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vola-trebla

playwright-network-chaos-mcp

by vola-trebla

inject_response_corruption

Intercept requests and corrupt responses with malformed JSON, false content-length, or truncation to verify resilient error handling.

Instructions

Intercepts requests matching a URL pattern and returns a malformed or corrupted response, simulating partial network failures at the protocol level. Use to answer: does the app handle malformed JSON, content-length lies, or truncated payloads without crashing?

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the page to test
wait_msNoMilliseconds to wait after navigation before checking state (default: 2000)
viewportNoViewport size (default: 1280×720)
corruption_typeYesType of corruption: malformed_json (unterminated JSON body), length_mismatch (content-length claims 99999 bytes but body is short), truncated (body cut off at truncate_at_byte)
truncate_at_byteNoByte offset to truncate at (only used when corruption_type is truncated)
fallback_selectorNoCSS selector for the fallback UI that should appear (e.g., '.parse-error')
intercept_patternYesGlob pattern for requests to corrupt (e.g., '**/api/data**')
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It explains the core behavior (intercepting and corrupting responses) but does not mention potential side effects, cleanup, or whether the corruption affects subsequent requests. This is adequate 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.

Conciseness5/5

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

The description is two sentences, front-loaded with the purpose and followed by a concrete use-case question. Every word earns its place with no redundancy.

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

Completeness3/5

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

The description covers purpose and usage, and the schema handles parameter semantics. However, there is no output schema and the description does not explain return values or how the fallback_selector is used, leaving some ambiguity about what the agent should expect after invocation.

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 description coverage is 100%, so the schema fully documents all parameters. The description reinforces the conceptual meaning of corruption types but adds little beyond the schema's own parameter descriptions.

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's function: intercepting requests and returning malformed or corrupted responses. It also distinguishes this tool from siblings by specifying protocol-level corruption such as malformed JSON, content-length lies, and truncated payloads.

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 provides an explicit use case ('Use to answer: does the app handle...?'), giving clear context for when to apply the tool. It does not explicitly name alternatives or exclusions, but the guidance is sufficiently 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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