Skip to main content
Glama
turingmindai

TuringMind MCP Server

Official
by turingmindai

turingmind_request_approval

Request human approval for critical spec nodes when confidence is low or surface changes are high-risk. Ensures human oversight before system-wide execution.

Instructions

Request human approval for a SpecNode. Only use for: (1) L0/L1 spec approval before system-wide execution, (2) low-confidence nodes (< 0.6) after repair cycles, (3) high-risk surface changes (api_endpoint or security_checks failing). For everything else, the engine runs autonomously.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonYes
contextYesSummary of the situation requiring approval
node_idYes
Behavior3/5

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

With no annotations, description must disclose behavior. It mentions human approval but lacks details on blocking/async nature, state changes, or permissions needed. Adequate but not thorough.

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?

Two sentences, front-loaded with purpose and clear usage rules. No redundant text.

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?

Given no output schema and moderate complexity, description is incomplete: does not explain what happens after request (e.g., blocking, return value) or provide more detail on context parameter. Adequate for basic use.

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?

Description maps reason enum values to scenarios, adding value over schema. But node_id and context are not explained beyond minimal schema descriptions. Schema coverage is 33%, so description partially compensates.

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 requests human approval for a SpecNode and lists three specific use cases, distinguishing it from siblings.

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

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (three conditions) and when not to use ('for everything else, the engine runs autonomously'), providing clear guidance on alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/turingmindai/turingmind-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server