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prenzio

Rudow MCP Server

by prenzio

metrics_summary

Get a computed health overview for an app: request volume, error rates, latency, top routes, and process metrics to answer how it performs or whether it is healthy.

Instructions

Computed health overview from telemetry: request volume, error rate (4xx/5xx), average and p95 latency, the top routes by traffic and errors, and the latest process metrics (memory, event-loop lag, uptime). Call this to answer how the app is performing or whether it is healthy.

Input Schema

NameRequiredDescriptionDefault
minutesNoWindow size in minutes (default 60).
project_idYesThe project to query. Call list_projects first to see which ids you can use.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that the tool computes a read-only summary and lists what the response includes, but it does not mention permissions, freshness/staleness, rate limits, or whether the summary is cached.

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 two sentences and front-loads the returned metrics before the usage guidance. The metric enumeration is dense but each item earns its place by telling the agent what to expect in the response.

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?

Because there is no output schema, the description needs to explain return values, and it does so by enumerating the main metrics returned. It remains somewhat incomplete about response shape, units, and how the time window affects each metric, but it is sufficient for selecting and calling the tool.

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 coverage is 100%, so project_id and minutes are already documented, including the default window and the requirement to call list_projects. The description adds no parameter-level meaning beyond the schema, which is the expected baseline when the schema does the heavy lifting.

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 gives a specific verb/resource combination: a computed health overview from telemetry, and enumerates the exact metrics included (volume, error rate, latency, top routes, process metrics). It is clear what the tool produces, but it does not explicitly distinguish itself from close siblings like metrics_overview or metrics_routes.

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?

It clearly states when to use the tool: to answer how the app is performing or whether it is healthy. It does not name alternatives or exclusions, so the agent must infer that route-specific or trend-specific siblings serve narrower questions.

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