Compute annotated media trajectories to identify weak spots in how audience emotions evolve during a creative work. Uses time-ordered cue annotations, not automatic media analysis.
Compute annotated media trajectories and explain weak spots. Does not infer media cues or claim population accuracy.
| Name | Required | Description | Default |
|---|---|---|---|
| options | No | ||
| document | Yes |
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, covering safety. The description adds meaningful scope limitations—it does not infer media cues or claim population accuracy—which prevent over-trust in the output. It stops short of describing return format or computational behavior.
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 with zero filler, and the core purpose is front-loaded before the limitation. It is efficiently structured, though extremely terse for a tool with two nested object parameters.
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?
With no output schema, 0% schema description coverage, and two nested object parameters, the description should do more to explain inputs, return values, or usage context. It conveys only a high-level purpose and two scope exclusions, leaving significant gaps for correct invocation.
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%, and both parameters ('document' and 'options') are nested objects with no field descriptions. The description says nothing about what these parameters should contain, so it fails to compensate for the schema gap.
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 ('Compute') and resource ('annotated media trajectories'), plus a secondary action ('explain weak spots'). However, the domain jargon is vague without further context, and it does not distinguish this tool from siblings like ap_compare, ap_sensitivity, or ap_fit_calibration.
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?
It provides a negative guideline ('Does not infer media cues or claim population accuracy'), which helps bound expectations. But it never states when to use this tool versus alternatives, nor does it name any sibling tool or prerequisite.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.