EntangleEQ

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

No annotations are provided, so the description carries full burden. It discloses a key behavior: the tool rejects calls (rather than guessing) if parameters are missing or mis-named. However, it does not mention authorization needs, rate limits, or whether the operation is read-only. For a readiness check, some additional context about safe usage would help.

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 a single, well-structured sentence that front-loads the purpose and adds behavioral nuance without excess. Every word earns its place, making it easy for an agent to parse quickly.

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?

The description adequately covers the tool's trigger and parameter requirements. However, without an output schema, it would benefit from explaining what the tool returns (e.g., a boolean readiness flag or detailed report). The absence of return value information leaves a gap for an agent to understand the tool's full contribution.

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 semantic value by emphasizing that 'country' and 'headcount' are required and that the tool deliberately rejects incomplete input to avoid inference. This goes beyond the schema's field descriptions and clarifies the tool's strictness.

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' and ties it to a specific workflow step (before requesting an EntangleEQ demo). It distinguishes itself from sibling tools like 'fetch' or 'request_demo_lead' by focusing on assessment rather than data retrieval or lead generation.

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 an EntangleEQ demo') and that both 'country' and 'headcount' are required. It does not explicitly list scenarios where this tool should be avoided or mention alternative siblings, but the context is clear enough for an agent to differentiate.

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 that it returns only public text and never private data, but does not mention read-only nature, side effects, authentication, or error handling. Adequate but minimal.

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, front-loaded with purpose, no redundant information. Every sentence earns its place.

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 the simple tool (1 param, no output schema, no annotations), the description is mostly complete. It tells when to use, what it returns, and what it excludes. Lacks specifics about return format but sufficient for a fetch tool.

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% for the single parameter 'id' with description. The tool description adds minimal extra meaning beyond 'Public page id returned by search.' 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?

The description clearly states the verb 'fetch', the resource 'full public text for a specific EntangleEQ page', and distinguishes from sibling tools like search. It also notes what it does not return.

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 specifies when to use ('when ChatGPT needs the full public text...') and what it does not return (private data). It implies usage after search but lacks explicit alternatives or when-not-to-use scenarios.

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, but the description discloses key behaviors: requires user consent, no sensitive data, and that the request is reviewed before follow-up. It does not mention auth or rate limits, but for a simple submission tool this is adequate.

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 the critical usage condition. No filler, 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?

With 6 parameters and no output schema, the description covers purpose, prerequisites, constraints, and workflow. It lacks details on the response or error handling, but for a simple creation tool this is largely sufficient.

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 has 100% coverage, so baseline is 3. The description adds value by emphasizing the minimum required fields and cautioning about the message field, reinforcing the schema constraints.

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's purpose: submitting demo requests after explicit user consent. It specifies the action ('records a demo request') and resource, and distinguishes it from sibling tools which are unrelated.

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 ('only after a user has explicitly agreed'), what to collect (minimum: company name, contact name, email), and what not to include (sensitive data). Also clarifies the workflow (review before follow-up).

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 are provided, so the description carries full burden. It discloses that the tool returns public pages and implies it's a read-only search. This is adequate for a simple search tool, though additional details (e.g., pagination) could enhance transparency.

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, front-loaded with the essential usage context, and every word earns its place. No redundancy or fluff.

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 the tool's simplicity (2 params, no output schema), the description is fairly complete. It covers what the tool does and when to use it. Minor details like result ordering or pagination are missing but not critical.

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 descriptions for both parameters. The description adds no additional meaning beyond the schema; thus, baseline 3 is appropriate. The schema already clearly defines the query and limit semantics.

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's purpose: to search for information about specific topics (EntangleEQ, Egapro, etc.) and return public pages. It distinguishes itself from siblings by specifying the content domain, though it doesn't explicitly contrast with each sibling.

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 says 'Use this when...' providing clear usage context with specific topics. It also notes 'Returns public pages only,' implying limitations. However, it does not mention when not to use it or alternatives, but sibling differentiation is implicit.

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 provided, the description fully carries the burden of behavioral disclosure. It explains that the tool returns the latest public Egapro declaration including global score and 5 current indicators, plus framing of the move to 7 indicators. It also clarifies data limitations and the non-legal nature of the output.

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 a single paragraph that front-loads the usage scenario and clearly states the tool's function. It is reasonably concise, though it could be slightly tighter without losing clarity. No wasted sentences.

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 the specialized nature of the tool (French gender-equality index reform), the description provides sufficient context for an agent to decide when to invoke it. It covers the input, output content, and important disclaimers. No output schema is present, but the description fills the gap by detailing the return values.

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?

The single parameter 'query' is fully described in the input schema (SIREN or company name, min 3 chars). The description repeats this information but does not add extra semantics or examples beyond what the schema already provides. With 100% schema coverage, a baseline 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?

The description clearly states the tool's purpose: to simulate the impact of the 2027 French gender-equality index reform for a French employer or advisor, or to look up a company's current published index by SIREN or company name. It differentiates from siblings by focusing specifically on the Egapro index and 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?

Explicitly states when to use the tool (when a French employer asks about the reform or wants to look up a company's index) and provides important context: it returns public data only, is an estimate for decision-support, not legal advice, and no private data.

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