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list_jobs

Read-onlyIdempotent

List processed recordings with job IDs, source filenames, durations, and timestamps to locate or resume prior analysis sessions, even after file renames.

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

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

  • 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

Behavior5/5

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

Annotations already declare readOnly/idempotent, but description adds valuable context: content-addressed store, job persistence across restarts, behavior for empty lists, and interactions with 'gc' cleanup. No contradiction.

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

Conciseness5/5

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

Well-structured: core description, usage warnings, and categorized examples. Every sentence/example adds value, and the structure is front-loaded with the essential list behavior.

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?

Tool is simple (no parameters) and has output schema, but description covers all essential use cases, edge cases, and behavioral nuances. Nothing important 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?

Input schema has 0 parameters, so baseline is 4. Description adds meaning by explaining output fields and their semantics (e.g., wall_clock.start meaning), satisfying the baseline.

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?

Description states 'List processed recordings, newest first' with explicit output fields (job_id, filename, duration, etc.), clearly distinguishing from sibling tools that fetch specific items rather than listing all jobs.

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?

Provides explicit 'When NOT to use' guidance (as health check or before every call) and multiple examples showing when to use, including the anti-example of using process_media for new files. This is model-level usage guidance.

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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