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Fluefold: Coordinated kitchen exhaust cleaning for restaurant groups

Read a requirement as it stands

get_requirement
Read-onlyIdempotent

A Fluefold requirement as it stands: each value with where it came from, what it replaced, the open question, words not yet read, whether it was sent and any offer on it.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "id": "<id>",
      "token": "<token>"
    },
    "name": "get_requirement"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesThe requirement id from an earlier answer
tokenYesIts private token (also the receipt token of the request it is sent as); keep it private

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so safety is covered. The description goes further by disclosing the composition of the returned record (provenance per value, what it replaced, open question, unread words, send status, attached offer), which is otherwise undocumented since there is no output schema.

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

Conciseness3/5

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

A single front-loaded sentence with no filler, but it is a dense list of cryptic fragments ('words not yet read', 'any offer on it') whose meaning depends on internal Fluefold vocabulary. Brevity is achieved at the cost of readability.

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?

For a two-parameter read tool with full schema coverage and complete safety annotations, the definition is mostly adequate, and it does sketch the return payload in place of a missing output schema. However, the return sketch is vague, and nothing is said about failure modes such as an invalid or mismatched token.

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?

Both parameters are fully documented in the schema (id 'from an earlier answer', token 'keep it private'), and the description adds nothing about them. With 100% schema coverage, the schema carries the parameter burden and baseline 3 applies.

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

Purpose3/5

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

The description is a noun phrase enumerating the fields of a requirement record rather than stating a verb or action; combined with the title ('Read a requirement as it stands') the intent is inferable as a fetch of the current requirement state. It never says explicitly that it retrieves anything, and it does not differentiate this read from siblings such as request_status or inspect_offer.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no mention of prerequisites, and no routing to alternatives like edit_requirement for changes or interpret_requirement for interpretation. The agent must infer the read-only use case purely from the title and annotations.

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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