What a federal grant program has actually awarded before, by CFDA/assistance-listing number: median award, largest award, and recent recipients from USAspending.gov. Use it to sanity-check whether a request is realistic — a $150k ask against a program with a $40M median means competing with major institutions.
| Name | Required | Description | Default |
|---|---|---|---|
| cfda | Yes | CFDA / assistance listing number, e.g. '93.243'. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that this is a historical read from USAspending.gov and lists the specific data returned. It does not mention limitations like missing data or errors, but the read-only nature and purpose are evident.
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?
Two sentences with no filler. The first defines the tool's output and data source, the second provides a powerful usage example. Every sentence earns its place.
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?
For a simple one-parameter tool with no output schema, the description adequately explains what is returned (median, largest, recipients) and how to interpret it. It is complete enough for an agent to invoke and use correctly.
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 coverage is 100% and the description repeats the CFDA/assistance-listing example already in the schema. It does not add meaning beyond the structured field definition, so baseline 3 applies.
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 shows historical award data for a federal grant program by CFDA number, including median award, largest award, and recent recipients. This distinguishes it from sibling tools by focusing on past awards rather than eligibility or finding grants.
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?
It explicitly says to use it to sanity-check whether a request is realistic, providing a concrete scenario. It does not name alternatives or state when not to use it, but the context is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool addresses a distinct aspect: checking a specific opportunity, searching eligible opportunities, and examining historical awards. No overlap exists between them.
Two tools follow a clear verb_noun pattern (check_grant_eligibility, find_federal_grants), while grant_program_history uses a noun phrase. The style is consistent with snake_case and descriptive names, but the slight deviation prevents a perfect score.
Three tools are well-scoped for the domain, covering search, eligibility verification, and historical context without redundancy. The count fits the server's focused purpose.
The tool surface fully supports the stated purpose: find eligible grants, check a specific one, and validate funding expectations. No critical gaps or dead ends exist for this workflow.