Retrieves one OVOMIND affect frame and converts it to PAD dimensions, returning each axis with its epistemic tier, provenance, and basis. Dominance values come only from non-human channels.
Poll one OVOMIND affect frame and lift it into PAD. Every axis is returned with its epistemic tier, provenance and basis string. Dominance is never estimated from the human channel.
| Name | Required | Description | Default |
|---|---|---|---|
| controllability | No |
Changes observed during successful MCP inspections.
v3.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description takes on the responsibility of behavioral disclosure. It reveals return value details ('Every axis is returned with its epistemic tier, provenance and basis string') and a key constraint ('Dominance is never estimated from the human channel'). The verb 'poll' implies a non-mutating read. However, it doesn't disclose whether the call blocks, any prerequisites, or error 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?
Three short sentences, each provides distinct information: the action, the return details, and a behavioral caveat. There is no filler or repetition; the core purpose is front-loaded in the first sentence.
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?
The tool lacks an output schema, so the description's explanation of returned fields is valuable. However, it omits the meaning of the only parameter (controllability), provides no usage context, and does not address edge cases or call semantics (e.g., blocking, defaults). This is a significant gap for a simple tool with one parameter.
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?
The schema defines one parameter, 'controllability' (number 0-1), but the schema description coverage is 0% and the description does not mention this parameter at all. An agent has no way to infer what 'controllability' controls or how it affects the read. With zero schema descriptions, the description must compensate but fails to do so.
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 uses a specific verb 'Poll' with a defined resource 'OVOMIND affect frame' and states it 'lift[s] it into PAD'. It also clarifies what is returned ('Every axis is returned with its epistemic tier, provenance and basis string'). This distinguishes it from siblings like ovo_status or ovo_cycle, though it doesn't explicitly name alternative tools.
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 phrasing 'Poll one OVOMIND affect frame' implies this is the tool to use when you need to read affect data in PAD format, but it does not provide explicit when/when-not conditions or mention alternative tools. No exclusions or conditional usage guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.