MacTech Federal Market

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

Annotations already cover readOnly, openWorld, idempotent, and destructive safety hints. The description adds 'Keyless' (no authentication required) and clarifies the output mapping to toptier/subtier names, which are meaningful beyond the structured annotations. No contradiction exists.

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 fluff. The first sentence states the core purpose and output, the second provides usage guidance and 'Keyless' note. Information is front-loaded and every word 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 lookup tool with strong annotations, the description is complete: it explains the operation, expected output (toptier/subtier names), and usage context. No output schema is present, but the return value is conceptually described. The guidance also covers when not to use it.

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%, with the parameter described as 'Agency name fragment' and examples 'navy' or 'DISA'. The description does not add extra parameter semantics beyond the schema, so the 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 uses the specific verb 'resolve' and clearly identifies the resource as 'federal agency name fragment' with the output being 'official awarding-agency names (toptier and subtier)'. It also distinguishes itself from sibling tools by referencing search_awards and spending_by_category, making its purpose unambiguous.

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?

Explicitly states 'use before search_awards or spending_by_category when unsure of the exact agency name', providing clear when-to-use context and naming alternative tools. This fully addresses usage guidance.

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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety. The description adds the keyless nature and clarifies the input can be either a description or a code, which is useful beyond annotations. It does not contradict 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 entire description is three short sentences, front-loaded with the purpose, followed by usage context, and ending with 'Keyless.' Every sentence adds meaningful information with no fluff.

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 is simple with one parameter and no output schema. The description covers purpose, usage, and keyless access, giving the agent everything needed to select and invoke it correctly. It also contextualizes within the sibling toolset.

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 query parameter description already explains the accepted inputs: 'Industry description ("cybersecurity services") or a NAICS code ("541512")'. The tool description repeats this without additional semantics, 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 uses specific verbs 'Resolve' and 'look up' with the resource 'NAICS codes', clearly stating the tool's dual functionality. It also distinguishes from siblings by positioning it as a preparatory step before search_opportunities/search_awards.

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 explicitly states when to use the tool: 'Use before search_opportunities/search_awards to get the right code.' This provides clear context and names the relevant sibling tools, giving the agent actionable guidance.

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 cover read-only, open-world, idempotent, and non-destructive behavior. The description adds value by explicitly disclosing 'Keyless' (no authentication required) and enumerating the returned data categories (description, obligations, period of performance, agencies, recipient, place of performance), which are not in 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?

The description is a single, well-structured sentence that front-loads the action and resource, then efficiently lists the included data fields. No redundant words or filler; every clause contributes to understanding the tool's purpose and output.

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 one parameter, full schema coverage, and no output schema, the description carries the burden of explaining return content. It lists the major field groups and notes the keyless nature, which is sufficient for an agent to understand the tool's scope and expected output.

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 already describes award_id as 'generated_internal_id / generated_unique_award_id from search_awards' with 100% coverage. The description adds a concrete example ('CONT_AWD_...') and reiterates the source context, providing a small but useful semantic boost 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 verb ('Get'), the resource ('one federal award from USAspending.gov'), and the input key ('generated award id'), with an example format. It also explicitly positions itself as a detail-retrieval complement to search_awards, making it distinguishable from siblings.

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: after obtaining a generated award ID from search_awards results. It does not explicitly name alternatives or state when not to use it, but the workflow context is clear enough for an agent to select it appropriately.

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 declare readOnly/openWorld/idempotent/non-destructive, so the bar is lower. The description adds real behavioral context beyond them: scope is limited to 'public registration sections only' and the call requires the caller's own SAM.gov API key via header or argument. It omits rate-limit or pagination behavior.

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, front-loaded with the resource and identifiers, then use cases, then constraints. Every clause carries distinct information; nothing is redundant.

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 no output schema, the description compensates by enumerating the returned fields. The auth requirement and the public-sections scope are both stated, so an agent has everything needed to invoke and interpret this 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 all three parameters are already documented, including the header fallback and key-acquisition link. The description only adds the UEI-vs-name precedence hint, which is already implied by the schema's 'used when no UEI given'. 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?

Specific verb (look up) plus resource (federal contractor registration in SAM.gov) with the key identifiers (UEI, legal business name) stated up front. It enumerates the returned fields and is clearly distinct from siblings like get_award or find_naics.

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?

Explicitly names three concrete use cases: vetting a teaming partner, checking a competitor, confirming award eligibility. It does not name a sibling alternative or state when NOT to use it, but the sibling set is disjoint enough that routing is unambiguous.

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 declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds 'Keyless and unlimited,' which addresses authentication and rate limits, providing valuable behavioral context beyond 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?

The description is concise: one sentence for purpose, one for filters, one for use cases. It front-loads the core action and every sentence adds value without unnecessary detail.

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 10 parameters and no output schema, the description covers the essential purpose, filters, and use cases. It hints at return fields ('who won what, from which agency, for how much') but does not detail result structure or pagination. Still, it is sufficient for a read-only 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 60%, so the description partially compensates for the missing param descriptions. It maps filters like 'NAICS, PSC, award amount' to the schema fields, giving meaning to otherwise undocumented params such as naics_codes, psc_codes, and min/max_amount.

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 searches actual federal contract awards on USAspending.gov, listing specific filters. It distinguishes from siblings by emphasizing 'actual' awards, contrasting with search_opportunities and get_award.

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 explicitly provides use cases: competitive intelligence, incumbent research, and agency spending patterns. It does not explicitly mention when not to use it or alternatives, but the contexts are clear enough for an agent to decide.

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 cover the safety profile (readOnly, idempotent, non-destructive, open world). The description adds genuinely new context beyond them: the required caller-owned SAM.gov API key, the error behavior when absent, and the 1-year date window cap. It stops short of describing result shape or pagination.

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 sentences, front-loaded with what the tool does, then when to use it, then the auth prerequisite. Dense but each sentence carries distinct information; minor overlap between the enumerated notice types and the notice_type enum.

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 9-parameter, optional-only-param search tool with no output schema, the description covers purpose, usage trigger, filterable dimensions, and the critical auth requirement. Only real gap is that it does not sketch the return shape despite there being no 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 every parameter is already documented in the schema, including enum code mappings and the date-window limit. The description restates filter categories (NAICS, set-aside, notice type, agency, dates) without adding syntax or defaults beyond the schema; 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?

States a specific verb (Search) and resource (active federal contract opportunities on SAM.gov), then enumerates the notice categories covered. This clearly separates it from siblings like search_awards (awards) and find_agency/find_naics (reference lookups).

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?

Gives a clear trigger condition: 'Call this when the user wants to find federal bidding opportunities or market intelligence on what agencies are buying.' No explicit when-not or named alternative (e.g., use search_awards for past awards), which keeps it short of a 5.

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 cover read-only, open-world, idempotent, and non-destructive behavior. The description adds 'Keyless' (no authentication) and clarifies that a single call returns aggregation rollups, which goes beyond the structured annotations without contradicting them.

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 with practical examples, front-loading the core aggregation behavior. Every phrase earns its place, with no filler or redundant repetition of schema details.

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 7 parameters, no output schema, and strong annotations, the description covers primary use cases and key filters. It lacks explicit return shape (though 'rollups' and 'top N' imply grouped totals) and filter interaction rules, but is sufficiently complete for typical market-sizing queries.

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 only 43%, so the description compensates by mapping category to NAICS/PSC/recipient/awarding agency and mentioning filter options (keywords, NAICS, agency). It adds meaning to parameters that lack schema descriptions, though it does not specify filter combination logic.

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 aggregates federal contract spending by NAICS, PSC, recipient, or awarding agency over a date range. It distinguishes itself from siblings like search_awards by framing it as 'market sizing in one call' with concrete examples.

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 tool provides clear context for when to use it, such as 'top contractors in NAICS 541512 last year' and 'which agencies buy the most cybersecurity', indicating market-sizing scenarios. However, it does not explicitly name alternatives or state when not to use this tool versus search_awards.

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