Teleport Documentation

Search and query Teleport's documentation using embeddings stored in a local Chroma vector database.

Build a MCP server for Teleport Documentation

Embeddings generated fromteleport docsare saved in a Chroma database. A MCP tool is provided to do the vector search and return the result from the database. Note that no LLM model is used to interpret the result within the MCP tool. It's up to the AI tool that calls the MCP tool to interpret the result.

https://hub.docker.com/r/stevetelelport/teleport-docs-mcp

docker run --rm -i stevetelelport/teleport-docs-mcp:v0.1.0

{ "mcpServers": { "teleport-docs": { "command": "docker", "args": [ "run", "--rm", "-i", "stevetelelport/teleport-docs-mcp:v0.1.0" ] } } }

note that it takes a few seconds to spin up.

docker run -d --name teleport-docs-mcp-sse -p 8282:8000 stevetelelport/teleport-docs-mcp:v0.1.0 uv run main.py --sse --host 0.0.0.0

curl -Ls https://astral.sh/uv/install.sh | sh

$ docker run --name teleport-docs-mcp-sse -d -p 8282:8000 teleport-docs uv main.py --sse --host 0.0.0.0

{ "mcpServers": { "teleport-docs": { "command": "uv", "args": [ "--directory", "/path/to/teleport-docs-mcp", "run", "main.py" ] } } }

The vector database is prepopulated and provided with this repo. You can refresh the data by removing existing indexes, and copy the latest pages from theteleportOSS GitHub repo.

rm -rf docs/pages rm -rf docs/pages_fixed cp /path/to/teleport/docs/pages docs/pages cp /path/to/teleport/examples docs/examples python3 fix_include.py

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