MIT
Primate Intelligence
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TDQS
A4.4/5.0
Scored across 10 tools
Disambiguation4/5
Most tools are clearly distinct: create_analysis vs create_analysis_batch, validate_analysis vs create_analysis, and get_analysis vs wait_for_analysis all have well-defined boundaries. The only potential confusion is between get_usage and get_credits, which both return credit balance, but their descriptions differentiate usage meters from transaction ledger.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with lowercase and underscores. Verbs are limited to get, create, validate, wait_for, and list, and each noun is clear. There are no mixed conventions or stylistic deviations.
Tool Count5/5
With 10 tools, the server is well-scoped for a video analysis API. Each tool serves a distinct purpose in the workflow: video ingestion, analysis creation (single/batch), validation, polling, retrieval, model listing, credit management, and test fixture access. The count feels neither sparse nor bloated.
Completeness3/5
The core workflow (create video, validate prompt, create analysis, wait for result, fetch result) is covered, including batch and dry-run operations. However, there are notable gaps: no way to retrieve or delete a video by ID, no list of analyses, no cancellation for a running analysis, and no explicit check for video readiness before analysis creation. These gaps could cause agent failures in multi-step workflows.
Maintenance
ActivitySlowing
ResponsivenessNo issues