competlab-mcp-server

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

No annotations provided, so description carries full burden. Discloses return limit ('up to 200 data points'), computation differences ('pre-computed aggregate summaries' vs 'computes from raw per-provider results'), safety ('Read-only'), and return format ('JSON array', 'Dates are ISO-8601'). Rich behavioral context.

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?

Eight information-dense sentences with zero waste. Front-loaded with purpose, followed by limits, behavioral details, usage guidelines, and format specifications. No redundancy with schema field descriptions.

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?

No output schema exists; description compensates with return type ('JSON array') and data limits. Complex filtering behavior (provider vs aggregate) is fully explained. Could be improved by describing the structure of returned data points, but adequate for tool selection.

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 has 100% coverage (baseline 3). Description adds crucial semantic meaning by explaining how the provider parameter affects computation method (aggregate pre-computed vs raw per-provider), which is not evident from the schema enum alone.

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?

Description opens with specific verb 'Get' + resource 'AI Visibility trend data' + scope 'over time' and 'LLM brand perception changes'. Explicitly distinguishes from siblings get_ai_visibility_dashboard (latest snapshot) and get_ai_visibility_check_detail (specific check).

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?

Provides explicit when-to-use guidance: 'Use this for time-series analysis; use get_ai_visibility_dashboard for the latest snapshot or get_ai_visibility_check_detail for a specific check.' Names exact alternatives for different use cases.

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