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intercept_network

Intercept network requests matching a URL pattern and return custom HTTP responses to test error, empty, and loading states without a real backend.

Instructions

Mock API responses on a session. Intercept requests matching a URL pattern and return custom responses. Use to test error states, empty states, loading states without needing the real backend. Call BEFORE the action that triggers the request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyNoResponse body (JSON string or plain text)
onceNoOnly intercept the first matching request (default: false)
methodNoHTTP method filter (optional, e.g. 'GET', 'POST')
statusNoHTTP status code to return (default: 200)
session_idYesSession ID
url_patternYesURL substring to match (e.g. '/api/tasks', 'graphql')
content_typeNoContent-Type header (default: 'application/json')
Behavior3/5

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

With no annotations, the description must disclose behavioral traits. It explains the mocking behavior and ordering requirement but lacks details on persistence of intercepts, impact on other requests, or how to clear them. This is a moderate gap.

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

Conciseness5/5

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

The description is extremely concise: three short sentences that are front-loaded with the key action and use cases. No wasted words.

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

Completeness4/5

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

Despite 7 parameters and no output schema, the description covers the main use case and ordering requirement. It does not explain cleanup, but a sibling tool 'clear_intercepts' exists, suggesting the system handles that. Overall, it is nearly complete for the intended audience.

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?

Schema coverage is 100% with all parameters having descriptions. The tool's description does not add significant meaning beyond what the schema provides, so baseline 3 is appropriate.

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

Purpose5/5

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

The description clearly states the tool mocks API responses on a session, intercepting requests matching a URL pattern and returning custom responses. It distinguishes itself from siblings like 'emulate_network' and 'clear_intercepts'.

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

Usage Guidelines4/5

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 (testing error states, empty states, etc.) and gives a critical ordering instruction: 'Call BEFORE the action that triggers the request.' It does not provide explicit when-not-to-use or alternatives, but the context is clear.

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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