Content
# Consulting MCP Server
MCP server that exposes three AI pipelines — [RAG Pipeline](https://github.com/Brinkv3/rag-pipeline), [Document Intelligence](https://github.com/Brinkv3/doc-intelligence), and [Agentic Audit](https://github.com/Brinkv3/agentic-audit) — as 11 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) |
### Agentic Audit
| Tool | Description |
|------|-------------|
| `audit_generate_questions` | Generate interview questions from engagement documents |
| `audit_process_interview` | Process interview artifacts against a question framework (one app at a time) |
| `audit_synthesize` | Synthesize all results into an executive summary + Excel deliverable |
### Utility
| Tool | Description |
|------|-------------|
| `health` | Server health check: API key, vector store, schemas, pipeline status |
## Quick Start
### Prerequisites
- Python 3.12+
- Pipeline repos cloned locally (any subset — unavailable pipelines are skipped):
- [rag-pipeline](https://github.com/Brinkv3/rag-pipeline)
- [doc-intelligence](https://github.com/Brinkv3/doc-intelligence)
- [agentic-audit](https://github.com/Brinkv3/agentic-audit)
- LLM provider config (`LLM_PROVIDER`, `LLM_MODEL`, `LLM_API_KEY`) set in environment or `.env`
### Setup
```bash
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 "llm-adapter[anthropic] @ git+https://github.com/Brinkv3/llm-adapter.git" \
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`):
```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",
"AUDIT_PATH": "/path/to/agentic-audit",
"LLM_PROVIDER": "anthropic",
"LLM_MODEL": "claude-sonnet-4-6",
"LLM_API_KEY": "your-key-here"
}
}
}
}
```
See `config/claude_desktop_config.json` for a complete example.
### Connect to Claude Code
```bash
claude mcp add consulting-mcp-server \
-e RAG_PIPELINE_PATH=/path/to/rag-pipeline \
-e DOC_INTEL_PATH=/path/to/doc-intelligence \
-e AUDIT_PATH=/path/to/agentic-audit \
-- /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
Agentic audit: available
LLM provider: anthropic
LLM 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
├── audit_tools.py → Tool handlers wrapping agentic audit
└── utils.py → Config, path validation, pipeline imports
│ │ │
▼ ▼ ▼
RAG Pipeline Doc Intelligence Agentic Audit
(path-based import) (path-based import) (path-based import)
```
All three pipelines use `src/` as their package name. The server imports them sequentially, flushing `sys.modules` between imports to avoid namespace collisions. Each pipeline is optional — if a path isn't configured, its tools report "unavailable" and the rest of the server works normally.
## Tests
```bash
source .venv/bin/activate
pytest tests/ -v
```
## License
[MIT](LICENSE) (c) 2026 Carter Brinkley Consulting LLC
