The full transcript of a meeting, with who said each line. Speaker attribution here is not a guess from a mixed audio stream — each microphone is transcribed separately, so the name on a line is the connection the words arrived on.
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | Only lines after this epoch-ms timestamp. | |
| meeting | Yes | The meeting code, like abc-defg-hij. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is crucial for transparency. It discloses the critical behavioral trait that speaker attribution is high-fidelity ('not a guess from a mixed audio stream'), managing the agent's expectations regarding data accuracy.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief, using two highly effective sentences. The primary value proposition (high-quality attribution) is front-loaded and immediately evident.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the small parameter set (2) and the complete schema coverage, the description adds exactly the right amount of high-value behavioral context regarding data reliability, making it fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Since the schema coverage is 100%, the parameter definitions already cover the schema requirements. The description does not add significant semantic value beyond the basic definitions of the meeting code or the since timestamp.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The tool purpose is stated explicitly: retrieving 'The full transcript of a meeting, with who said each line.' This clearly distinguishes it from sibling tools like get_notes or get_task.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
While it doesn't explicitly state 'when' to use it versus other tools, the description strongly implies its use case is for high-fidelity transcripts due to its detailed explanation of speaker attribution quality.
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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