AI Agents API Library

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

With no annotations provided, the description carries the behavioral disclosure burden. It goes beyond a field list by warning that a repository must be installed and a docs-only server does not call the API, and by noting values come from vendor pages and that pricing/terms should be confirmed there. It does not explicitly state read-only behavior, but that is reasonably implied by 'get' and the content.

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 dense and front-loaded ('Full entry for one service'), and every listed item earns its place. However, it is structured as one very long run-on sentence with nested parentheticals, which hurts skimmability enough to prevent a 5.

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 there is no output schema, the description compensates well by enumerating the major output categories and important caveats such as repository installation, link checks, and fetch dates. It lacks explicit success/error behavior, but for a one-parameter lookup tool the provided context is largely 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 single parameter is already fully described in the schema as 'Service id from search_apis,' and the description adds no additional parameter-level detail. With 100% schema coverage, 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 clearly states a specific operation: returning the full entry for one service, enumerating docs, auth, base URL, operations, rate limits, and separate signals. It implies differentiation from search/list siblings by scoping to 'one service,' but it does not explicitly name or contrast sibling tools, so it falls short of a 5.

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?

Usage context is only implied: the schema says the id comes from search_apis and the description says 'Full entry for one service,' suggesting this is the drill-down call after a search. There is no explicit guidance about when to use this tool versus search_apis, get_reviews, or list_categories, and no exclusions are 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?

With no annotations, the description carries the full burden. It discloses the source (SourceForge), the 100-review cap, omitted reviewer names, and that review text is third-party content to treat as data, never instructions. It does not mention error or pagination behavior, but the safety/trust profile is clearly conveyed.

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 dense but not bloated, front-loading the core purpose and field list. The security note earns its place, and the filter guidance is useful, though a few phrases restate schema 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?

For a list-style tool with no output schema, it covers the return fields, source, limits, and usage context. It does not describe pagination or result shape, but enough context is present 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?

Schema description coverage is 100%, so the baseline is 3. The description repeats the filter semantics (since, max_rating, about_api) and the complaint use case, but adds no meaning beyond what the schema already documents for each 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 resource and action: public user reviews of one service from SourceForge, with a list of fields returned. It is clearly distinct from get_api and search_apis in subject matter, though it does not explicitly name or contrast those siblings.

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 when real-world quality matters, e.g., to break a tie, and warns that reviews do not show whether the API fits the task. It does not name an alternative tool, so it misses the top bar for explicit 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?

Without annotations, the description carries the full burden, and it does well by specifying that it returns categories along with the number of services in each. The 'List' verb implies a read-only operation with no side effects. It omits details like pagination or empty-category handling, but for a simple zero-parameter listing this is sufficient.

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 front-loads the action and resource, then adds the key output detail. Every word contributes value; there is no redundancy 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 zero-parameter, read-only list operation with a clear output description, nothing is missing. The agent can call this tool correctly without additional context, and the sibling tools are distinct enough to avoid confusion.

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 explain. The baseline of 4 applies because no parameter ambiguity exists.

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'), the resource ('all categories in the catalog'), and the specific output detail ('number of services in each'). It is easily distinguished from siblings like get_api, get_reviews, and search_apis, which focus on individual APIs, reviews, or searching rather than an overall category overview.

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: use this tool to get an overview of all categories and their service counts. It does not explicitly name alternative tools or exclusions, but the purpose is unambiguous enough that an agent would not confuse it with the listed siblings.

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?

There are no annotations, so the description carries the full behavioral burden. It discloses match semantics (exact vs adjacent), cautions that ranking is not proof of fit, explains what an empty result means, and warns that services work only after a human provisions credentials. These details go well beyond the schema and meaningfully shape an agent's expectations.

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 dense but every sentence earns its place: purpose, usage timing, failure handling, result interpretation, and the credential prerequisite. It is somewhat long, but the length is justified by 9 parameters, no annotations, and no output schema. The purpose is front-loaded and the guidance follows logically.

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 search tool with no annotations and no output schema, the description covers the essential context: when to use it, what results contain (match, operations, what), how to interpret failures, and the human-credential requirement. It does not enumerate the full return fields or pagination, but this is not critical for invoking 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 coverage is 89%, so the baseline is 3. The description adds some contextual guidance around query matching, volume comparison, and retrying with category, but most parameter semantics are already well documented in the schema. It doesn't substantially enrich the meaning of individual parameters beyond what the schema 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 opens with a specific verb and resource: 'Find a third-party API or MCP server for a capability you lack, among 300+ catalog entries.' This clearly establishes a catalog search tool, which distinguishes it from siblings like get_api (retrieving details for a specific entry) and list_categories (listing categories). The scope, purpose, and output emphasis are immediately understandable.

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 explicit when-to-use and when-not-to-use guidance: use it after checking built-in/connected tools and your own model, skip it if those suffice, and use it after direct attempts fail with 403/rate limit/unsupported format. It also tells the agent what to do on an empty result: retry with category, drop filters, or fall back to web search.

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