Apply bulk corrections and mark words as verified on a podcast track's transcript to keep edits accurate and confirmed.
Apply bulk corrections and mark words verified on a track's transcript.
| Name | Required | Description | Default |
|---|---|---|---|
| track_id | Yes | ||
| corrections | Yes | ||
| project_path | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.1Input schema / properties / correctionsAdded value: +{
+ "items": {
+ "additionalProperties": true,
+ "type": "object"
+ },
+ "title": "Corrections",
+ "type": "array"
+}Input schema / properties / corrections_jsonRemoved value: -{
- "title": "Corrections Json",
- "type": "string"
-}Input schema / properties / project_path / anyOfAdded value: +[
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+]Input schema / properties / project_path / defaultAdded value: +nullInput schema / properties / project_path / typeRemoved value: -"string"Input schema / requiredPrevious value: -[
- "project_path",
- "track_id",
- "corrections_json"
-]New value: +[
+ "track_id",
+ "corrections"
+]v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It implies a mutation ('apply corrections', 'mark verified') but says nothing about reversibility, permissions, whether unlisted words keep their state, conflicts with existing corrections, or the shape of the result.
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?
A single well-formed sentence with no filler and the action front-loaded. It is efficient, though the brevity trades away necessary detail rather than trimming waste.
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?
An output schema exists, so returns need not be described. However, for a mutation tool with zero annotations and an opaque nested 'corrections' payload, the description is far too thin to let an agent invoke it correctly.
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?
Schema coverage is 0% and the description only echoes the parameter names implicitly ('bulk corrections', 'a track's transcript'). The critical 'corrections' parameter is an array of arbitrary objects whose item structure is entirely unexplained in both schema and description, leaving the agent unable to form valid entries.
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?
States a specific verb+resource: applying bulk corrections and marking words verified on a track's transcript. This distinguishes it somewhat from single-word tools, but it never names or contrasts with close siblings like correct_transcript_tool, find_replace_transcript_tool, or reconcile_transcript_tool, so an agent must infer the split.
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?
There is no when-to-use guidance and no mention of alternatives. Given the dense cluster of transcript-correction siblings (correct_transcript_tool, correct_transcript_phrase_tool, apply_transcript_cleanup_tool), the absence of routing cues is a real gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.