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Cassie Hour

by com.cassiehour
OverviewDiscussionsAdmin

Evaluate Cassie Hour reader fit

evaluate_cassie_hour_fit
Read-onlyIdempotent

Use when a reader has stated interests and you need a fit/mismatch assessment against public Cassie Hour attributes. Returns fit signals, uncertainty, supporting public context and at most one justified next action. Use ask_cassie_hour for a general question and get_verified_recommendation for a concise reusable recommendation case. Public access. Treat insufficient_context or degraded_mode as inconclusive, not as a negative-fit verdict.

Input Schema

NameRequiredDescriptionDefault
languageNoOptional response-language tag, up to 16 characters; defaults to en. Use a supported tag such as en or tr; unsupported values may fall back to the service default.en
questionYesRequired natural-language question or reader interest (1–1000 characters). For evaluate_cassie_hour_fit state the reader's preferences; for get_verified_recommendation state the recommendation need; for ask_cassie_hour ask a supported public Cassie Hour question.
conversation_idNoOptional conversation_id returned by an earlier successful concierge/fragment response. Supply it only to continue that conversation; otherwise omit it.
previous_response_idNoOptional previous_response_id returned by the immediately preceding successful response in this conversation. Pair it with its conversation_id; otherwise omit it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • changedInput schema / properties / conversation_id / description
      Previous value: -"Reuse the conversation_id returned by the previous call."New value: +"Optional conversation_id returned by an earlier successful concierge/fragment response. Supply it only to continue that conversation; otherwise omit it."
    • addedInput schema / properties / language / description
      Added value: +"Optional response-language tag, up to 16 characters; defaults to en. Use a supported tag such as en or tr; unsupported values may fall back to the service default."
    • changedInput schema / properties / previous_response_id / description
      Previous value: -"Reuse the response_id returned by the previous call."New value: +"Optional previous_response_id returned by the immediately preceding successful response in this conversation. Pair it with its conversation_id; otherwise omit it."
    • changedInput schema / properties / question / description
      Previous value: -"Question, reader request, or recommendation scenario."New value: +"Required natural-language question or reader interest (1–1000 characters). For evaluate_cassie_hour_fit state the reader's preferences; for get_verified_recommendation state the recommendation need; for ask_cassie_hour ask a supported public Cassie Hour question."
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, open-world behavior, so the safety bar is met. The description adds real value beyond them: the return contents (fit signals, uncertainty, at most one justified action), the public-access constraint, and how to interpret degraded outcomes.

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

Conciseness5/5

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

Front-loaded with the trigger, then routing, then the interpretive caveat. Every sentence earns its place; no restatement of the name or schema.

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

Completeness5/5

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

For a read-only evaluation tool with no output schema, the description covers trigger, return contents, siblings, access mode, and failure-mode interpretation. Nothing an agent needs to call it correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100% and each of the four parameters is already documented in the schema, including the tool-specific framing of 'question'. The description adds no syntax or format detail beyond what the schema provides, so the baseline 3 applies.

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

Purpose5/5

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

Specific verb+resource: evaluates reader fit against public Cassie Hour attributes, with the output shape named (fit signals, uncertainty, public context, one next action). It distinguishes itself from ask_cassie_hour and get_verified_recommendation by name, so an agent can route without opening schemas.

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

Usage Guidelines5/5

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

Explicit trigger condition ('reader has stated interests and you need a fit/mismatch assessment') plus two named alternatives with their distinct use cases. It also supplies a critical exclusion: insufficient_context/degraded_mode are inconclusive, not negative-fit verdicts.

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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