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 adds valuable context beyond the input schema: it mentions caching behavior ('bypass cache'), preview capabilities ('preview query without executing'), and practical advice ('start with small date ranges'). However, it doesn't cover aspects like rate limits, error handling, or authentication needs, which are relevant for a data retrieval 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 and front-loaded with the core purpose, followed by examples and tips that earn their place by illustrating usage and providing practical advice. Each sentence adds value, and there is no redundant information, making it efficient and easy to scan.

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 8 parameters, 100% schema coverage, and no output schema, the description is mostly complete. It covers purpose, examples, and behavioral tips, but lacks details on return values (e.g., data format, pagination) and error scenarios, which would be helpful for an agent invoking this tool.

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?

The input schema has 100% description coverage, providing detailed parameter information. The description adds minimal semantic value beyond the schema, as it primarily restates parameter usage in examples (e.g., 'dataset="GLBX.MDP3"') without explaining nuances. The baseline score of 3 is appropriate since 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 with a specific verb ('Retrieve') and resource ('historical market data for symbols from Databento'), and it distinguishes itself from siblings like 'get_live_data' by specifying historical data. The examples reinforce this purpose with concrete use cases.

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 through examples (e.g., for ES futures trades, AAPL OHLCV bars) and tips (e.g., start with small date ranges), but it lacks explicit guidance on when to use this tool versus alternatives like 'get_live_data' or 'get_dataset_range'. No clear exclusions or prerequisites are stated.

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