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get_mcp_usage

Track and analyze MCP server and tool usage frequency across Cursor and Claude Code sessions to monitor AI coding analytics.

Instructions

Get MCP (Model Context Protocol) tool usage: which MCP servers and tools are being used, and how often.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
startDateNoStart date. Formats: "YYYY-MM-DD", "7d", "30d", "today", "yesterday". Default: "30d"
endDateNoEnd date. Formats: "YYYY-MM-DD", "today", "yesterday". Default: "today"
usersNoComma-separated emails to filter by specific users
Behavior2/5

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

With no annotations, the description carries full burden but only states what data is retrieved without behavioral details. It lacks information on permissions needed, rate limits, response format, pagination, or whether this is a read-only operation (implied by 'Get' but not explicit).

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?

The description is a single, efficient sentence that front-loads the core purpose ('Get MCP tool usage') and elaborates concisely ('which MCP servers and tools are being used, and how often'). Every word contributes value with no redundancy.

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?

For a read operation with 3 parameters and 100% schema coverage, the description is minimally adequate but lacks output details (no schema provided) and behavioral context. It covers the 'what' but not the 'how' or 'when', leaving gaps in usage guidance and transparency.

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 100%, so the schema fully documents parameters (startDate, endDate, users). The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for high coverage.

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 verb ('Get') and resource ('MCP tool usage'), specifying what data is retrieved ('which MCP servers and tools are being used, and how often'). It distinguishes from sibling tools like get_agent_edits or get_model_usage by focusing on MCP-specific usage metrics.

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?

No guidance is provided on when to use this tool versus alternatives like get_daily_usage or get_usage_events. The description implies usage tracking but doesn't specify scenarios, prerequisites, or exclusions, leaving the agent to infer context from tool names 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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curl -X GET 'https://glama.ai/api/mcp/v1/servers/ofershap/cursor-usage'

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