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calebn
by calebn

apply_transcript_cleanup_tool

Batch-correct transcript words and phrases in a single undoable step, using a corrections JSON to fix errors in the chosen track.

Instructions

Batch word + phrase fixes in one undoable history step.

JSON: {"words": [{word_index, text}], "phrases": [{start_word_index, end_word_index, text}]}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
track_idYes
project_pathYes
corrections_jsonYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the burden. It discloses that the operation is a batch fix and that it is undoable as one history step, which is useful behavioral context. However, it doesn't mention whether it overwrites existing corrections, how conflicts are handled, or what the response contains.

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

Conciseness4/5

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

The description is compact and front-loaded with the core purpose. The JSON example is useful and earns its place, though it could be slightly clearer with a brief note on how corrections_json maps to the parameter.

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

Completeness3/5

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

The tool has an output schema and only 3 parameters, so the description doesn't need to explain return values. However, with no annotations and 0% schema coverage, the description should provide more context about prerequisites (e.g., transcript must exist) and the effect of the batch operation. It's adequate but not complete.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It provides a JSON structure for corrections_json, which adds meaning for that parameter, but project_path and track_id are left entirely to the schema with no additional context. The JSON example is helpful but incomplete for full parameter understanding.

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?

The description states a specific verb ('apply') and resource ('transcript cleanup'), and clarifies it handles batch word and phrase fixes in one undoable history step. It distinguishes itself from single-fix tools like correct_transcript_tool and correct_transcript_phrase_tool by emphasizing batch and undoable history, though it doesn't explicitly name a sibling.

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 implies usage for batch word/phrase corrections and mentions the undoable history step, which suggests when to use it over single-edit tools. However, it doesn't explicitly state when not to use it or name alternatives like correct_transcript_tool or correct_transcript_phrase_tool.

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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