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by com.sagots
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assistants_preview

Check an OAuth authorization request from an AI assistant (Claude, ChatGPT, Gemini or any MCP client) before consent: returns the assistant's name, the address it returns to, the scopes requested in plain words, or the problem with the 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": {
      "request": "<request>"
    },
    "name": "assistants_preview"
  }
}

Input Schema

NameRequiredDescriptionDefault
requestYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the return contents (assistant name, return address, scopes in plain words, problems) and implies a read-only validation. It doesn't state failure modes beyond 'the problem with the request', but that is sufficient for a check operation.

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?

One well-formed sentence that front-loads the action and enumerates what the tool returns. No waste.

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 single-parameter tool with no output schema, the description covers what the tool does, when to use it, and what it returns. It could mention error handling or authentication requirements, but it is adequate.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. The description names the main fields returned and the input is a single 'request' object whose subfields are documented in the schema. It doesn't explain every subfield, but it covers the essential semantics of the request check.

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 states a specific verb and resource: 'Check an OAuth authorization request from an AI assistant'. It clearly distinguishes this from siblings like assistants_authorize or assistants_list by describing the pre-consent check and the return contents.

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 phrase 'before consent' gives clear context for when to use the tool. It does not explicitly name alternatives like assistants_authorize, but the timing cue ('before consent') is a strong usage signal.

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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