

python3.12 -m venv .venv
source .venv/bin/activate

# Install server + pipeline dependencies
pip install -r requirements.txt
pip install anthropic chromadb sentence-transformers PyMuPDF python-docx \
            python-pptx openpyxl pandas tiktoken

# Configure pipeline paths
cp .env.example .env
# Edit .env with your actual paths and API key

Connect to Claude Desktop

Copy the config into your Claude Desktop settings (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "consulting-mcp-server": {
      "command": "/path/to/consulting-mcp-server/.venv/bin/python",
      "args": ["src/server.py"],
      "cwd": "/path/to/consulting-mcp-server",
      "env": {
        "RAG_PIPELINE_PATH": "/path/to/rag-pipeline",
        "DOC_INTEL_PATH": "/path/to/doc-intelligence",
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

See config/claude_desktop_config.json for a complete example.

Connect to Claude Code

claude mcp add consulting-mcp-server \
  -e RAG_PIPELINE_PATH=/path/to/rag-pipeline \
  -e DOC_INTEL_PATH=/path/to/doc-intelligence \
  -- /path/to/consulting-mcp-server/.venv/bin/python src/server.py

Verify

Once connected, ask Claude to run health — it reports the status of each component:

Server: running
RAG pipeline: available
Doc intelligence: available
API key: set
Vector store: found
Schemas: found (6 types)

Architecture

MCP Client (Claude Desktop / Claude Code / any MCP client)
      │ (MCP protocol over stdio)
      ▼
consulting-mcp-server
  ├── server.py         → MCP server entry point, tool registration
  ├── rag_tools.py      → Tool handlers wrapping RAG pipeline
  ├── doc_tools.py      → Tool handlers wrapping doc intelligence
  └── utils.py          → Config, path validation, pipeline imports
      │                          │
      ▼                          ▼
  RAG Pipeline                Doc Intelligence
  (path-based import)         (path-based import)

Both pipelines use src/ as their package name. The server imports them sequentially, flushing sys.modules between imports to avoid namespace collisions.

Tests

source .venv/bin/activate
pytest tests/ -v

License

MIT (c) 2026 Carter Brinkley Consulting LLC