DataLayer MCP

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively reveals key traits: the tool returns scored results based on multiple intent signals and has a cost implication ('5 credits per result'). However, it lacks details on rate limits, authentication needs, error handling, or what 'scored' means operationally (e.g., ranking methodology).

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 extremely concise and front-loaded: the first clause states the core purpose, followed by scoring criteria and cost information. Every sentence earns its place with no wasted words, making it easy for an agent to quickly grasp the tool's function and implications.

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 complexity (9 parameters, no output schema, no annotations), the description is minimally complete. It covers purpose, scoring dimensions, and cost, but lacks details on output format, error conditions, or how results are structured (e.g., pagination behavior implied by parameters but not described). For a tool with significant parameter complexity and no output schema, more contextual information would be beneficial.

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%, so the schema already documents all 9 parameters thoroughly. The description adds no parameter-specific semantics beyond implying that parameters filter or score intent signals (e.g., 'scored by web traffic' relates to 'min_monthly_traffic'). This meets the baseline for high schema coverage but doesn't enhance understanding beyond the schema.

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: 'Find companies showing buying intent' with specific scoring criteria (web traffic, Google ad spend, hiring velocity, employee growth, funding). It distinguishes itself from siblings like 'search_companies' by focusing on intent signals rather than general search, though it doesn't explicitly contrast with all siblings.

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 implies usage for finding companies with buying intent based on specific metrics, but provides no explicit guidance on when to use this tool versus alternatives like 'search_companies' or 'lookup_company'. The cost disclosure ('Costs 5 credits per result') offers some practical context but not comparative usage rules.

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