fixou

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

Discloses critical behaviors: request is not published immediately, requires email confirmation (double opt-in), link expires in 48 hours, email is client's not agent's. No annotations to contradict.

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?

Description is relatively long but well-structured: purpose, caveat, email details, post-call instructions, parameter notes. Each sentence adds value; could be slightly more concise but acceptable.

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, description mentions returning requestId for use with get_request. Covers min description length, answers format from list_categories, and workflow end-to-end. Very complete.

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 coverage is 100% so baseline is 3. Description adds meaning by explaining the workflow for answers and email, e.g., 'answers reprend les id de questions et les value d options'. Adds above baseline.

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 clearly states the tool creates a quote request for Fixou on behalf of the client. It distinguishes itself from siblings by detailing the double opt-in process and referencing get_request and list_categories.

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?

Explicitly advises the agent to invite the user to check email and click the confirmation link after the call. States the agent never accesses the client space, providing clear context for when to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided, so description carries full burden. It discloses behavioral traits: never returns coordinates, explains why, and states API key scoping. Could add more about latency or potential errors, but sufficient.

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 sentences, no wasted words. First sentence conveys purpose and returned data; second adds constraints. Information is front-loaded.

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?

For a simple 1-param tool with no output schema, the description covers what the tool does, what it returns (listing fields), what it does not return, and access restrictions. No gaps.

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 covers 100% of parameters with a description for requestId. The description adds context that requestId comes from create_request, linking to sibling tool, which adds value beyond schema.

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 clearly states it checks the status of a Fixou request and artisan responses, listing specific data elements returned. It distinguishes itself from siblings create_request (creation) and list_categories (listing).

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 description explicitly states when to use (after creation) and what the tool does not return (contact details), and provides the access constraint that an API key can only query its own requests.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided, so description carries full burden. It describes the tool's output structure (metiers, families, questionnaires) and indicates it is a read operation (listing) with no side effects. However, it does not explicitly state read-only nature or mention potential limitations like pagination.

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 concise sentences, front-loaded with main action, followed by essential usage context. No redundant words; every sentence adds value.

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?

Given no annotations or output schema, the description provides thorough context: lists all professions with grouping and questionnaire, explains how to use the output in create_request, and specifies data mapping. Complete for a list tool that serves as a prerequisite.

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?

Tool has zero parameters, so baseline is 4. Description correctly omits parameter details as they are unnecessary. The schema coverage is 100% with no properties.

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 clearly states the verb 'Liste' and the resource 'tous les metiers' with grouping by family and questionnaire. It is specific and distinct from sibling tools (create_request, get_request) which have different purposes.

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?

Explicitly states 'A appeler avant create_request' and explains how the tool's output maps to create_request parameters (slug becomes categorySlug, questionnaire indicates questions). Provides direct guidance on when and how to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.