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calebn
by calebn

set_transcript_vocabulary_tool

Sets podcast transcript vocabulary from terms and guest names to guide future transcription prompts. Requires a matching revision; stale writes are rejected to protect show context.

Instructions

Replace vocabulary using the revision returned by get_transcript_vocabulary_tool.

Pass null only when that read returned a null revision. A stale revision rejects the write; the existing show title and other transcript context stay unchanged. This changes future transcription prompts, not existing transcript words.

Input Schema

NameRequiredDescriptionDefault
termsYes
guest_namesYes
project_pathNo
base_revisionYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.1

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses optimistic-concurrency behavior ('a stale revision rejects the write'), the blast radius ('existing show title and other transcript context stay unchanged'), and the scope of effect ('changes future transcription prompts, not existing transcript words'). It omits auth/permission requirements and rate or size limits.

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?

Three short sentences, each front-loading the operative constraint: the revision source, the null exception, and the scope of the change. No filler or restatement of the tool name.

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?

An output schema exists, so return values need not be described. For a mutation tool with zero annotations, the description covers the essential behavioral facts (stale rejection, what is and isn't affected); the remaining gap is parameter-level detail for terms/guest_names and any permission requirements.

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 0% for 4 parameters, so the description must compensate. It explains base_revision thoroughly (source, null condition, staleness), but terms, guest_names, and project_path get no semantic elaboration at all, leaving three of four parameters undocumented.

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

Purpose4/5

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

States a specific verb and resource ('Replace vocabulary') and explicitly ties it to the paired read tool get_transcript_vocabulary_tool, so an agent can tell the write apart from the read. It does not spell out what the vocabulary contains (terms vs guest names), but the verb+resource pairing is unambiguous.

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

Usage Guidelines5/5

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

Explicit sequencing: the revision must come from get_transcript_vocabulary_tool, and null is permitted only when that read returned a null revision. The 'when-not' case (stale revision) is stated with its consequence, leaving no inference to the agent.

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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