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

With no annotations provided, the description carries full responsibility for disclosure. It states that matching occurs against title and body content and that all matches are returned, which is useful. However, it does not discuss limitations, authentication, pagination, or return structure.

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 starts with the action verb and contains no unnecessary words. Every part contributes to understanding the tool's function and 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?

For a simple one-parameter search tool with no output schema, the description adequately covers purpose, search fields, and return behavior. It could mention pagination or result ordering, but the tool is simple enough that the current level is 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?

The schema only describes 'query' as a search term. The description adds that the query matches against title and body content, providing field-level semantics beyond the schema and increasing clarity for the agent.

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 verb 'Search' with the specific resource 'Ghost CMS pages' and clarifies that matching is against title and body content. This distinguishes it from sibling tools like search_blog_posts or search_events.

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 searching pages, but provides no explicit guidance on when to choose it over alternatives such as search_blog_posts. There are no exclusions or alternative references, leaving the usage context 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, the description carries the burden of behavioral disclosure. It adds useful context by specifying which fields are searched and that all matching posts are returned. However, it omits details about matching semantics (e.g., case sensitivity, partial vs. exact matching) and any rate limits or authentication requirements, leaving some behavioral ambiguity.

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 verb and resource, and every word contributes to understanding the tool's purpose and behavior. No redundant or filler content.

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 simplicity of the tool (one parameter, no output schema, no annotations), the description covers the essential aspects: what is searched and what is returned. It could be more complete by explaining result format or matching behavior, but for a basic search tool it is sufficiently complete for an AI 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?

Schema coverage is 100% with a basic description of the query parameter. The tool description adds meaningful context by specifying that the query matches against title, excerpt, and body content, which goes beyond the schema's generic 'search term to match against blog post 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 clearly states the verb 'Search', the resource 'Ghost CMS blog posts', and the specific scope 'matching query against title, excerpt, and body content'. It distinguishes from sibling tools like search_blog_pages by specifying posts rather than pages, and from other content types by explicitly naming 'blog posts'.

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 clearly implies this tool is for searching blog posts, and the sibling tool names indicate alternatives for other content types. However, it does not explicitly state when not to use this tool or name alternatives as exclusions, so it stops short of full usage 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?

With no annotations provided, the description carries the full burden. It discloses that the search matches across a specific set of fields and that all matching case studies are returned, effectively indicating an unbounded result set. This is useful behavioral context. It does not explicitly state the operation is read-only, but that is inherently obvious for a search tool. The description provides more behavioral detail than a generic search tool and is not misleading.

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 states the action and resource. It efficiently lists the searched fields and concludes with the return behavior. Every word contributes to understanding, with no fluff or repetition of schema details.

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 tool is simple (one required parameter, no output schema), and the description covers the input meaning and result scope ('all matching case studies'). It does not describe the structure of a case study object in the response, which could be useful without an output schema, but the overall context is sufficient for an agent to invoke the tool correctly for most search scenarios.

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 alone defines the 'query' parameter as a search term with 100% coverage. The description adds significant meaning by listing all the content fields against which the query is matched, giving the agent a concrete understanding of what 'match' entails. This goes beyond the schema's generic description, though it does not add syntax or formatting 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 starts with the specific verb 'Search' and identifies the exact resource ('Contentful case studies (success stories)'). It further disambiguates from sibling tools by enumerating the fields being matched (company name, meta title, meta description, overview, quote, use case, impact, and body content) and explicitly states the return behavior ('Returns all matching case studies'). This is a model of clear, specific purpose.

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 clearly implies when to use this tool: when searching for case studies/success stories, not blog posts or events. It provides a distinct context from sibling tools by naming the content type and fields. However, it does not explicitly mention alternatives or state 'use this instead of X when Y', so it stops 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?

With no annotations provided, the description bears the full burden of behavioral disclosure. It states that the tool searches and returns all matching events, but does not discuss potential side effects, authentication requirements, rate limits, or output format. Since it's a search operation, the read-only nature is implied but not explicitly stated.

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 concise at two sentences, front-loads the core action, and avoids redundant detail. Every word contributes to conveying purpose and behavior.

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 low complexity (one parameter, no output schema), the description is largely complete: it explains what is searched, which fields are matched, and what is returned. It stops short of discussing result ordering or pagination, but these are minor gaps for a simple search 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 for the query parameter is 100%, giving a baseline of 3. However, the description adds meaningful semantics by specifying that the query matches against title, description, and body content, which goes beyond the schema's generic 'match against event content' phrasing.

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: searching Contentful events, specifying the exact content types (event pages and event cards) and fields matched (title, description, body content). This specific verb+resource combination distinguishes it from sibling search tools targeting other 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 establishes clear context for when to use this tool (when searching for events), implicitly differentiating it from sibling tools for other content types. It does not explicitly mention alternatives or exclusions, but the resource-specific language makes the intended usage apparent.

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 carries the burden of disclosing behavior. It clearly states that the search matches against multiple specified fields and that the tool returns all matching pages, which goes beyond the schema. However, it omits potential details like pagination, ordering, or case sensitivity, so it's not fully transparent.

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 concise sentences, front-loading the purpose and then adding return behavior. Every word is informative, with no redundant phrasing or filler.

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 search tool with no output schema, the description provides sufficient context: it names the resource, the searchable fields, and the return behavior. It also clearly distinguishes its scope from the sibling tools. There are no nested objects or complex outputs requiring further elaboration.

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 already describes the 'query' parameter as a search term to match against learn page content, and coverage is 100%. The description adds value by enumerating exactly which fields (title, meta title, meta description, body content) are searched, giving the parameter richer meaning than the schema alone.

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 ('Search'), identifies the resource ('Contentful learn pages'), and details the searchable fields (title, meta title, meta description, body content). This clearly distinguishes it from sibling search tools targeting other content types like case studies, events, blog posts, and blog pages.

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 does not explicitly state when to use this tool versus the sibling search tools, nor does it mention exclusions or prerequisites. The resource type 'learn pages' implies the appropriate usage, but this is not spelled out, leaving the agent to infer context from the tool name.

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