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iamsamuelfraga

Pipedrive MCP Server

by iamsamuelfraga
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

system_get_metrics

Get real-time performance metrics for the MCP server including request counts, error rates, response times, cache and rate limiter statistics. Monitor server health and debug performance issues.

Instructions

Get performance metrics for the MCP server.

Returns comprehensive statistics about the server's performance including:

  • Request counts and error rates

  • Average response times

  • Requests and errors by endpoint

  • Cache statistics (size, hit rate)

  • Rate limiter statistics

Useful for monitoring server health, debugging performance issues, and understanding usage patterns.

Metrics are always fresh (not cached) to provide real-time data.

Response includes:

  • requestCount: Total number of API requests made

  • errorCount: Total number of failed requests

  • totalDuration: Cumulative request duration in milliseconds

  • averageDuration: Average request duration in milliseconds

  • errorRate: Percentage of requests that failed (0.0 to 1.0)

  • requestsByEndpoint: Request count breakdown by endpoint

  • errorsByEndpoint: Error count breakdown by endpoint

  • cacheStats: Current cache size and statistics

  • rateLimiterStats: Rate limiter queue and token information

  • timestamp: Current server time

Common use cases:

  • Monitor server performance

  • Debug slow responses or errors

  • Analyze usage patterns

  • Check cache effectiveness

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.4/5.0
Behavior4/5

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

No annotations, but description confirms non-cached, real-time data and enumerates all returned fields. Safe read operation is implicit. Sufficiently transparent.

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?

Well-structured with bullet points and sections, but slightly verbose. Front-loads purpose effectively.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Completely describes return values, use cases, and caching behavior. No gaps given zero parameters and no output schema.

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?

No parameters; baseline 4 per instructions. Description adds no param info but doesn't need to.

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 gets performance metrics for the MCP server, lists specific metrics and use cases, and is distinct from all sibling tools.

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?

Explicitly states use cases (monitoring, debugging, analysis) and mentions 'Metrics are always fresh' for real-time. Lacks explicit negative guidance or alternatives, but unnecessary given no sibling overlap.

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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