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kaiding-ucb

podcast-summarizer-mcp

by kaiding-ucb

analyze_videos_batch_result

Poll the status of a batch video analysis job until it completes, then retrieve per-video summarization results.

Instructions

Poll the Gemini Batch API once; if done, return per-video results.

Call repeatedly until status is SUCCEEDED / FAILED / CANCELLED / EXPIRED. Recommended cadence: every 30-60s.

Args: batch_job_name: Name returned by analyze_videos_batch_start

Returns: { status, batch_job_name, results? (on SUCCEEDED), error? (on terminal failure) }

  • results: { video_id: AnalysisResult }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
batch_job_nameYes
Behavior4/5

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

Describes possible statuses (SUCCEEDED, FAILED, CANCELLED, EXPIRED) and return structure including results and error. Without annotations, it provides sufficient behavioral context for a simple poll operation.

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?

Concise yet comprehensive: one initial sentence for purpose, followed by usage instructions, parameter description, and return shape. No wasteful content.

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 all necessary aspects: polling behavior, argument source, return shape, terminal statuses, and error handling. For a tool with one parameter and no output schema, this is fully complete.

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 description adds valuable context for 'batch_job_name' by specifying it is the name returned by 'analyze_videos_batch_start', which is not evident from the schema alone (coverage 0%).

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 polls the Gemini Batch API once to check for results, and distinguishes itself from sibling tools like 'analyze_videos_batch_start' (which starts the batch) by specifying its polling nature and return of per-video results upon completion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs to call repeatedly until a terminal status, with recommended cadence of 30-60s. It also references the start tool for the argument but does not explicitly state when not to use (e.g., before starting a batch).

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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