US Government Open Data 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 data is from an 'annual survey (June 30)', indicating it's historical and not real-time, which is useful behavioral context. However, it lacks details on permissions, rate limits, error handling, or response format (e.g., pagination behavior beyond the schema's limit/offset), leaving gaps for a mutation-free read 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 front-loaded with the core purpose in the first sentence, followed by filtering context and use cases in two additional sentences. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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, no output schema, no annotations), the description is reasonably complete. It covers purpose, data source, filtering, and use cases. However, it lacks details on output format (e.g., what fields are returned) and behavioral aspects like rate limits, which would be needed for full completeness in the absence of annotations and output schema.

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 thoroughly (e.g., filters with examples like 'STALP:"NY"', sort_by with fields like 'DEPSUMBR'). The description adds marginal value by mentioning 'Filter by state or institution', which aligns with the filters parameter but doesn't provide additional syntax or format details beyond the schema. 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 explicitly states the tool's purpose: 'Get Summary of Deposits — branch-level deposit data from annual survey (June 30). Shows deposit amounts at each bank branch.' It specifies the verb ('Get'), resource ('Summary of Deposits'), and scope ('branch-level deposit data'), clearly distinguishing it from sibling tools like 'fdic_financials' or 'fdic_search_institutions' which serve different purposes.

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 when to use the tool: 'Useful for market share analysis and banking access by geography.' It also mentions filtering capabilities ('Filter by state or institution'), which guides usage. However, it does not explicitly state when not to use it or name specific alternatives among siblings, preventing a score of 5.

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