MCP Knowledge Base Server
This folder contains the MCP server and unstructured-text ingestion pipeline for the support-ticket triage demo.
The server exposes knowledge-base articles and sample tickets through MCP tools. The search tool is backed by a local SQLite vector index built from Markdown files in data/kb/.
Files
mcp/
├── main.py # FastMCP server
├── pipeline.py # Ingest, chunk, embed, and store KB documents
├── vector_store.py # SQLite vector search helpers
├── models.py # Pydantic models returned by tools
└── data/
├── kb/ # Source knowledge-base articles
├── tickets/ # Sample support tickets
└── index.db # Generated SQLite indexTools
The MCP server currently exposes:
search_kb(query: str) -> list[Chunk]
Semantic search over indexed KB chunks.
get_article(name: str) -> str
Fetch a full KB article by filename.
list_tickets(status: str) -> list[Ticket]
List sample tickets by status.
Setup
Install dependencies:
uv syncSet the Gemini API key used for embeddings:
export GEMINI_API_KEY="..."Build The Index
Run the ingestion pipeline:
uv run python pipeline.pyThe pipeline:
- Reads Markdown files from
data/kb/. - Normalizes and chunks each document.
- Creates embeddings with
gemini-embedding-001. - Stores chunks and embeddings in
data/index.db. - Uses SHA-256 hashes to skip unchanged documents on reruns.
Run The Server
Start the MCP server over stdio:
uv run python main.pyMost clients, including the ADK agent in ../agent, launch this command as a subprocess instead of running it manually.
Client Config Snippet
Example stdio client configuration:
{
"mcpServers": {
"kb-server": {
"command": "/Users/vianel/Workspace/samples/mcp/.venv/bin/python",
"args": ["/Users/vianel/Workspace/samples/mcp/main.py"],
"env": {
"GEMINI_API_KEY": "${GEMINI_API_KEY}"
}
}
}
}Smoke Tests
Rebuild the index and check that reruns skip unchanged files:
uv run python pipeline.py
uv run python pipeline.pyThen run the agent-side discovery script from ../agent:
cd ../agent
uv run python discovery.pyYou should see the MCP tools discovered by the client.
