Federal Grants — find funding you are actually eligible for

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 the 'Never guesses' behavior and the specific NEEDS_REVIEW handling for missing/ambiguous applicant types, which is valuable. It also states the output includes a reason. This goes beyond basic purpose, though it doesn't detail error handling for invalid IDs.

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 two sentences, front-loaded with the core action, and every clause adds value (verdict categories, no-guess behavior). No redundancy.

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 simple tool with 2 parameters and no output schema, the description adequately covers inputs, output format, and an important edge case. It omits potential error states but is otherwise complete for an agent to 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%, so the schema already documents both parameters. The description restates the inputs but doesn't add new parameter-level detail beyond the schema. Therefore baseline 3 is appropriate.

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 uses a specific verb ('return') with a clear resource ('a definitive eligibility verdict') and specifies inputs (opportunity ID, organization type). It clearly distinguishes from sibling tools like find_federal_grants and grant_program_history, which are about discovery and history, not eligibility.

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 states the input conditions ('Given...') and output, making the use case clear. However, it doesn't explicitly mention when not to use it or recommend alternative tools. Since sibling tools are named, it would benefit from an explicit contrast.

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

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

With no annotations provided, the description carries the full burden. It discloses that data is free, requires no key, is live, and uses machine-readable applicant-type codes. It also states what is returned (deadlines, ceilings, cost-sharing, prior winner amounts), adding useful behavioral context beyond the 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 three sentences, front-loaded with the primary value, and every clause adds information. It avoids repetition and is highly scannable.

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?

The tool has four parameters and no output schema, but the description covers key output elements (deadlines, awards, cost-sharing) and the filtering logic. It doesn't fully describe the response format or edge cases like zero results, but the description is strong enough for a search 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?

Schema coverage is 100%, so the baseline is 3. The description adds context by explaining how organizationType maps to Grants.gov's applicant-type code, how keyword affects results (broad terms return more), and the purpose of includeForecasted. This enhances understanding beyond the schema.

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 finds open federal grant opportunities specifically filtered by applicant eligibility, with a concrete verb ('Find') and resource ('U.S. federal grant opportunities'). It distinguishes itself from siblings by emphasizing eligibility-based filtering and providing outcome details like deadlines and award amounts.

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 when to use the tool (when seeking open grants for a specific organization type) and explains the unique filtering approach. It doesn't explicitly mention alternatives or when not to use it, but the eligibility emphasis makes the use case clear.

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

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.