Databento 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 describes what the tool detects and returns (e.g., quality score, issues list), which adds useful context beyond basic functionality. However, it lacks details on performance aspects like rate limits, error handling, or computational intensity, which are important for a data analysis tool.

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 bullet points and an example, making it easy to scan. It is appropriately sized, with each sentence adding value, such as listing detections and returns. However, the example could be more concise, and some information might be slightly redundant given the schema coverage.

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 of a data quality analysis tool with no annotations and no output schema, the description provides a good overview of what it does and returns. However, it lacks details on output structure (e.g., format of the 'list of issues'), error cases, or dependencies, which would enhance completeness for an agent's understanding.

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 input schema already documents all parameters thoroughly. The description adds minimal parameter semantics beyond the schema, only implying in the example that parameters like 'dataset', 'symbols', 'start', and 'end' are used for analysis. No additional syntax or format details are provided, meeting the baseline for high schema coverage.

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 with specific verbs ('analyze', 'detect') and resources ('data quality', 'market data'), and distinguishes it from siblings by focusing on quality analysis rather than data retrieval or job management. It explicitly lists what it detects: time gaps, price outliers, null values, and duplicate records.

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 clear context for usage through an example that references a sibling tool ('First retrieve data with get_historical_data'), implying this tool should be used after data retrieval. However, it lacks explicit guidance on when not to use it or alternatives, such as comparing it to 'quick_analysis' or other analysis tools.

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