mcp s

The PLAUD MCP server provides access to PLAUD transcripts and notes directly to LLMs. It is designed to let Claude (or other compatible LLMs) retrieve structured transcript data and associated notes from PLAUD through a dedicated MCP endpoint. Once started, you can invoke the PLAUD MCP to query transcripts by identifier, timeframe, or keywords, and to fetch related notes that accompany those transcripts. The server acts as a bridge between your PLAUD data and the LLM, enabling contextual retrieval to improve accuracy and relevance of responses.

To use it, deploy the PLAUD MCP in your MCP-enabled environment and point your agent's tools or memory access layer to the PLAUD endpoint. Typical capabilities will include: listing available transcripts, retrieving a specific transcript's text, pulling associated notes, and filtering results by date, author, or topic. Use the MCP's standard query interface to request data, and let the agent incorporate the retrieved PLAUD content into its reasoning and responses.

Prerequisites:

• Git
• Access to the repository containing the MCP servers (this repo)
• Depending on the server implementation inside /plaud, you may need Node.js or Python installed (see the specific subdirectory for details)

Steps:

1. Clone the repository containing the MCP servers:

git clone https://github.com/your-org/audiobridge-ai-mcp-servers.git
cd audiobridge-ai-mcp-servers

2. Navigate to the PLAUD subdirectory:

cd plaud

3. Install dependencies (choose the approach that matches the server implementation in this subdirectory):

• If the PLAUD server is Node.js-based:

npm install

• If the PLAUD server is Python-based:

pip install -r requirements.txt

4. Start the PLAUD MCP server using the command that the implementation specifies (example patterns):

# Node.js example
node server.js

# Python example
python -m plaud_server

5. Verify the server is running and listening on the expected port, and note the endpoint/port to configure your MCP client.

If you do not know the exact command, check the plaud subdirectory for a README, package.json, or main script (e.g., server.js, app.py) to determine the correct startup command.

Tips:

• Ensure the PLAUD data source is accessible from the machine running the MCP server (network permissions and API keys may be required).
• Some deployments require environment variables for authentication or data paths (look for files like .env.example or environment documentation in the plaud subdirectory).
• Keep the MCP server up-to-date with the latest PLAUD data to ensure accurate retrieval of transcripts and notes.
• If you encounter endpoint timeouts, consider increasing timeout settings in your MCP client and ensuring network stability between the LLM and the PLAUD MCP server.
• When configuring tools, you may need to map the PLAUD MCP endpoints to your agent’s tool namespace; consult your MCP tooling documentation for how to register and call the new tool.