byok-observability-mcp

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool executes a query and returns a time series, but lacks details on authentication requirements, rate limits, error handling, or what the returned time series structure looks like. For a query tool with no annotation coverage, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two sentences that each serve a distinct purpose: the first states what the tool does, and the second provides usage guidance. There's zero wasted language, and it's front-loaded with the core functionality.

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

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

Given the tool's moderate complexity (query execution with temporal parameters), lack of annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and usage context but lacks details on authentication, error handling, and return format that would be helpful for an AI agent. The high schema coverage helps compensate somewhat.

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

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

The input schema has 100% description coverage, providing clear documentation for all 4 parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain PromQL syntax or provide examples). With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Execute a PromQL range query and return a time series.' It specifies the verb ('Execute'), resource ('PromQL range query'), and outcome ('return a time series'). However, it doesn't explicitly differentiate from its sibling 'prometheus_query' (which likely executes instant queries), though the mention of 'range query' and 'see how a metric changed over time' provides some implicit distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool: 'Use this to see how a metric changed over time.' This indicates it's for temporal analysis rather than single-point queries. However, it doesn't explicitly mention when not to use it or name alternatives (like 'prometheus_query' for instant queries), which would be needed for a perfect score.

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