interactive-api-playground

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

With no annotations, the description carries the burden of transparency. It discloses the live nature, external URL scope, private IP blocking, and return contents, but omits details like authentication, rate limits, redirect behavior, and error handling. This is moderate added value but 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?

Two sentences, front-loaded with the core purpose, and a concise safety note. No wasted words, and the structure is easy to parse.

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 tool with no annotations or output schema, the description provides the essential purpose and a key constraint, but it lacks guidance on parameter formats, error handling, and edge cases like redirects or non-2xx responses. Adequate but incomplete for a network tool with five 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 description coverage is only 20%, and the description does not explain the body, headers, or timeout parameters beyond what the schema offers. It focuses on outputs rather than input semantics, leaving significant gaps for a 5-parameter 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's action ('Execute a live HTTP request') and its outputs ('real status, headers, and response body'), making it distinct from all sibling tools (code_generator, history_tracker, etc.). The additional constraint on private IPs adds useful context.

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 when to use this tool (for live external URL requests) and notes a critical exclusion (private IPs are blocked). However, it does not explicitly compare to alternatives or state when not to use it beyond the IP restriction, 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?

No annotations are present, so the description carries the full burden. It accurately indicates the tool generates code without executing it, which is a key behavioral trait. However, it omits details about error handling, output format, or side effects (though none are expected for a code generator). The description is functionally clear but minimal.

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 efficiently communicates the tool's main function and scope. Every word adds value, and there is 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?

The tool is relatively simple with 5 parameters and no output schema, yet the description covers only the high-level purpose. It does not explain how method, url, headers, and body combine to form the request, nor does it mention any constraints or return value details. The description is minimally viable but leaves gaps for a new 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?

Schema description coverage is 0%, and the description does not explain the purpose of url, method, body, or headers. It only lists language options, which are already defined by the schema enum. The phrase 'for an HTTP request' hints at the parameters' collective role but does not add meaning to individual parameters, failing to compensate for the missing schema descriptions.

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 ('Generate'), a resource ('a ready-to-run code snippet for an HTTP request'), and enumerates the supported languages. This clearly differentiates from sibling tools like api_executor (executes requests) and schema_parser (parses schemas).

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 implied by the description and tool name, but no explicit exclusions or alternative recommendations are provided. An agent can infer this is for producing code rather than executing, but the description does not mention when to choose this over api_executor or other 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?

Without annotations, the description carries the burden of disclosure. It discloses two key behaviors: the history is scoped to the current Worker instance and it resets on cold start. However, it does not describe the output format or any other behavioral nuances, leaving some gaps.

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 short sentences, front-loaded with the core purpose and an important caveat. No redundant 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 simple tool with one parameter and no output schema, the description covers the essential aspects: what it does, its scope, and a key limitation (reset on cold start). It leaves out details about the response, but this is minor 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 input schema has one parameter 'id' with a clear description and examples. Since schema coverage is 100%, the description does not need to add more. However, it also adds no additional context about the parameter, so the baseline of 3 stands.

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 'Get' and identifies the resource as 'invocation history for a tool within the current Worker instance.' This clearly distinguishes the tool from its siblings (api_executor, code_generator, etc.), which are the tools whose history is being tracked.

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 alternatives, but it implies that it is for inspecting current-instance invocation history. It lacks any mention of alternatives or exclusions, so the usage context is only implied.

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 that the output is self-contained HTML with try-it-out forms, which is key behavioral context. However, it does not address potential side effects, prerequisites, or error handling, leaving some transparency gaps.

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-loaded with the action verb 'Render', and contains no redundant information. Every word 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 renderer with three parameters and no output schema, the description covers the core function and return value. It lacks usage context and interaction details but is mostly complete 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 all three parameters with 100% coverage, so the description adds no additional parameter-specific meaning. Per the rubric, baseline 3 is appropriate when schema coverage is high.

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 ('Render') and identifies the resource ('interactive HTML API playground') for 'a list of endpoints'. This clearly distinguishes it from sibling tools like api_executor and code_generator, which handle execution and code generation respectively.

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?

No guidance is provided on when to use this tool versus alternatives. The description only states what it does, leaving the agent to infer the use case without any explicit context 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?

No annotations are provided, so the description must carry the burden. It discloses the input formats and return value, but does not mention potential errors, the fact that exactly one of the two parameters must be provided, or any side effects. As a parser, it is likely safe, but this is not explicitly confirmed.

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 with no redundant wording. Every phrase adds value: the action, the accepted inputs, and the output shape.

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 handles two distinct input formats and has no output schema, yet the description only states the general output shape. It does not clarify that one parameter is required, nor does it explain the structure of the returned list. However, for a parser, the core behavior is adequately covered.

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 both parameters are well-described in the schema. The description adds the relationship between the parameters ('alternative to schema') but does not provide additional format or constraint details beyond what the schema already includes.

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 ('Parse') and names distinct resources ('OpenAPI 3.x schema object' or 'GraphQL SDL string'), clearly distinguishing it from siblings like api_executor or code_generator. It also states the output ('structured list of endpoints (and GraphQL 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 when to use this tool: whenever you need to extract endpoints or types from a schema. It does not explicitly describe exclusions or alternatives, but the sibling tool names and the clear parse-only purpose provide adequate context for selection.

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