Zyla API Hub

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

With no annotations, the description carries the full burden. It makes clear this is a read-only check ('Check', 'Returns'), lists the returned data, and discloses the authentication requirement (API key in Authorization header). No side effects are implied beyond reading status.

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?

Every sentence adds necessary detail: what it does, what it returns, when to use it, and what auth is required. It is detailed without being verbose and front-loads the core purpose.

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 tool with no output schema, the description fully covers behavior, return fields, diagnostic purpose, and authentication. Nothing critical is missing for an agent to select and 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?

The tool has zero parameters, so there is no parameter vocabulary to explain. The baseline of 4 applies because no parameter documentation is needed.

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?

Description states a specific verb ('Check') and resource ('current user's account status, billing, and usage') and enumerates exact returned fields. It is clearly distinct from sibling tools like call_api or get_payment_url.

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 to use it to diagnose why API calls are blocked due to missing card or failed payment. It does not name alternatives or exclusions, but the use case is specific 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?

No annotations are provided, so the description carries the full burden. It states the tool returns paginated results with total count and page info, but does not disclose authentication requirements, rate limits, or that it is read-only. The description adds some behavioral context but is not comprehensive.

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 three concise sentences that front-load the core purpose and key features. Every sentence provides valuable information 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 has 6 parameters and no output schema or annotations, the description covers pagination, filtering, and response contents. It is mostly complete but could mention authentication or error cases for full context.

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 reinforces that search is server-side across all APIs and mentions filtering, but does not add significant new meaning 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 it's for browsing the full Zyla API Hub catalog with pagination. It distinguishes from sibling tools like 'search_catalog' by emphasizing exploration of all public APIs beyond the top 500, making its 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 advises using this to explore all public APIs beyond the top 500, which sets context. However, it does not explicitly contrast with the sibling 'search_catalog' tool or specify when not to use it, lacking 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?

With no annotations provided, the description carries the full burden and does so thoroughly. It discloses pay-per-successful-2xx billing, the cost_usd field in every successful response, automatic credit card charging, no monthly fee or minimums, and the mandatory browser flow for 402/payment_url. This is far beyond a basic 'calls an API' statement.

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 long but information-dense; each sentence adds a distinct operational fact such as billing model, cost reporting, automatic charging, and payment recovery. It is front-loaded with the core action and prerequisites before the billing details. Minor redundancy between 'credit card on file' and 'charged automatically' prevents 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?

For a generic execution tool with no output schema, the description covers the essential contract: required inputs, auth prerequisites, billing side effects, the successful-response cost field, and the key 402 failure mode. It does not specify how the API key is passed or describe general error response shapes, but the schema and sibling tools fill much of the surrounding context.

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 already documents all four parameters at 100% coverage, so the baseline is 3. The description only restates 'Pass the api_id, endpoint_id, HTTP method, and parameters' and adds no deeper meaning about how params are structured or how they map to endpoint behavior. It is not harmful, but it does not elevate the score.

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 clear verb and object ('Call an API endpoint on Zyla API Hub') and specifies the required inputs: api_id, endpoint_id, HTTP method, and parameters. It doesn't explicitly contrast the tool with catalog/lookup siblings, but its generic execution role is unmistakable.

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 names concrete prerequisites (API key, credit card on file), points to account_status for billing mode and accrued charges, and references get_api_info for pricing. It also gives explicit 402/payment_url retry behavior. It doesn't explicitly state 'use this tool when you need to execute the actual API call after discovering APIs', but the context strongly implies 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?

Lists exactly what is returned (name, description, category, price, latency, endpoints, schema). No annotation coverage, but tool is read-only with no side effects; disclosure 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?

Single sentence with clear structure: action, input, output. No redundant words. Every clause adds value.

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 one parameter, no output schema, and no annotations, the description fully explains what the tool does and what it returns. No gaps.

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?

Only one parameter 'api_id' with example and type already in schema. Description adds context that ID is numeric and ties to the information retrieval. Schema coverage is 100%.

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 verb 'Get full details' and the resource 'specific Zyla API by its numeric ID'. Distinguishes from siblings like browse_catalog (listing) and call_api (execution).

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?

Implied use when needing full API details given an ID, but no explicit guidance on when to use versus search_catalog or browse_catalog, or prerequisites like obtaining the ID first.

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 behavioral burden and does so well. It discloses billing semantics (pay-per-successful request, no monthly fee), clarifies that only 2xx responses are charged, and notes that API calls are blocked without a valid card.

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 front-loaded with the core action and then provides essential trigger conditions and billing context. It is slightly longer than strictly necessary, but every sentence adds relevant decision-making or behavioral information.

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 tool with no output schema, this description is complete. It covers when to call it, what the user must do with the result, how billing works, and the blocking condition, tying it together with sibling tools like register and get_api_info.

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 and 100% schema coverage by definition, so there is no parameter ambiguity. The baseline of 4 applies because no parameter documentation is needed.

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 and resource: it retrieves a Stripe Checkout URL for adding or updating a credit card. This clearly distinguishes it from siblings like call_api, register, and get_api_info.

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 when to use the tool: REQUIRED after registration and whenever a 402 error occurs. It also explains that the user must open the URL in a browser and that API calls are blocked until a card is on file, giving clear usage context.

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?

No annotations are provided, so the description carries full burden. It states that the tool lists categories with counts, which is a safe read operation. It does not disclose any additional behavioral traits like authentication or rate limits, but for a simple list tool 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?

Two sentences: first describes the action and result, second provides usage guidance and an example. No wasted words, front-loaded with key information.

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 zero parameters, no output schema, and a simple purpose, the description is complete. It covers what the tool does and how its output is used, which is sufficient for an agent.

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?

There are no parameters, so baseline is 4. The description adds value by explaining how the output (category names) is used in browse_catalog, though it does not need to describe parameters.

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 'List all available API categories' with specific resource 'categories on Zyla API Hub' and includes the added detail of 'real count of public APIs in each', which distinguishes it from siblings like browse_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?

It explicitly instructs to use the returned category names as the 'category' parameter in browse_catalog, providing clear context for when to use this tool. It does not mention when not to use it, but the context is sufficient.

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 of behavioral disclosure. It does this thoroughly: it discloses that registration returns an API key and payment URL, that the user MUST open the payment_url in a browser to add a credit card before API calls, the pay-per-request billing model with no monthly fee, and that no authentication is required. This goes well beyond a bare 'registers an account' and gives the agent actionable behavioral 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 front-loaded with the core purpose and outputs, then covers the critical user action and billing model. It is a bit longer than strictly necessary, but every sentence contributes useful information—especially the billing terms and the instruction to check account_status. No filler or 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 itself is a simple 3-parameter registration action with no output schema, the description is complete enough for an agent to invoke it correctly and manage expectations. It spells out the outputs (API key, payment_url), the required follow-up action (open payment_url in browser), the billing mode, and authentication status. It even references account_status for accrued usage, covering the natural next step.

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?

All three parameters (name, email, password) are already fully described in the input schema with 100% coverage. The description does not add parameter-specific guidance beyond what the schema provides. Baseline 3 is appropriate because the schema already carries the semantic load here.

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 begins with a clear, specific verb and resource: 'Register a new Zyla API Hub account directly from the AI agent.' It then names the two outputs (API key and payment_url), which makes the tool's function unmistakable. This also distinguishes it from sibling tools like account_status or search_catalog, which serve different purposes.

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 for when to use this tool: it is the account registration entry point and requires no authentication. It also explicitly routes the agent to account_status for billing details, which acts as a pointer to a sibling alternative for follow-up. It does not explicitly state 'use this instead of X,' but the purpose and post-conditions make the usage context 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?

No annotations provided, so description carries full burden. It implies a read operation via 'search' but does not explicitly disclose safety, auth requirements, or rate limits. Adds return field details but lacks explicit behavioral traits.

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 wasted words: first sentence states core purpose, second gives query guidance and result fields. Front-loaded and 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 no output schema, the description details return fields (ID, name, description, category, price, latency, endpoints). Lacks pagination or error handling, but sufficient for a search tool with well-documented parameters.

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%, baseline 3. Description adds value by emphasizing natural-language queries and providing examples for the query parameter, and explains what the results contain, which is not in 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 performs semantic search across all 10,000+ public APIs on Zyla API Hub, distinguishes from siblings like browse_catalog and get_api_info by specifying scope and query 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?

Provides context that natural-language queries work best and gives examples, but does not explicitly state when not to use this tool or mention alternatives beyond implied sibling differentiation.

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