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list_jobs

Read-onlyIdempotent

Find previously processed recordings and their job_ids to resume analysis or triage sessions by filename, date, or duration.

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. Jobs with unreadable manifests are omitted; an empty list does not prove there are no stored sources. process_media or process_url may rebuild such a job and report manifest_recovery_note. 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 → no readable jobs; a damaged manifest may still have a source recoverable by process_media/process_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 readOnly/idempotent/non-destructive, and the description adds substantial behavior beyond that: content-addressed identity surviving renames and moves, persistence across sessions and machine restarts, that unreadable manifests are silently omitted, that an empty list is not proof of no sources, and that gc can explain a disappeared job. This is exactly the kind of non-obvious semantics annotations cannot convey.

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?

Front-loaded correctly: purpose and field list first, caveats second, usage rules third, examples last. The example list is long (eleven bullets) and a few overlap in intent ('match by filename/created' vs 'same filename -> newer created'), but each covers a distinct scenario, so the volume is defensible rather than padding.

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?

For a parameterless list tool with an output schema, the description covers the remaining risk surface: staleness/identity semantics, silent omission of broken manifests, empty-list interpretation, and the recovery path via process_media/process_url. Nothing an agent needs to call or interpret this correctly is missing.

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?

Zero parameters, so the baseline is 4; there is nothing to document. The description's field-level detail (duration, created, wall_clock.start, speakers, origin) describes the return payload rather than inputs, which is redundant with the existing output schema but harmless.

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?

Opens with a specific verb+resource and scope: 'List processed recordings, newest first', then enumerates exactly what a returned record carries (job_id, filename, duration, created, wall-clock start, counts). It also separates itself from the write-path siblings by noting that process_media/process_url are what rebuild a job, so an agent can tell 'list' from 'process' without opening a schema.

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 an explicit 'When NOT to use' section (not a health check, don't poll before every call) plus an anti-example ('checking whether a NEW file is processed -> just call process_media'). Multiple worked scenarios map user utterances to the call and name when to ask a follow-up question instead.

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