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trevorwelch

Fathom Video MCP Server

by trevorwelch

get_summary

Obtain the AI-generated summary of a meeting recording, delivered as markdown with key points and discussion topics.

Instructions

Get the AI-generated summary for a specific meeting recording.

Returns a markdown-formatted summary of the meeting including key points and discussion topics.

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 actual tool handler for get_summary. It takes a recording_id, calls the Fathom API endpoint /recordings/{recording_id}/summary, and returns the AI-generated summary (template_name and markdown_formatted) or an error if none is available.
    @mcp.tool
    def get_summary(
        recording_id: Annotated[int, "The recording ID of the meeting (from list_meetings)"],
    ) -> dict:
        """Get the AI-generated summary for a specific meeting recording.
    
        Returns a markdown-formatted summary of the meeting including key points
        and discussion topics.
        """
        data = make_request(f"/recordings/{recording_id}/summary")
    
        summary = data.get("summary")
        if summary:
            return {
                "recording_id": recording_id,
                "template_name": summary.get("template_name"),
                "markdown_formatted": summary.get("markdown_formatted"),
            }
    
        return {
            "recording_id": recording_id,
            "error": "No summary available for this recording",
        }
  • The @mcp.tool decorator registers this function as an MCP tool named 'get_summary'.
    @mcp.tool
  • Exports get_summary in the package's __all__ and imports it from server.py for public API access.
    from fathom_video_mcp.server import mcp, list_meetings, get_summary, get_transcript
    
    __all__ = ["mcp", "list_meetings", "get_summary", "get_transcript", "__version__"]
Behavior3/5

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

With no annotations provided, the description carries full burden. It states the tool returns a markdown summary, but does not disclose potential latency, idempotency, or prerequisites like the meeting must be processed. The behavior is partially transparent but lacks details about side effects or error conditions.

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?

The description is two sentences: first sentence states the core function, second sentence explains the output. No unnecessary words. Front-loaded with the essential verb+resource.

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 low complexity (1 parameter, output schema exists), the description is largely sufficient. It covers purpose, output format, and parameter source. However, it omits error handling or prerequisites for generating the summary, which could help completeness.

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?

Schema description coverage is 100% (parameter described as 'The recording ID of the meeting (from list_meetings)'). The tool description adds no extra parameter details beyond the schema. Baseline 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 the tool's purpose: 'Get the AI-generated summary for a specific meeting recording.' It specifies the resource (summary) and indicates the output format (markdown) and content (key points, discussion topics). This distinguishes it from siblings like get_transcript and list_meetings.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives. It implies that the recording_id should come from list_meetings (from the parameter description), but there is no guidance on when not to use it or what conditions must be met (e.g., meeting must have summary generated).

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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