Databento MCP

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'List of unit prices showing cost per GB or per record,' which adds some behavioral context. However, it doesn't cover critical aspects like whether this is a read-only operation, rate limits, authentication needs, or error handling. For a tool with zero annotation coverage, this is insufficient.

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 sections (purpose, parameters, returns, example) and front-loaded key information. It's concise at four sentences, but the 'Parameters' section repeats schema details unnecessarily, slightly reducing efficiency.

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 low complexity (1 optional parameter, no output schema, no annotations), the description is moderately complete. It covers purpose, parameters, and returns, but lacks behavioral details like safety or performance. Without annotations or output schema, it should do more to compensate, making it adequate but not fully comprehensive.

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 the optional 'dataset' parameter. The description repeats this information in the 'Parameters' section without adding meaning beyond what the schema provides (e.g., no examples of valid datasets beyond 'GLBX.MDP3'). Baseline 3 is appropriate when the schema does the heavy lifting.

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: 'Get current pricing information per dataset/schema combination.' It specifies the verb ('Get') and resource ('pricing information'), and distinguishes it from siblings like 'get_cost' by focusing on unit prices per dataset/schema rather than overall costs. However, it doesn't explicitly differentiate from all siblings, so it's not a perfect 5.

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 context with 'Helps understand pricing before querying,' suggesting it's for pre-query planning. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_cost' or 'list_datasets,' and doesn't specify exclusions or prerequisites. This makes it adequate but with gaps.

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