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 key behavioral traits: the matching algorithm ('contains' matching), a minimum character requirement ('2 char min'), and the data source ('GSA CALC+ data'). However, it doesn't mention rate limits, error handling, or response format, which are gaps for a tool with no annotations.

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 highly concise and front-loaded: two sentences with zero waste. The first sentence states the purpose and scope, and the second provides usage guidelines and behavioral details. Every sentence earns its place by adding critical information.

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 moderate complexity (2 parameters, no annotations, no output schema), the description is largely complete. It covers purpose, usage, and key behavior. However, without annotations or an output schema, it lacks details on response format, error cases, or performance constraints, which slightly reduces completeness.

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 both parameters fully. The description adds some context by mentioning 'labor categories, vendor names, or contract numbers' (mapping to the 'field' enum) and giving examples like 'soft' for software categories and 'Booz' for Booz Allen (illustrating 'prefix'). This provides marginal value beyond the schema, meeting the baseline for high 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: 'Autocomplete/suggest values for labor categories, vendor names, or contract numbers in GSA CALC+ data.' It specifies the verb ('autocomplete/suggest'), resource ('values'), and scope ('GSA CALC+ data'), and distinguishes it from the sibling tool 'calc_search_rates' by explaining its usefulness for finding exact values to use in that tool.

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 explicit usage guidance: 'Useful for finding exact values to use in calc_search_rates.' It names the alternative tool ('calc_search_rates') and clarifies when to use this tool (to find values for that tool). It also includes a constraint: 'Uses 'contains' matching (2 char min).'

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