get_video
One video by slug with its YouTube id; include=["content"] returns the full transcript.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| include | No |
One video by slug with its YouTube id; include=["content"] returns the full transcript.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | ||
| include | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions the optional include parameter and its effect, but does not state whether the tool is read-only, requires authentication, or any other side effects. The name implies read-only, but it's not explicit.
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?
The description is a single, front-loaded sentence of 12 words with no filler. Every part serves a purpose, stating the core functionality and the optional behavior.
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?
For a simple get-by-id tool, the description covers the main action and the optional transcript retrieval. However, it lacks definition of the slug parameter and does not describe what the tool returns (beyond the transcript aspect). With no output schema, more detail would be helpful.
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?
Schema description coverage is 0%, so the description must compensate. It explains the include parameter (enum 'content' returns transcript), but does not describe the slug parameter (what kind of slug, format?). The explanation adds some value but is incomplete.
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?
Clearly states that the tool retrieves a single video by slug and can optionally return the full transcript via the include parameter. The verb 'get' and resource 'video' are specific, and it distinguishes itself from sibling tools like 'find_videos' which likely handles listing.
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 usage when you have a video slug and need details or transcript, but it does not explicitly state when to use this tool versus alternatives (e.g., find_videos for searching). No when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes with clear descriptions, reducing ambiguity. However, some overlap exists between search tools like 'find_posts' and 'search_api_evangelist', though they target different scopes (stories vs. unified search). Overall, an agent can reasonably differentiate them.
The majority of tools follow a verb_noun pattern (e.g., find_areas, get_post), but several use noun_noun or inconsistent prefixes (e.g., api_coverage, company_gaps, insights_adoption). This inconsistency can confuse pattern recognition, though the pattern is still readable.
With 56 tools, the server is overloaded for a typical MCP context. While the domain is broad, the sheer number risks agent confusion and selection errors. Calibration suggests 25+ tools are excessive, and this server far exceeds that threshold.
The tool set covers a wide range of API governance, search, analysis, and generation tasks. There are no obvious dead ends for navigating the API Evangelist network, though some areas (e.g., direct API creation) are intentionally out of scope. Minor consolidation could improve efficiency.