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api_test_request

Execute GET, POST, PUT, DELETE, or PATCH requests, measure response latency, and view formatted API output for debugging and validation.

Instructions

Executes an HTTP request (GET, POST, PUT, DELETE, PATCH) and measures latency & formats response data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesTarget URL (e.g. 'https://api.github.com/zen')
bodyNoOptional request body (for POST/PUT)
methodNoHTTP method
headersNoOptional request headers as key-value pairs
timeoutMsNoRequest timeout in milliseconds (default: 10000)
Behavior2/5

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 states that the tool executes an HTTP request, which implies network activity, but it does not warn that mutating methods (POST, PUT, DELETE, PATCH) can modify remote resources, nor does it mention potential side effects, error handling, or authentication requirements. The added details about latency measurement and formatting are helpful but insufficient for a tool that can cause external changes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, compact sentence that front-loads the core action and includes the supported methods, followed by the secondary outputs. It conveys the essential information without waste. Minor improvement could be splitting into two sentences (execution vs. measurement/formatting) but overall it is appropriately concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (network call, 5 parameters, potential side effects) and the absence of an output schema, the description is too sparse. It does not explain what the formatted response looks like, how latency is measured, what happens on error, or whether authentication is required. An agent calling this tool would lack critical context for interpreting results and handling failures.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema describes all 5 parameters with 100% coverage, so the schema already documents each field's meaning (url, body, method, headers, timeoutMs). The description adds no extra semantic detail beyond the schema, so the baseline of 3 applies. The description does not compensate for any schema gaps because there are none.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('executes an HTTP request'), enumerates the supported methods, and adds two further outputs (latency measurement and response formatting), which is more specific than the tool name alone. It is not explicitly differentiated from sibling tools, but the siblings are all in different domains (security, docs, git, etc.), so the risk of confusion is low. A clear, unambiguous statement of function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide any guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. While the action itself implies a use case (testing an API endpoint), there is no explicit statement such as 'use for live HTTP testing' or 'not for generating mocks'. The agent is left to infer usage entirely from the purpose.

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

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