get_videoFetch one commentary video: title, channel, thumbnail, UAPDrop page and YouTube URLs, plus — where this deployment allows it — our full written summary, analysis and every timestamped talking point (search results carry only a truncated preview). Raw transcripts are never returned.
| Name | Required | Description | Default |
|---|---|---|---|
| videoId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and non-destructive. The description adds meaningful behavioral context beyond that, including the conditional availability of the full summary ('where this deployment allows it') and a clear exclusion ('Raw transcripts are never returned'). This sets accurate expectations for the agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences front-load the core action and payload, then add a caveat and an exclusion. Every clause earns its place; there is no filler or repetition of structured data.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description does a strong job of enumerating the return contents and setting data-availability expectations. It is slightly incomplete because the videoId parameter semantics are left unexplained, which an agent needs to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description needed to explain the videoId parameter, but it never mentions it. The property name and the verb 'Fetch one' imply it is an identifier, but the description does not clarify whether it is a YouTube ID, an internal ID, or how to obtain it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb-resource pair ('Fetch one commentary video') and enumerates exactly what is returned: title, channel, thumbnail, UAPDrop page, YouTube URLs, and optionally the full summary, analysis, and talking points. This clearly distinguishes it from siblings like search_videos by noting that search results carry only a truncated preview.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it: when you need the full commentary details rather than the truncated preview available in search results. It does not name an alternative tool explicitly or state exclusions, but the contrast with search results provides usable context.
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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Each tool pairs a single action (get, list, search, related) with a distinct resource type, so there is no real overlap. Cross-references in descriptions also actively clarify the intended separation between search_documents, search_videos, search_semantic, and the get_video full-detail endpoint.
All tools follow a consistent lowercase snake_case verb_noun pattern: get_*, list_*, search_*, and related_*. There are no mixed conventions, vague generic verbs, or unpredictable naming styles.
Fourteen tools is well within the ideal range and each one earns its place: five getters, four search methods, three listers, and two related-content navigators. The count is broad enough to cover the research archive without becoming bloated.
The archive is retrieval-focused, and the tool surface covers all major entities: documents, news, sightings, videos, and beings, plus bidirectional related-content browsing. Search, semantic search, and source-key listing provide the discovery paths a research archive needs; no obvious dead ends or missing core operations are apparent.