Flamel Public Content

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable context beyond annotations: it specifies that only 'published' posts are returned and that the output is Markdown, which are behavioral details not present in the 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, well-structured sentence that immediately states the action, resource, format, and key parameter. There is no redundancy or filler; every word contributes to the meaning.

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 tool with one parameter, strong safety annotations, and no output schema, the description fully covers the essential information: what the tool does, what it returns, and how to specify the post. It clearly states the return format (Markdown) and the constraint (published), making it self-sufficient for an agent to invoke 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 100% description coverage, with the slug parameter fully explained via an example and a field description. The tool description reinforces 'by slug' but does not add additional semantics beyond what the schema already provides, so the 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 uses a specific verb 'Fetch' and clearly identifies the resource: the full body of one published Flamel blog post as Markdown, keyed by slug. This distinguishes it from sibling tools like list_blog_posts (which list posts) and other get_* tools for different content types.

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 implies usage context by stating 'by slug' and 'full body... as Markdown', which tells the agent when to use this tool (when you need the complete content of a single post and have its slug). However, it does not explicitly name alternatives or state when not to use it, so it falls short of the highest score.

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 idempotentHint=true, so the safety profile is clear. The description adds that the tool fetches by slug and returns specific content sections, but it does not disclose additional behavioral details such as error handling, return format, or potential side effects beyond what annotations cover.

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, front-loaded sentence that immediately communicates the action and resource. It has no unnecessary words or repetition, making it highly 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?

Given the tool's simplicity (one parameter, clear annotations, no output schema), the description is complete. It specifies the resource and the type of content returned, which is sufficient for an agent to select and invoke the tool correctly without 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?

The schema covers the slug parameter 100%, including its meaning and format. The description only repeats 'by slug' without adding any new semantic information, so it does not go beyond what the 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's action ('Fetch') and resource ('one Flamel case study by slug'), and specifies the content returned (challenge, solution, results, customer quotes). This distinguishes it from list_case_studies and other sibling 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?

The description implies the tool is for retrieving a specific case study when the slug is known, which is a clear usage context. It does not explicitly mention when not to use it or provide alternative tool names, but the context signals and sibling list make the intended use evident.

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 the tool as read-only, idempotent, and non-destructive. The description adds value by detailing the content breakdown of the overview, which helps the agent set expectations for the response. No contradiction with 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?

Two sentences, no fluff: the first lists content areas, the second gives usage guidance. Every word earns its place, and key 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, zero-parameter tool with no output schema, the description fully specifies what the tool returns (product, audience, solutions, links, contact/trial) and when to use it. This is complete for the tool's complexity.

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 schema description coverage is trivially 100%. The baseline for 0-param tools is 4, and the description need not explain parameter 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 retrieves a structured overview of Flamel, enumerating specific content areas (product, audience, solution areas, links, contact/trial info). This distinguishes it from sibling tools like get_blog_post or list_faqs, which cover specific content types.

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 'Start here if you know nothing about Flamel' gives clear contextual guidance for when to use this tool. It does not explicitly name exclusions or alternatives, but the provided context effectively frames this as the entry-point overview tool.

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, idempotentHint=true, and destructiveHint=false, which fully cover the safety profile. The description adds no behavioral traits beyond the filtering relationship, which is more about purpose than behavior. With annotations doing the heavy lifting, 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?

One sentence, no fluff, and the key point (categories for filtering) is front-loaded. The description is optimally concise for the tool's simplicity.

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 parameters, no output schema, and strong annotations, the description fully covers what an agent needs to know: what the tool returns (categories) and why it's useful (filtering `list_blog_posts`). The sibling context confirms this is a simple lookup 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?

The tool has zero parameters, so the schema is trivially complete (100% coverage). The description adds no parameter-specific details because none exist. Baseline 4 is appropriate for a zero-parameter tool where the description explains its role.

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 lists blog categories and explicitly ties this to filtering `list_blog_posts`, distinguishing it from sibling tools that list other resources. It uses a specific verb ('List') and resource ('blog categories'), making the purpose unambiguous.

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 references `list_blog_posts` as the consumer of these categories, providing clear context for when to use this tool. It doesn't explicitly mention exclusions or alternatives, but the purpose is self-evident as a helper for a specific filter, which suffices.

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, idempotentHint, and destructiveHint. The description adds valuable behavioral context beyond those: it returns only published posts, orders newest first, and specifies the exact output fields (titles, URLs, summaries, publish dates) while excluding full bodies. No contradictions with 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?

Two concise sentences, front-loaded with the core action and resource, followed by key details about filtering and return content. Every sentence earns its place with no redundancy.

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 is a simple list operation with three optional parameters and no output schema, the description covers all essential aspects: published scope, ordering, optional category filter, and return fields (including what is NOT returned). It is fully adequate for an agent to select and invoke the 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?

Schema description coverage is 100% with all three parameters (page, limit, category) well-explained in the schema. The description only restates the optional category filter and adds ordering context, which does not meaningfully enhance parameter understanding 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?

The description clearly states the tool 'List published Flamel blog posts, newest first', with a specific verb and resource. It distinguishes from sibling tools like list_case_studies and get_blog_post by specifying blog posts and clarifying it returns summaries, not full bodies.

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 clear context: when you want a paginated list of published posts with metadata, optionally filtered by category. The phrase 'not full bodies' implies a limitation, hinting at using get_blog_post for full content, but it does not explicitly name alternatives or 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 declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds that only 'published' case studies are returned and that output includes 'headline results,' offering useful behavioral context beyond annotations. However, it does not mention pagination, ordering, or return format.

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, direct sentence that front-loads the action and resource, with no verbose or redundant wording. It is appropriately sized for the tool's simplicity.

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 no-parameter list tool, the description covers the core purpose and hints at the output content (headline results). It lacks details on pagination or sorting, but given the low complexity, it is reasonably complete. A more explicit return structure would have been helpful since no output schema exists, but the current level is adequate.

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 schema provides full coverage and no parameter documentation is needed. The description correctly focuses on the purpose rather than parameters, which is appropriate for a parameterless 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 the action ('List') and the resource ('published Flamel customer case studies') with an additional detail ('with their headline results') that distinguishes it from sibling tools like 'get_case_study' (single item) and 'list_testimonials' (different content type).

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 implies its usage (to retrieve all published case studies) but provides no explicit comparison with alternatives such as 'get_case_study' or 'search_flamel_content'. There is no mention of when not to use it or which sibling to prefer for specific needs.

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 description's addition of 'published' as a filter clarifies the data scope (excludes drafts). It does not disclose return format or pagination but adds 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?

One sentence listing the resource and topics, plus a short usage note. No wasted words.

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 list operation with two optional parameters and no output schema, the description adequately conveys what will be returned (a list of published FAQs) and when to use it. It lacks for example the response shape, but that is less critical 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?

The schema describes both 'limit' and 'category' with 100% coverage, and the description adds no additional parameter-level detail. The description's mention of categories (pricing, onboarding, etc.) implies possible values for the category parameter, but the schema already covers it.

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 uses the specific verb 'List' with resource 'published Flamel FAQ entries' and enumerates covered topics (platform capabilities, pricing, onboarding, integrations, security), clearly distinguishing from sibling tools like blog posts or case studies.

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 states 'Good for direct factual questions about the product' which provides a clear use-case context. It does not explicitly exclude alternatives or name sibling tools, but the context is sufficient to know when 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 declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds the 'published' filter and mentions return fields, which is useful but not extensive. It does not disclose ordering, pagination, or any side effects, which keeps it at a moderate level.

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, efficient sentence that clearly states the purpose and key details without redundancy. It is front-loaded and every word contributes to understanding.

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 read-only list tool, the description combined with the schema and annotations provides sufficient context. It mentions return fields, and the schema covers defaults and filters. It lacks explicit return envelope or error handling, but given the low complexity, it is nearly 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?

The input schema provides 100% coverage with descriptions for both parameters (limit and featured). The description adds no additional parameter semantics beyond what is already in the schema, matching the baseline for high schema coverage.

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 tool as listing published customer testimonials, specifying the resource (testimonials), the action (list), and the key fields returned (author, title, company). This distinguishes it from sibling tools like list_blog_posts or list_case_studies.

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 implies when to use the tool (when you need to retrieve testimonials) and the 'published' filter provides contextual guidance. However, it does not explicitly mention alternatives or exclusion criteria, such as using search_flamel_content for broader queries, so it falls short of a 5.

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 the tool as read-only, idempotent, and non-destructive. The description adds the behavioral scope of searching across multiple content types, but it does not disclose output format, result grouping, or limit behavior beyond what the schema already provides. This is consistent with annotations and adds moderate 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?

Two sentences, front-loaded with the action and scope, followed by a brief, actionable usage instruction. Every word earns its place with no redundancy.

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 search tool, the description, combined with the schema and annotations, gives an agent enough information to select and invoke it correctly. The scope is defined, the usage is clear, and the parameters are fully documented. No output schema exists, but the tool's behavior is straightforward enough that this is not a significant gap.

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?

Both parameters have schema descriptions with examples and defaults, giving 100% coverage. The description adds no additional parameter semantics beyond what the schema already documents, so the 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 a full-text search across Flamel's blog posts, case studies, and FAQ entries. This specific verb and resource scope distinguishes it from the getters/listers for individual content types, making its purpose immediately clear.

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 gives explicit guidance: 'Use this first when you do not already know which page answers the question.' This clearly indicates when to use the tool, but it does not explicitly name alternative tools or state when-not-to-use, so it falls short of a full 5.

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