Clipnote — turn SaaS release notes into a 60-second narrated launch video.
Scored across 2 tools
The two tools target distinct phases: draft_script handles script authoring from release notes, while video_status checks an existing render. There is no overlap in purpose or input type, so an agent can easily select the right tool.
Both names use snake_case, which is consistent. However, draft_script follows a verb_noun pattern while video_status uses noun_noun, creating a minor structural deviation from an ideal action-oriented naming scheme.
Two tools is borderline thin for a video creation service. While each tool has a clear role, the set lacks enough surface area to support the apparent domain without relying on external steps.
The core lifecycle is significantly incomplete: there is no tool to create, render, list, or cancel a video. draft_script only produces a script, and video_status only checks an existing render, leaving agents unable to complete the main video-generation workflow.
Draft a 60-second launch-video script from release notes. Free; rate-limited to 3 drafts/day per IP.
| Name | Required | Description | Default |
|---|---|---|---|
| No | |||
| product_name | Yes | ||
| release_notes | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It does disclose two genuinely useful traits — the operation is free and rate-limited to 3 drafts/day per IP — which is real value. However, it omits the mutation/creation semantics, what the call actually returns, and whether drafting is asynchronous given the video_status sibling, so the disclosure is partial.
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 short sentences, zero filler, with the core purpose front-loaded and the cost/rate-limit constraint appended. Every clause carries information an agent can use.
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 three-parameter tool with 0% schema coverage, no annotations, and no output schema, the description should explain the parameters and the likely async relationship to video_status. It covers purpose and quotas but leaves the agent guessing about inputs and return behavior.
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% across three parameters, so the description must compensate and largely does not. It implies release_notes as the source, but product_name is only hinted at and the optional email parameter is completely unexplained (purpose, whether it is for delivery or identity) in both schema and description.
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 states a specific verb (draft), a specific artifact (60-second launch-video script), and the source input (release notes), so the agent knows exactly what this produces. It does not explicitly name or contrast with its only sibling, video_status, though the distinction is implicit from the wording.
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?
Usage is implied by 'from release notes' — you bring release notes and get a script draft. There is no explicit when-to-use/when-not guidance and no mention of how this relates to the video_status sibling (e.g., whether drafting starts an async job you must poll), leaving the workflow to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check the render status of a Clipnote video by its access token.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Check' implies a read, but it never states the operation is read-only, what status values are returned, whether it must be polled, or any rate limits. For a status endpoint with zero annotation coverage, this is thin.
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?
A single efficient sentence with the verb and lookup key front-loaded and no filler. It is appropriately sized, though it could carry a bit more without becoming bloated.
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?
A one-parameter lookup with no output schema needs little, and the description covers the core action. However, since there is no output schema, an agent gets no hint about what the status response contains — the one piece of context that would most help here.
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 coverage is 0% and the single parameter has no description in the schema, so the description must compensate. 'By its access token' does clarify what the token identifies, adding real meaning, but it omits format or provenance details for that token.
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?
States a specific verb (check) and resource (render status of a Clipnote video), and names the lookup key (access token). It is clearly distinct from the sibling draft_script, though it does not explicitly call that out.
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?
Gives no when-to-use guidance, no prerequisites, and no mention of alternatives or polling behavior. An agent must infer that this is the status-polling counterpart to some render/creation tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
draft_scriptvideo_statusThe AI video studio: records your web app, writes and voices the script, renders a tutorial MP4.
The AI video studio: records your web app, writes and voices the script, renders a tutorial MP4.
Turn URLs, recordings, docs, assets, or ideas into launch films, demo recordings, and decks.
Screen recording, meeting notes, and voice dictation - all with AI
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