US Government Open Data MCP

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

Annotations provide readOnlyHint=true, indicating it's a safe read operation. The description adds valuable context beyond this: it explains the tool's exploratory nature, lists specific actions and their purposes, and provides examples of datasets. However, it doesn't mention behavioral traits like rate limits, error handling, or response format details, which would be helpful given no output schema.

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. It uses bullet points for actions and a clear list for datasets, making it easy to scan. Every sentence adds value without redundancy, and it efficiently covers essential information in a compact format.

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 complexity (5 parameters, multiple actions) and the absence of an output schema, the description does a good job of explaining what the tool does and when to use it. However, it lacks details on return values, error conditions, or pagination, which would be beneficial for completeness. The annotations cover safety, but more behavioral context could be added.

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 parameters well. The description adds semantic value by explaining the purpose of each action (e.g., 'List all available BEA datasets' for list_datasets) and providing a list of dataset examples, which helps the agent understand the context better. However, it doesn't add detailed syntax or format beyond what the schema provides.

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: 'Discover BEA datasets, parameters, and valid parameter values.' It specifies the exact actions (list_datasets, list_parameters, get_values, get_filtered_values) and distinguishes itself from sibling tools by being an exploration tool for the BEA API before making data requests, unlike the sibling tools which appear to be specific data retrieval tools (e.g., bea_gdp_by_state, bea_nipa_underlying_detail).

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 explicitly states when to use this tool: 'Essential for exploring the BEA API before making data requests.' This clearly positions it as a preparatory tool for discovery, implying alternatives are the sibling tools for actual data retrieval. It also lists the actions with their required parameters (e.g., 'requires dataset_name'), providing clear guidance on usage contexts.

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