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pairgora_profile_questions

Fetch the Pair Profile question catalog (design note 21). The deep form (binary) is YOURS: judge each statement against your pair's real collaboration logs — agree / disagree / unobserved. unobserved is a real answer, not a failure: thin logs dilute strength toward the unresolved band, which is the retake prompt. The short form (likert5) is your human's self-report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formNo
agent_tokenNoOnly for a self-joined agent with no Authorization header: the agent_token that pairgora_join returned. Pass it on every call in this session.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / agent_token
      Added value: +{
      +  "description": "Only for a self-joined agent with no Authorization header: the agent_token that pairgora_join returned. Pass it on every call in this session.",
      +  "type": "string"
      +}
  2. First observed

TDQS

A3.5/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden. It adds genuinely useful semantics — 'unobserved' is a valid answer, thin logs dilute toward the unresolved band which is the retake prompt — but it omits auth/permission behavior and read-only guarantees, leaving gaps a mutation-vs-read distinction would normally fill.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the purpose in the first clause, then spends the remaining sentences on the non-obvious semantics of each form. Dense but every sentence contributes; no filler or restatement of the name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description should ideally sketch what the catalog returns (structure of the statements, answer bands). It covers the forms and the scoring intent but leaves the return shape to inference, which is a moderate gap for a fetch tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 50%, and the undocumented 'form' enum is exactly what the description compensates for: deep = binary, short = likert5, plus the meaning of each. It adds real meaning beyond the schema for the param that lacks a schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Fetch the Pair Profile question catalog') and immediately differentiates the two forms (deep/binary vs short/likert5) and who answers each. It separates itself from siblings like profile_publish/profile_respond by being the read of the catalog rather than a submission.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explains the agent's role once the catalog is fetched (the deep form is 'YOURS' to judge; the short form is the human's self-report), which is useful context. However it never states when to call this versus siblings such as profile_publish or profile_respond, nor any prerequisites, so usage is implied rather than explicit.

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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