list_metrics

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

With no annotations provided, the description fully discloses behavior: it returns names, API keys, premium requirements, and notes that calculated metrics are NOT returned by the API. It also warns to check live docs if a key is rejected, demonstrating thorough transparency.

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 well-structured with clear paragraphs and front-loaded purpose. Each sentence adds value, though it could be slightly more concise without losing clarity.

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 output schema exists, the description still adds value by explaining the catalog contents and calculated metrics. It covers parameters, return structure, and provides a fallback reference to live docs, making it complete for the agent.

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?

Schema coverage is 0%, so the description fully compensates by listing exact allowed values for network (all social platforms) and scope (profile, post, all) with explanations of their meanings, adding crucial context beyond the schema's type defaults.

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: 'Look up valid Sprout metric keys for a network, so calls don't guess.' It uses a specific verb and resource, and distinguishes itself from sibling tools like get_profile_metrics and get_post_performance, which fetch actual metrics.

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 usage context: specify a network or omit for all, and choose scope for the appropriate endpoint. It implies use before making metric calls, but does not explicitly state when not to use or provide alternative tools.

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