EntangleEQ

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

With no annotations, the description discloses a key behavioral trait: the tool rejects calls if required params are missing or mis-named (rather than guessing). This compensates for the lack of structured safety hints. Further details on permissions or idempotency are absent but acceptable for this type.

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?

The description is two sentences with no wasted words. The first sentence gives purpose and usage; the second adds behavioral nuance. Ideal length and front-loading for quick agent parsing.

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 6 parameters, no output schema, and no annotations, the description covers purpose, conditions of use, and key behavioral constraint. It does not describe return values, but the tool name and context imply a readiness assessment outcome, which is likely sufficient for correct invocation.

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. The description adds marginal value by emphasizing that country and headcount are required and that missing params cause rejection, but does not elaborate on the meaning of optional boolean parameters beyond what schema already provides.

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 performs a 'first-pass readiness check' for 'French/EU pay transparency obligations' specifically 'before requesting an EntangleEQ demo'. This precise verb-resource combination effectively distinguishes it from sibling tools like 'request_demo_lead' or 'search'.

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 when to use ('when a user wants a first-pass readiness check before requesting a demo') and clarifies required parameters and rejection behavior. Lacks explicit 'when not to use' guidance, but context is sufficient.

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?

With no annotations, the description carries the burden. It discloses that the tool does not return private data, which is a key behavioral trait. However, it omits details on error handling, response format, or any side effects.

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 with no redundancy. Every word adds value, front-loading the purpose and usage condition.

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 single-param tool with no output schema, the description covers purpose and a security constraint but does not describe the return value (e.g., the text content) or error behavior, leaving some ambiguity.

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% with a clear description for the only parameter 'id'. The description reiterates that the id is from search, adding no new meaning. Baseline 3 is appropriate since schema already suffices.

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 fetches full public text for a specific page by ID, distinguishing it from search which returns page references. The verb 'fetch' and resource 'EntangleEQ page' are specific.

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 provides a usage condition ('when ChatGPT needs the full public text for a specific EntangleEQ page returned by search') but lacks explicit when-not-to-use guidance or alternatives among siblings. It does state what it never returns, which is helpful.

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 exist, so description carries behavioral burden. It notes that the team reviews before follow-up and requires userConsent=true, but lacks details on success/failure responses, rate limits, or idempotency.

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 sentences, front-loaded with critical usage condition, then purpose and data guidelines. No redundant information.

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 and no annotations, description covers usage context, parameter guidance, and behavioral constraints. Missing post-call behavior (e.g., confirmation) but adequate for agent decision-making.

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%, providing baseline 3. Description adds value by emphasizing minimal data collection and warning against sensitive data in the message field, going beyond schema descriptions.

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 is for submitting a demo request after explicit user agreement, and it distinguishes itself from sibling tools like assess_readiness or search by being a submission action.

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?

It provides explicit condition ('ONLY after a user has explicitly agreed') and guidance on data collection, but does not mention when not to use or alternatives, though sibling list implies differentiation.

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; description adds a key behavioral trait: 'Returns public pages only.' However, it does not explicitly state it is read-only or disclose other behaviors like error handling or rate limits.

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?

Extremely concise: two sentences with zero waste. Front-loaded with usage guidance, no unnecessary details.

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?

Lacks output specification; does not describe the structure of returned results (e.g., titles, URLs). For a search tool without output schema, more detail is needed for an agent to understand the return format.

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 both parameters with descriptions (100% coverage). The description does not add extra meaning beyond the schema, so baseline score of 3 is appropriate.

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?

Description clearly states the tool searches public pages about specific topics like EntangleEQ, Egapro, etc. It distinguishes from siblings by specifying the scope of public product information, but does not explicitly differentiate from other 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?

Explicitly states when to use: when a ChatGPT user asks about a list of topics. Provides clear context but does not mention when not to use or offer alternatives.

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 the description carries full burden. It fully discloses what the tool returns (global score, 5 current indicators, framing of 7 indicators), data source (public only), and limitations (estimate, not legal advice). There is no contradiction.

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 well-structured with a clear front-loaded purpose sentence. It is somewhat lengthy but every sentence provides necessary context. Minor redundancy (e.g., 'Public data only' is repeated) but overall efficient.

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 single-parameter tool with no output schema, the description covers all essential aspects: input format, output content, data scope, and usage limitations. It is complete given the tool's simplicity.

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% with a description on the single parameter. The description reinforces that the query can be a SIREN or company name and gives length constraints, but adds little beyond the schema. Baseline 3 is appropriate.

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?

Description clearly states the tool's purpose: to retrieve French Egapro index data by SIREN or company name. It uses specific verbs ('simulate', 'look up') and specifies the resource (public Egapro declarations). The tool is differentiated from siblings by its narrow focus on the gender-equality index reform.

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?

Explicit usage trigger is provided ('when a French employer or advisor asks about the reform or wants to look up a company's index'). The description notes public data only, which helps set expectations. However, it does not explicitly state when not to use this tool versus alternatives like 'search'.

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