GAIIA Expert Proxy (MCP Server)

interrogate_endpoint

Discover the schema of any REST endpoint by intelligently probing it with a reinforcement learning loop. Automatically identifies request parameters and response structure.

Instructions

Intelligently interrogates a REST endpoint to discover its schema via reinforcement learning loop.

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": {
      "url": "<url>",
      "method": "<method>"
    },
    "name": "interrogate_endpoint"
  }
}

Input Schema

Name	Required	Description
`url`	Yes	The full URL of the endpoint.
`method`	Yes
`auth_header`	No	Optional Authorization header.
`base_payload`	No	Optional base JSON payload.
`extra_headers`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0May 26, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions a 'reinforcement learning loop' but does not explain its implications (e.g., time consumption, network activity, state changes). The agent cannot assess safety or side effects.

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 sentence, which is concise and front-loaded. However, the phrase 'intelligently' and 'reinforcement learning loop' could be seen as slightly verbose. Overall, it efficiently conveys the core action.

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 complex behavior (RL loop, output schema discovery) and lack of output schema, the description is incomplete. It does not explain the loop's mechanics, return format, or termination conditions, leaving the agent without crucial context.

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 has 5 parameters with 60% description coverage. The description does not elaborate on parameters beyond the schema. Though the schema provides some meaning (e.g., url, auth_header), the description adds no extra value for parameter comprehension.

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 purpose: it intelligently interrogates a REST endpoint to discover its schema via reinforcement learning. The verb 'interrogate' and resource 'REST endpoint' are specific, and the mention of schema discovery differentiates from the listed sibling tools.

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 provides no guidance on when to use this tool versus alternatives, such as before integrating with an unknown API. It lacks explicit context, prerequisites, or exclusion criteria, leaving the agent to infer usage from the description alone.

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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