API-Market MCP Server

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 retrieves suggestions, implying a read-only operation, but doesn't cover critical aspects like authentication needs, rate limits, error handling, or what the output looks like (e.g., list of corrections). For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior and constraints.

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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized for a simple tool, though it could be more front-loaded with key details. Every sentence earns its place, but the brevity contributes to gaps in other dimensions rather than optimal 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 tool's moderate complexity (2 parameters, no output schema, no annotations), the description is incomplete. It lacks details on output format, error cases, or behavioral traits, which are crucial for an AI agent to use it correctly. Without annotations or an output schema, the description should provide more context, such as what the suggestions look like or usage prerequisites, but it falls short.

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 description coverage is 100%, with both parameters ('country' and 'q') documented in the schema. The description adds minimal value beyond the schema, as it only generically references 'query parameters' without explaining semantics like how 'country' affects results or what 'q' expects (e.g., misspelled text). Baseline 3 is appropriate since the schema does the heavy lifting, but the description doesn't compensate with additional insights.

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 states the tool's purpose as 'Retrieve spellcheck suggestions based on query parameters', which specifies the verb ('Retrieve') and resource ('spellcheck suggestions'). However, it's somewhat vague about what 'spellcheck suggestions' entails compared to siblings like 'Search_Suggestions' or 'Search_Web', lacking differentiation in scope or output type. It avoids tautology but doesn't clearly distinguish from similar tools.

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 no guidance on when to use this tool versus alternatives. It mentions 'based on query parameters' but doesn't specify contexts, exclusions, or compare to siblings like 'Search_Suggestions' or general search tools. Without explicit when/when-not instructions or named alternatives, usage is implied at best, leaving the agent to infer based on the tool name alone.

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