atom-mcp-server

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

Annotations declare read-only/idempotent safety; the description adds critical return value structure (vendor/model/SKU counts, price quartiles, modality breakdown) compensating for the missing output schema. Notes aggregation scope but omits data freshness or rate limiting details.

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?

Every sentence earns its place: purpose declaration, return value specification (essential without output schema), filtering behavior, and targeted examples. No redundant or filler text.

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?

For a 2-parameter tool with complete annotations, the description adequately compensates for the missing output schema by detailing return statistics (median, quartiles, breakdowns) and covers the free-tier limitation via the schema description. No gaps remain.

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?

With 100% schema coverage establishing a baseline of 3, the description adds value by specifying that modality acts as a 'filter' (changing the aggregation scope), adding semantic meaning beyond the schema's 'focus on' language.

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 opens with 'Aggregate AI inference market intelligence' (specific verb + resource) and distinguishes from siblings by emphasizing aggregate metrics (counts, distributions, quartiles) rather than individual model/vendor retrieval like get_model_detail or search_models.

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 three concrete examples showing when to use the tool (market overview, text pricing stats, image generation stats), establishing clear context. Lacks explicit 'when not to use' guidance or named sibling alternatives, preventing a 5.

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