Agent Feedback Loop

get_tool_quality

Retrieve performance metrics for MCP tools, including success rates, quality scores, and recent feedback to evaluate reliability.

Instructions

Get quality metrics for a specific MCP tool.

Shows success rate, average quality score, and recent feedback.

Args: tool_name: Name of the tool to check

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": {
      "tool_name": "<tool_name>"
    },
    "name": "get_tool_quality"
  }
}

Input Schema

Name	Required	Description	Default
`tool_name`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Apr 2, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively compensates by specifying exactly what the tool shows: 'success rate, average quality score, and recent feedback.' However, it omits details about data freshness, caching, or any rate limiting.

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 efficiently organized into three distinct parts: purpose, return value details, and argument documentation. However, the 'Args:' formatting is slightly informal/awkward compared to natural language integration, preventing a perfect score.

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?

Given the low complexity (single string parameter, no nested objects) and lack of output schema, the description adequately covers the essential information: what the tool does, what it returns, and what input is required. It appropriately compensates for the sparse schema without being overly verbose.

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 0% (the tool_name property lacks a description field). The description compensates minimally with 'Name of the tool to check,' which provides basic semantics but lacks format details, examples, or validation rules that would fully address the schema gap.

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 'Get[s] quality metrics for a specific MCP tool' with a specific verb and resource. It distinguishes itself from siblings like get_best_tools and get_trending_tools by emphasizing this retrieves metrics for a 'specific' tool rather than listing multiple tools.

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

Usage Guidelines3/5

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

While the description implies usage by specifying 'specific MCP tool' (suggesting use when analyzing one tool rather than browsing), it lacks explicit guidance on when to choose this over get_best_tools or report_tool_result. No prerequisites or exclusion criteria are mentioned.

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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