Federal Grant Evidence

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

The description adds useful behavioral context beyond annotations: the preview is 'always free' and it 'explicitly marks unresolved project relevance, document, or award-history evidence.' This helps an agent set expectations without contradicting the 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?

Three dense sentences with no filler: the first front-loads the exact use case, the second adds behavioral value, and the third lists exclusions. 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?

Combined with the output schema and annotations, the description is complete enough for an agent to select and invoke the tool correctly. It specifies the input context, free preview behavior, evidence-marking behavior, and off-limit use cases, leaving no critical ambiguity.

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?

The schema describes opportunity_id as a numeric Grants.gov id from search_federal_grants, and the description echoes 'official opportunity record.' However, the nested organization object's fields such as can_meet_cost_share and requested_amount_usd are not given semantic meaning in the description, and schema coverage is only 50%, so more parameter-level explanation would be beneficial.

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 states a specific action and resource: a 'sourced go/no-go pursuit screen for one U.S. federal grant' using a non-personal organization profile and the official opportunity record. It also distinguishes itself from discovery tools by explicitly ruling out 'discovery only' use.

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 states when to use the tool ('when the user needs a sourced go/no-go pursuit screen') and lists clear exclusions: not for discovery only, legal eligibility advice, award prediction, drafting, or submission. This gives an agent strong routing guidance relative to search_federal_grants and other potential alternatives.

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?

Adds substantial operational context beyond annotations: beta status, guaranteed no-payment behavior, global daily capacity, the fact that only newly released complete results count against capacity, and request_id reuse for retries. These operational traits (payment, rate limiting, retry semantics) are exactly the kind of behavioral detail annotations cannot convey, and nothing contradicts the idempotentHint=true / readOnlyHint=false 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?

Four sentences, each carrying distinct information: purpose and usage trigger, payment safety, capacity and retry semantics, and exclusion boundaries. Purpose is front-loaded and there is zero redundancy with the schema or annotations.

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?

With an output schema present and annotations covering idempotency, the description covers the critical operational concerns: when to use it, payment safety, capacity constraints, retries, and non-goals. Minor gaps remain — what happens when daily capacity is exhausted and a gloss for the jargon 'hard-gate' — but nothing that would prevent an agent from invoking it 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 description coverage is 67%, leaving the nested organization object undocumented in the schema. The description adds only marginal parameter meaning: it reinforces request_id retry semantics already stated in the schema, but gives no guidance on opportunity_id or the organization sub-fields. The schema carries most of the parameter explanation burden.

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?

States a specific verb and resource: retrieving a 'complete, versioned hard-gate and award-history evidence pack for one U.S. federal grant.' It is scoped to a single grant and explicitly differentiates from the search/analyze siblings by positioning itself as the post-search, pack-delivery step.

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?

Provides an explicit trigger condition ('after search or preview when the user wants a complete... evidence pack') and a strong exclusion list ('does not evaluate semantic project relevance, determine legal eligibility, predict an award, draft, or submit'). However, it never names the sibling tools (analyze_federal_grant, search_federal_grants) that would handle those excluded cases, so routing is implied rather than explicit.

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?

Annotations already indicate this is idempotent and non-destructive. The description adds useful context about returning official identifiers and source URLs, and clarifies that results cover current or forecasted opportunities. It does not disclose pagination behavior, data freshness limits, or any access requirements, though those are less critical for a non-destructive search 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 compact and front-loaded with the primary use case, followed by the key output and a short exclusion list. Every sentence contributes value, and there is no redundant restatement of the title or schema.

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 two-parameter search tool with a rich schema, annotations, and an output schema, the description covers purpose, output nature, and non-use cases. The only meaningful gap is the lack of explicit guidance on when to use the sibling tools instead, which keeps it from being fully complete.

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%, and the schema already documents query as a topic or phrase and limit as a bounded integer. The description adds no additional parameter-level meaning or examples beyond the schema, so a baseline score of 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 clearly identifies a specific action (find/search), a specific resource (current or forecasted U.S. federal grant opportunities), and the key output (official identifiers and source URLs). It is more specific than the tool title and gives enough for an agent to understand the core function, though it does not explicitly distinguish itself from the sibling tools.

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 opens with a direct usage trigger ('Use this when the user needs to find...') and adds clear when-not-to-use boundaries for eligibility, award prediction, application writing, or submission. It does not name alternatives like analyze_federal_grant or get_federal_grant_evidence_pack, so it falls just short of full routing guidance.

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