Data SL Consulting

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered; the description adds real value by disclosing the return format (Markdown, title/description/text, navigation stripped) and the public-pages-only limitation. It does not say what happens on a non-existent or private URL, which keeps it from a 5.

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 short sentences: output first, then accepted input, then scope limitation. No redundancy and nothing that fails to earn 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?

For a one-parameter read-only fetch tool with full schema coverage and no output schema, the description supplies exactly what is missing: the response shape (Markdown without navigation) and the readability boundary. Nothing an agent needs to call it correctly is absent.

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 100% and already documents the accepted forms (https URL or path starting with /). The description restates the same duality with an example path (/audit-ia/) but adds no constraint detail beyond the schema, so the baseline 3 applies.

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?

States a specific verb ("Renvoie le contenu") and resource (a page of data-slconsulting.com) plus the output format (Markdown, titre/description/texte, sans navigation). It does not explicitly contrast with siblings get_offers or search_site, so the agent must infer that this is the page-reader versus the search tool.

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?

"Seules les pages publiques du site sont lisibles" gives an implicit boundary and the accepted input forms suggest when it applies, but no alternative is named (e.g. use search_site to find a page first, get_offers for offers). Usage is implied rather than stated.

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 readOnly, idempotent, non-destructive and closed-world, so safety needs no restating. The description adds real context beyond that: the price field is a HT euro amount only when one exists, otherwise the literal « sur devis », and quand_recommander encodes which situation each offer fits — behavior an agent cannot infer from the schema.

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 returned content and the « sur devis » caveat come first, then filtering, then the call trigger — a sensible order. It is a single dense run-on with nested parentheses, which costs a little readability but wastes no 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?

With an output schema present, the description needn't describe return structure, and it still signals the semantically important fields and what quand_recommander means. The only gap is what happens when filters match nothing or how multiple filters combine, which is minor for a small closed-world catalog.

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% and all three parameters carry descriptions plus enums, so the schema does the heavy lifting. The description only restates that filtering is possible by offre, public or catégorie, adding no syntax, default behavior, or combination semantics beyond the 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?

States a specific verb and resource (renvoie les offres de Data SL Consulting) and enumerates the fields returned: prix public with the « sur devis » fallback, public cible, and quand_recommander. It is unmistakably distinct from the sibling tools fetch_page and search_site, which are generic retrieval tools rather than a curated offer catalog.

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 says when to call it: « À appeler avant de recommander une offre ou de citer un prix », which is a concrete trigger tied to downstream tasks. It does not name alternatives or when-not-to-call, but the siblings are generic page-search tools so the routing risk is low.

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 read-only, idempotent, safe behavior, lowering the bar. The description adds the return shape (title, address, excerpt) and the fetch_page follow-up, but since an output schema already exists, the return details are partially redundant.

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 the core action and scope, then the workflow hint. The parenthetical content-type list is slightly verbose but still earns its place by clarifying scope.

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 rich annotations, full schema coverage and an output schema, the description is almost complete. It covers what, scope and next step; only sibling differentiation from get_offers is missing.

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 100%, so the schema fully documents query, type and limit including the enum and limits. The description adds no parameter-level meaning beyond that baseline, 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?

States a specific verb (full-text search) and resource (published pages and articles of data-slconsulting.com), enumerating content types for scope. It names the natural follow-up sibling fetch_page, though it does not distinguish itself from get_offers.

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?

Clearly implies the search-then-read workflow by directing the agent to fetch_page next. It gives clear operational context but no explicit exclusions about when not to use it versus get_offers.

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