Skip to main content
Glama
korovin-aa97

talkthrough-mcp

by korovin-aa97
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_jobs

Read-onlyIdempotent

Retrieve processed recordings, newest first, with job IDs, filenames, timestamps, and durations to locate and resume prior analyses.

Instructions

List processed recordings, newest first: job_id, source filename, duration, created, wall-clock start, segment/frame counts. The store is content-addressed — the same file maps to the same job even after renames or moves, and jobs persist across sessions and machines restarts. When NOT to use: as a health check or before every call — job_ids are stable, remember them. Examples:

  • user: "triage the recording I processed this morning" → list_jobs() → pick by filename + created

  • user names neither job_id nor path → list_jobs() first; only ask if still ambiguous

  • resume yesterday's analysis in a fresh conversation → list_jobs() → reuse its job_id directly

  • file was renamed after processing → match by duration/created; the content hash ignores names

  • wall_clock.start answers "WHEN was this session?" — pick the job from "yesterday around 15:00"

  • after CLI batch pre-processing (talkthrough-mcp process big.mov) the job shows up here — query it

  • two jobs with the same filename → the newer created one is usually the re-recording

  • diarized jobs show "speakers": N — "the 4-person meeting from Tuesday" is findable at a glance

  • URL jobs carry "origin" (provider, provider_id, title) — "the YouTube video from yesterday" is findable

  • empty list → nothing processed on this machine yet; ask the user for a file path or a public URL

  • job disappeared → likely talkthrough-mcp gc cleaned it; re-run process_media on the file (same id)

  • anti-example: checking whether a NEW file is processed → just call process_media, it is idempotent+instant

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.2.4
  2. Removedv0.2.3
  3. First observedv0.1.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and the description adds behavioral context: content-addressed store (same file maps to same job despite renames), persistence across restarts, empty list semantics, and gc cleanup behavior. No contradictions with annotations.

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 front-loaded with the core purpose and output fields, then structured into 'When NOT to use' and examples. It is long but every segment adds decision value; a slight reduction in redundancy would make it tighter, yet nothing is wasted.

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?

Covers ambiguity resolution (same filename, renamed files), temporal queries, CLI interplay, URL jobs, diarization, empty results, and job disappearance. With an output schema present and no input params, this is more than enough contextual guidance for reliable invocation.

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

Parameters4/5

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

The tool takes zero parameters and the input schema coverage is trivially complete at 100%. Per the rubric, a no-parameter tool earns a baseline 4; the description also usefully explains output field semantics (wall_clock.start, origin, speakers) that help an agent interpret rows.

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 opens with 'List processed recordings, newest first' and enumerates the exact fields returned (job_id, source filename, duration, created, wall-clock start, segment/frame counts). This is a specific verb+resource that clearly distinguishes list_jobs from siblings like process_media or get_transcript.

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?

Contains a dedicated 'When NOT to use' section plus 12 examples covering triage, resume, renamed files, CLI pre-processing, duplicate filenames, and missing jobs. The anti-example explicitly routes new-file checks to process_media, leaving no ambiguity about when to call this tool vs alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools