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trevorwelch

Fathom Video MCP Server

by trevorwelch

get_transcript

Retrieve the full transcript of a meeting recording with timestamped segments and speaker attribution for analysis.

Instructions

Get the full transcript for a specific meeting recording.

Returns timestamped transcript segments with speaker attribution. Each segment includes the speaker name, what they said, and when.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
recording_idYesThe recording ID of the meeting (from list_meetings)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The get_transcript tool handler: decorated with @mcp.tool, it calls the Fathom API at /recordings/{recording_id}/transcript, extracts transcript segments with speaker attribution and timestamps, and returns the structured result.
    @mcp.tool
    def get_transcript(
        recording_id: Annotated[int, "The recording ID of the meeting (from list_meetings)"],
    ) -> dict:
        """Get the full transcript for a specific meeting recording.
    
        Returns timestamped transcript segments with speaker attribution.
        Each segment includes the speaker name, what they said, and when.
        """
        data = make_request(f"/recordings/{recording_id}/transcript")
    
        transcript = data.get("transcript", [])
        segments = []
        for seg in transcript:
            segment_data = {
                "text": seg.get("text", ""),
                "timestamp": seg.get("timestamp"),
            }
            speaker = seg.get("speaker")
            if speaker:
                segment_data["speaker"] = {
                    "display_name": speaker.get("display_name", "Unknown"),
                    "email": speaker.get("matched_calendar_invitee_email"),
                }
            segments.append(segment_data)
    
        return {
            "recording_id": recording_id,
            "transcript": segments,
            "segment_count": len(segments),
        }
  • The input schema for get_transcript: takes a single recording_id (int) parameter annotated with a description for the LLM.
    def get_transcript(
        recording_id: Annotated[int, "The recording ID of the meeting (from list_meetings)"],
    ) -> dict:
  • The @mcp.tool decorator registers get_transcript as an MCP tool on the FastMCP server instance.
    @mcp.tool
  • get_transcript is exported from the package via __init__.py, making it available to consumers importing fathom_video_mcp.
    from fathom_video_mcp.server import mcp, list_meetings, get_summary, get_transcript
    
    __all__ = ["mcp", "list_meetings", "get_summary", "get_transcript", "__version__"]
  • The make_request helper function used by get_transcript to make authenticated HTTP requests to the Fathom API.
    def make_request(endpoint: str, params: dict | None = None) -> dict:
        """Make an authenticated request to the Fathom API."""
        url = f"{FATHOM_API_BASE}{endpoint}"
        headers = {"X-Api-Key": get_api_key()}
    
        transport = httpx.HTTPTransport(retries=3)
        with httpx.Client(transport=transport, timeout=30.0) as client:
            response = client.get(url, headers=headers, params=params)
            response.raise_for_status()
            return response.json()
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description effectively discloses that this is a read operation returning transcript segments with timing and speaker info. It does not mention any side effects, which is fine for a retrieval tool. Could add more about error conditions or authorization needs, but sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first states the core purpose, second describes the return format. No wasted words, well-structured and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the existence of an output schema, the description sufficiently covers the return format. It lacks a note about prerequisites (e.g., need a valid recording_id from list_meetings), but the param description hints at that. Overall, adequate for a simple retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with a clear description for recording_id. The tool description does not add extra semantic detail about the parameter; it only restates the purpose. Since schema already covers it, a score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves the full transcript for a specific meeting recording, with specifics like timestamped segments and speaker attribution. This verb-resource combination distinguishes it from siblings: list_meetings lists meetings, get_summary gets a summary.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like get_summary or list_meetings. The description does not provide context for appropriate usage scenarios or preconditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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