Flamel Public Content

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

Annotations already cover read-only, idempotent, non-destructive behavior, and the description adds meaningful constraints: only published posts, full body content, Markdown format, and slug-based lookup. This supplements the annotations with useful behavioral context beyond what structured metadata provides.

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 verb, resource, format, and key parameter with no filler. Every phrase 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?

For a simple one-parameter read tool with full annotations and an output schema, the description is complete. An agent has everything needed to decide when to use it and how to invoke it 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% and the single slug parameter is well documented with an example and meaning. The tool description adds little beyond 'by slug', so the schema carries the semantic weight; a 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 uses a specific verb (Fetch) and resource (full body of one published Flamel blog post as Markdown, by slug), which clearly distinguishes it from sibling listing and search tools. It leaves no ambiguity about what object is returned or how it is identified.

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 makes it clear this tool is for retrieving a single post by slug, which implicitly signals the right context versus list_blog_posts or search_flamel_content. It does not explicitly mention when not to use it or name alternatives, but the scope is specific enough for an agent to select it correctly.

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 no destructive actions, so the description need not repeat safety. The description adds value by specifying the retrieval granularity (single item) and the included content sections, which goes beyond the schema's slug parameter. It does not detail what happens if the slug is not found, but this is minor given the safety coverage.

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 action and key details. It doesn't waste words, and the focus on content sections is efficient and informative.

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 is sufficient for an agent to call this tool correctly: it identifies the input (slug) and the output content areas. The output schema exists, so return-value details are not required. The description could specify error behavior, but that is a minor gap given the simple single-parameter tool and safety annotations.

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 fully covers the slug parameter with a clear description and an example URL structure. The description does not add new semantic meaning beyond what the schema provides, but with high coverage, that is acceptable. Baseline 3 is appropriate because the schema does the heavy lifting.

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 a single Flamel case study by slug announced with a specific verb ('Fetch'). It lists the key content sections (challenge, solution, results, quotes), which is specific and helps the agent know what to expect. It distinguishes from siblings like list_case_studies by implying a single item lookup.

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 states the use case: fetching a single case study by slug. This clearly implies it is for when a specific case study is needed, contrasting with list_case_studies. It does not explicitly mention when not to use it or name alternatives, but the context is clear enough for an agent to choose correctly.

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, so the safety profile is well covered. The description adds context by specifying the overview includes links and sales/trial paths, and by positioning the tool as an orienting step rather than a search or detailed-content retrieval.

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 filler: the first names the deliverable and its contents, the second adds a clear routing cue. Every clause contributes to an agent's understanding of what this tool is for and when to use it.

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 overview tool with an output schema, the description is complete. An agent knows what information to expect, when to start here, and that the operation is safe. Sibling differentiation is handled with the 'start here' cue, and the annotations cover the remaining behavioral guarantees.

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 there is nothing for the description to clarify about inputs; the baseline for a no-parameter tool is appropriate. The description focuses instead on what the returned overview contains, which is the only useful semantic content.

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 ('Get') with a defined deliverable ('structured overview of Flamel') and enumerates the content areas covered. It also frames itself as the entry point, distinguishing it from sibling content-detail tools like get_blog_post or get_case_study.

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 instruction 'Start here if you know nothing about Flamel' explicitly states the condition for use and implies this tool is the initial routing point before more specific content tools. It does not explicitly name alternatives or exclusion conditions, but the entry-point guidance is clear and actionable.

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 cover read-only, idempotent, and non-destructive behavior. The description adds purpose but does not describe output format or potential edge cases, so it adds moderate 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 conveys the tool's function and purpose without unnecessary details. It is optimally concise.

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 is complete for the tool's simple purpose, but it does not mention the return format or any nuances like sorting or pagination. Given the presence of an output schema, it is sufficiently complete for an agent to use 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 parameter schema has 100% coverage because there are no parameters. The description adds no additional parameter details, so it meets the baseline 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 tool lists blog categories and explicitly mentions their purpose for filtering blog posts. This makes it distinct from sibling tools that list other entities like 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?

The description implicitly indicates this is used when you need category options for filtering blog posts. It does not explicitly mention alternative tools, but the context is clear for a tool with no parameters.

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, non-destructive, idempotent behavior. The description adds meaningful behavioral details: only published posts, newest-first ordering, optional category filtering, and the explicit exclusion of full bodies. These are useful beyond the structured fields.

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 with no filler. The primary action and scope are front-loaded, and the return-format clarification is efficiently included without bloating the description.

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 read-only list operation with complete parameter descriptions, rich annotations, and an output schema present, the description is fully adequate. An agent knows what to expect, what to pass, and what will be returned.

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 each parameter clearly documented (page, limit, category, including defaults and allowed values). The description mostly restates the category filtering option without adding parameter-level meaning beyond the schema, so 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?

Clearly states the specific action ('list'), resource ('blog posts'), scope ('published'), ordering ('newest first'), and return fields ('titles, URLs, summaries, publish dates'). The 'not full bodies' clause distinguishes it from get_blog_post.

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 it should be used (browsing published post summaries) and what it will not do (return full bodies), which points toward get_blog_post for full content. It does not explicitly name alternatives or exclusions, but the context is clear.

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?

The annotations already declare readOnlyHint, idempotentHint, and destructiveHint to be true, covering the safety profile. The description adds useful context by specifying 'published' status and that results include headline results, which is not in the annotations. However, it does not disclose pagination, ordering, or filtering behavior, though the presence of an output schema (indicated by 'Has output schema: true') reduces the need to explain return values. The description does not contradict 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 with no redundant or filler words. It front-loads the primary action ('List') and resource, and the qualifiers 'published' and 'headline results' add essential specificity without bloat. It is appropriately concise for a zero-parameter tool.

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 read-only listing tool with no parameters and comprehensive annotations (readOnly, idempotent, non-destructive), the description is sufficient. The output schema (present) likely details the structure of the list, and the description tells an agent what it will get (case studies with headline results) and its scope (published only). There is no missing information that would prevent an agent from deciding to invoke this 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 100% covered. Per the rubric, a baseline score of 4 applies when there are no parameters. The description does not need to explain parameters because none exist, and it does not introduce any implicit parameter-like constraints beyond the 'published' filter, which is a semantic qualifier rather than a parameter.

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 states a specific verb ('List'), a specific resource ('published Flamel customer case studies'), and adds 'with their headline results' to clarify what the list contains. It clearly differentiates from siblings like get_case_study (single item) and list_blog_posts (different content type) without ambiguity.

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 listing all published case studies, and its simplicity suggests use cases like browsing or overview. However, it does not explicitly state when to prefer this tool over search_flamel_content or get_case_study. No exclusions or alternatives are mentioned, so the guidance is implied rather than explicit.

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 cover readOnly, idempotent, non-destructive. The description adds that it lists published entries and what they cover, but doesn't clarify any unusual behavior like default pagination or error handling. Given annotations, it is adequate 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?

Two sentences that are direct and informative, with no 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?

The description plus schema and annotations sufficiently cover the tool's purpose and usage for a simple list operation. The output schema is separate, so return values are already defined. The tool is straightforward, and the description provides enough context for when to use it.

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 each parameter described. The description does not add additional semantics beyond the schema, so 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 action (List) and the specific resource (published Flamel FAQ entries), and enumerates the content categories (capabilities, pricing, integrations, security). It is immediately distinct from sibling tools like list_blog_posts or get_case_study.

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 it's good for 'direct factual questions about the product,' which gives usage context. However, it doesn't explicitly mention when not to use it or contrast with search_flamel_content, which might be the alternative for broader queries. It implies the use case but doesn't exclude 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?

Annotations already declare the tool read-only, idempotent, and non-destructive, so the description's job is lighter. It adds meaningful context by specifying that only published testimonials are returned and what fields each item contains, which is useful beyond 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?

A single, well-structured sentence that covers the resource, scope, and return content without redundancy. No wasted words and the key terms are 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 read-only list tool with two fully documented parameters, an existing output schema, and clear annotations, the description provides enough context. The published-testimonials scope fills the main behavioral gap and the tool works well within the sibling list.

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 clear descriptions for the limit (range and default) and the featured boolean. The description contributes no additional param guidance, but the schema fully documents both parameters, so 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 uses a specific verb ('List') with a clear resource ('published customer testimonials about Flamel') and enumerates the included fields (author name, title, company). This clearly distinguishes it from sibling list tools for blog posts, case studies, and FAQs.

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 intended use case is implied by naming the resource and its scope, e.g., to fetch testimonials as opposed to blog posts or case studies. However, the description does not explicitly state when to prefer this over alternatives like search_flamel_content, nor provide any exclusion criteria.

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, covering the non-mutating nature. The description adds that it searches 'full-text' across specific content types, giving context on scope. It doesn't mention pagination or result grouping, but with annotations covering safety, this is a minor gap. A 4 is appropriate as it adds useful context 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 sentence, front-loaded with the purpose and usage guidance. It is concise and every clause earns its place, efficiently conveying when to use it.

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 (two params, one required, high schema coverage, output schema present), the description is mostly complete. It could mention that results are grouped per content type or how to handle multiple content types, but for an agent, the essential information is present. A 4 is fair.

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 description doesn't need to compensate. However, the query parameter's description is clear with an example, and limit has defaults and range. The description adds little beyond schema, but given high coverage, a baseline of 3 is correct.

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 it performs full-text search across Flamel's published blog posts, case studies, and FAQ entries. It specifies the resource (content types) and the action (search), distinguishing it from retriever tools like get_blog_post or get_case_study. The scope is explicit and the phrasing 'Use this first when you do not already know which page answers the question' further clarifies its role.

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 instructs to use this tool first when the agent lacks knowledge of the specific page, providing clear guidance on when to use it. While it doesn't explicitly name alternatives, the context of siblings and the instruction to use this first imply that once a page is identified, use the respective retrieval tools. This is strong enough to warrant a 5.

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