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AE Test Bridge MCP

AE Test Bridge MCP

by NeuralIO444
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

set_default_response

Set a fallback JSON response for incoming requests that do not match any queue_response rule, enabling testing of bridge client behavior.

Instructions

Set the response returned when no queue_response rule matches an incoming request.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "response": "<response>"
    },
    "name": "set_default_response"
  }
}

Input Schema

NameRequiredDescriptionDefault
responseYesThe JSON object to send back for any unmatched request.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4/5.0
Behavior3/5

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

No annotations exist, so the description carries the full disclosure burden. It states the core behavior (sets a fallback response for unmatched requests) but does not disclose whether repeated calls overwrite an existing default, whether the default persists across requests, or how to unset/reset it. For a mutation tool this is a meaningful but not critical 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?

A single, front-loaded sentence that leads with the action ('Set the response returned') and then states the trigger condition. No filler or redundancy; every clause carries information.

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?

For a one-parameter setter with full schema coverage and no output schema, the description covers the essential call semantics. Missing only the overwrite/persistence behavior of repeated calls and how to clear the default, which have no dedicated sibling tool and are not otherwise documented.

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% and the sole parameter is already fully described in the schema as 'The JSON object to send back for any unmatched request'. The description reinforces that the response parameter is the fallback payload, but adds no format, constraint, or type detail beyond what the schema provides.

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?

States a specific verb ('Set') plus a well-defined resource: 'the response returned when no queue_response rule matches an incoming request'. Naming queue_response by name distinguishes this fallback mechanism from the rule-matching sibling, so an agent can select it without ambiguity.

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 condition 'when no queue_response rule matches an incoming request' gives clear context: this is the catch-all fallback, to be configured alongside queue_response rules. It names the relevant alternative and the trigger condition, though it does not explicitly state when-not-to-use this tool or instruct the agent to prefer queue_response for rule-based matching.

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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