outils

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

Annotations already declare the tool read-only, non-destructive, and closed-world, so the baseline safety profile is known. The description adds useful context about the possible levels of aggregation (national, sector, top sectors) but does not disclose output format, pagination, or other behavioral details. This is adequate given the annotations but not rich.

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?

A single, compact sentence that front-loads the tool's identity and immediately lists the available scopes. No filler or redundant phrasing.

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 one-parameter read-only tool, the description covers the core purpose and scope options but leaves the exact parameter encoding (e.g., what value triggers 'top sectors') and expected output format unspecified. Since there is no output schema, more explicit parameter usage would improve completeness.

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 schema provides no description for the 'secteur' parameter (0% coverage), so the description must compensate. It does imply that the parameter selects between national, sector, or top exposed sectors, but it does not specify how to encode 'top sectors' or whether empty means national. This partial guidance earns a mid-range score.

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 identifies a specific resource ('Barometre d'exposition structurelle cle-personne') and its scope options (national, sector, top sectors), sourced from Insee 2023. However, it lacks an explicit verb and does not contrast with sibling tools such as benchmark_sectoriel, so it is clear but not strongly differentiated.

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?

No guidance is provided on when to use this tool versus the sibling tools. It does not mention any conditions, alternatives, or exclusions, leaving the agent to infer the context from the name and description.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds behavioral context by specifying the output includes median and 75th percentile, and that score_global can be used to situate a score. It does not describe side effects or error conditions, but given the read-only nature, that is acceptable. It adds some value beyond annotations without 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?

The description is two short sentences, front-loaded with the core purpose and followed by an optional parameter hint. Every word adds value; there is no fluff or redundancy. It is efficient and well-structured for quick 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?

With no output schema, the description gives the key return metrics (median, 75th percentile) but does not specify the exact response structure (e.g., object fields, nesting). It also does not clarify the expected format of 'secteur', which is a required parameter. Given the tool's simplicity, this is a minor gap, but for a complete agent experience, more detail on input format and output shape would be helpful.

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 description coverage is 0%, so the description must compensate. It explains the purpose of score_global (to position a score) but provides no details about the 'secteur' parameter—such as accepted values, format, or examples. There is no guidance on how to specify a sector (e.g., industry name, code). This leaves a significant gap for agents to know how to fill the required parameter correctly.

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 provides sector benchmarks with specific metrics (active patents, R&D, global score) and statistical values (median, 75th percentile). It is a noun phrase but unambiguous in intent. While it doesn't explicitly say 'retrieve' or 'get', the purpose is evident. It doesn't explicitly differentiate from siblings, but sibling names (e.g., identite_operateur, statut_services) are clearly different domains, so no confusion.

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 no guidance on when to use this tool versus alternatives. It mentions an optional parameter (score_global) for positioning a score, which is a parameter-level hint, but does not state conditions like 'use when sector benchmarks are needed' or exclude any scenarios. There is no mention of alternative tools or when not to use it.

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?

Annotations already indicate readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds that the search operates on a 'public index', implying no authentication is needed. It does not disclose rate limits, pagination, or response behavior, but the read-only nature is consistent and the added public-access context provides some value beyond annotations.

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, focused sentence that states the core action and scope immediately. There is no redundant information or filler; every word contributes to understanding what the tool does.

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 tool with no output schema and minimal schema descriptions, the description is incomplete. It does not explain how to construct a query, what the 'limite' parameter controls, or what kind of results to expect. An agent would need to guess important invocation details, making this insufficient for reliable tool selection and use.

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 description coverage is 0%, so the description must compensate for parameter meaning. It does not explain 'requete' (query) or 'limite' (limit) at all. The description only describes the overall search action, leaving parameter semantics entirely to inference from names.

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: 'Recherche dans l'index public des pages de ScoreInnov.com' – a specific verb (recherche) and resource (index public). It also lists content areas, making the scope understandable. However, it does not explicitly differentiate from sibling tools like barometre_cle_personne or benchmark_sectoriel, though the general search nature is implicitly distinct.

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 gives no guidance on when to use this tool versus its siblings. There are no when/when-not conditions, no mention of alternatives, and no context about prerequisites. An agent would have to infer usage from the name and description alone.

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?

Annotations already mark the operation as read-only and non-destructive. The description adds useful behavioral context beyond that: it lists the returned claims and explains the no-token fallback behavior. It does not disclose error handling for invalid/expired tokens, but the annotations lower the burden.

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 compact, front-loaded with the core action, and every part adds value: what it returns, why it is useful, and what happens without a token. There is no filler or repetition of schema/annotation data.

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 zero-parameter, read-only tool with no output schema, the description covers the main needs: the returned claims are enumerated, and the no-token case is addressed. It could add how the token is supplied or what invalid-token responses look like, but these are minor 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?

The input schema has zero parameters, so the description does not need to explain parameter behavior. The phrase 'jeton OAuth fourni' implies the token comes from the request context rather than a parameter, which is consistent with the empty schema. Baseline 4 applies for a zero-parameter tool.

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 a specific verb and resource: it returns the claims of the provided OAuth token and lists the key fields (sub, scope, audience, expiration). It is distinct from sibling tools by its explicit focus on OAuth token claims, though it does not explicitly contrast itself with identite_operateur.

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 gives a clear use case: allowing a client to verify its authorization, and it explains the behavior when no token is present (indicates how to obtain one). It does not explicitly mention when not to use it or name alternatives, so it stops short of full routing guidance.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds that the data is 'public' but does not disclose additional behavioral traits such as authentication needs, rate limits, or response format. It is consistent with annotations and adds minimal behavioral context.

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 sentence that front-loads the core subject ('Identite publique') and then adds purpose. It contains no filler or redundancy, making it highly concise and well-structured.

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 lookup tool with no parameters and no output schema, the description adequately conveys what data is returned and why it is useful. It could be slightly more explicit about the output format, but the provided details are 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?

The tool has zero parameters, so the input schema fully covers the inputs (trivially). The baseline is 4 for 0-parameter tools, and the description does not need to add parameter information. It does not provide any input-related detail, which 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 identifies the resource (the site publisher's public identity) and lists specific data fields (TENDIL COURTAGE, SIREN, ORIAS). It distinguishes itself from sibling tools by topic, but it lacks an explicit action verb like 'returns' or 'provides', which is a minor deduction.

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 gives a clear use case: verifying that the site is not fraudulent. It implies when to use the tool, but it does not explicitly mention alternatives or when not to use it. This is clear context without exclusions.

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?

Annotations already mark the tool as read-only and non-destructive. The description adds useful boundary context, clarifying that the tool returns a scope statement about ScoreInnov.com rather than an audit or legal judgment. It does not describe the exact return shape, but for a static, no-input informational tool the transparency 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?

A single dense sentence front-loads the core subject, enumerates specific examples in a compact parenthetical, and ends with a clear usage hook. No words are wasted and the structure is easy to scan.

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 zero-parameter, low-complexity informational tool, the description explains what it is, what it is not, and when to use it. It does not specify the output format, and no output schema exists, but the remaining ambiguity is low for invoking this tool correctly.

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 input schema has zero parameters and full coverage, so the baseline is 4. There are no parameter meanings to explain, and the description appropriately focuses on the tool's purpose rather than inputs.

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 subject: what ScoreInnov.com does ('test gratuit', 'scanner SIREN') and does not do ('pas un audit', 'pas de conseil juridique'). This is specific enough to distinguish it from the sibling data-analysis tools, though it lacks a direct imperative verb and relies on the name for context.

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 phrase 'a utiliser pour toute question de confiance' is an explicit usage trigger, and the exclusions 'pas un audit, pas de conseil juridique' indicate when this tool should not be relied upon. It does not name alternative sibling tools, but for a no-parameter informational tool this is a minor gap.

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