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mcp_usage

Read-only

Track AI agent activity on Metricairn: tool-call counts, error rates, and recent questions over a selected number of days.

Instructions

How AI agents are using this MCP server: tool-call counts, error rates, and recent questions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnly, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds what data is reported (counts, error rates, recent questions), which is useful context beyond annotations, but it does not disclose aggregation level, permissions required, or how 'recent questions' are defined.

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, well-structured sentence that front-loads the purpose and lists the key metrics. No filler or redundant information.

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?

An output schema exists, so the description need not explain return values, and the low complexity (one optional parameter, no nesting) keeps the description from being inadequate. However, the missing semantics for 'days' and the absence of any usage context leave clear gaps for an agent deciding whether and how to call the tool.

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

Parameters1/5

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

The schema has a single parameter 'days' with 0% description coverage, and the tool description does not mention it at all. The description therefore fails to compensate for the missing parameter documentation, leaving the agent without any indication of what the time window controls or how its default of 30 is used.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the resource (MCP server usage) and the specific metrics reported (tool-call counts, error rates, recent questions). It is readily distinguishable from sibling tools, which deal with product analytics rather than the MCP server's own usage. However, it lacks an explicit verb and does not name any sibling alternative.

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?

The description implies when the tool is useful—when an agent wants to see how it and others are using the server—but gives no explicit when-to-use or when-not-to-use guidance. No alternative tools are mentioned, nor are prerequisites or edge cases.

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