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Glama

get_performance_metrics

Retrieve current operational metrics, including operation counts, timings, and stats, to monitor performance and identify trends.

Instructions

Get current performance metrics.

Returns: Dict with: operation stats, counts, timings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.1

TDQS

A3.8/5.0
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 the return shape (a dict with operation stats, counts, timings) and implies a read-only operation via 'Get', but it does not explicitly state side-effect-free behavior, whether metrics are reset, or what 'current' means temporally. Adequate for a simple getter but not rich.

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 concise and front-loaded: the action and resource appear in the first sentence, and the return format follows immediately. Every sentence earns its place with no fluff or repetition.

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?

For a parameterless metrics-getting tool with an output schema available, the description is sufficiently complete. The main gap is the absence of usage context (e.g., when to call this vs. analyzing detailed reports), but the core invocation needs are covered.

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?

The tool has zero parameters, so there is no parameter ambiguity. The description correctly focuses on return value rather than inputs, which meets the baseline for a parameterless tool.

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 retrieves 'current performance metrics' and specifies the return contents: operation stats, counts, and timings. This verb+resource pairing is distinct from the memory-management siblings, leaving no ambiguity about what the tool does.

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 given on when to use this tool versus alternatives or when to prefer it. There are no explicit conditions, prerequisites, or exclusions. The intended use is arguably implied by the name, but the description does not actively route an agent.

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