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korovin-aa97

talkthrough-mcp

by korovin-aa97
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Find spoken or on-screen text in a recorded job using case-insensitive substring search. Returns source, timing, and nearest frame to jump to evidence.

Instructions

Case-insensitive substring search across BOTH transcript segments and frame OCR text. Hits carry source (transcript|ocr), t_ms, t_wall when known, the matched text, and the nearest frame position — everything needed to jump straight to evidence. Exact substring only, no embeddings. When NOT to use: fuzzy/semantic questions ("anything about performance?") — page get_transcript and read; regex is not supported. Examples:

  • search(job_id="...", query="login") — every spoken or on-screen mention of login

  • user: "what did I say about the login button?" → search(job_id, "login button") → get_moment at hits

  • search(job_id, "error") — catches the SPOKEN word and the on-screen error text (OCR) in one call

  • search(job_id, "TypeError") — stack traces on screen are OCR-indexed; great for bug repros

  • search(job_id, "€49") — prices, IDs, and literals on screen are findable via OCR

  • take hit.t_wall and grep your server logs ±30 s around it to pair remark ↔ log line

  • no hits? shorten the stem: "notif" matches notification / notifications / notify

  • prefer one distinctive word ("checkout") over a whole sentence — substrings must match exactly

  • every hit has nearest_frame_ms → get_frames(job_id, at_ms=) shows the moment

  • audio-only job → transcript hits only (there is no OCR index)

  • anti-example: "summarize the pricing discussion" → get_transcript(format="text") and read it

  • anti-example: finding an icon or layout glitch with no text → get_frames over the range; OCR sees text only

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
job_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.9/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavior: exact substring only, no embeddings, case-insensitive, audio-only jobs yield only transcript hits, and desired output fields. No contradictions.

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?

Description is thorough but slightly long. However, it is well-organized with examples and anti-examples, and front-loaded with core purpose. Every sentence adds value.

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

Completeness5/5

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

Given the tool's simplicity (2 params, output schema exists), the description covers input, output, usage context, edge cases (audio-only), and suggestions for integration.

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

Parameters5/5

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

Schema has 0% description coverage, so description compensates by explaining query as search string, job_id as job identifier, and provides concrete usage examples that illustrate parameter semantics.

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 it performs case-insensitive substring search across transcript and OCR text, and specifies what hits contain. It distinguishes from sibling tools like get_transcript and get_frames.

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?

Explicitly lists when not to use (fuzzy/semantic questions, regex) and recommends alternatives (get_transcript, read). Provides multiple examples and anti-examples with context.

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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