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 cover safety (readOnlyHint=true, destructiveHint=false). The description adds valuable behavioral context: the tool accesses only 'Public registration sections' and explains the optional SAM.gov API key behavior (spending your own quota and skipping the shared budget). This goes beyond the annotations by disclosing scope and rate-limit considerations, though it doesn't cover all potential response details or error handling.

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 and well-structured: a single opening sentence states the core function and returns, followed by concrete use cases, and a final scope limitation. Every sentence adds value with no fluff or repetition, front-loading the most 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?

The tool has no required parameters and no output schema, so the description must convey what the tool returns and its limitations. It lists the returned data fields and notes the public-only scope, which is sufficient for a simple read-only lookup. It also addresses the API key option, which is relevant for usage context. It might have been improved by explicitly mentioning the response format, but given the low complexity, it is nearly 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%, so the schema already documents all three parameters (UEI, name, sam_api_key) with their descriptions. The tool description adds minimal parameter-specific info beyond restating that lookup can be by UEI or name. It doesn't clarify behavior when both are provided or any precedence, so it stays at the baseline for high schema 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 function: 'Look up a federal contractor registration in SAM.gov by Unique Entity ID (UEI) or legal business name' with a specific verb ('look up') and resource ('federal contractor registration'). It also lists the returned fields (registration status, CAGE code, address, business types, NAICS codes), distinguishing it from sibling tools like find_agency or 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 provides explicit usage scenarios: 'Call this to vet a teaming partner, check a competitor, or confirm an entity is award-eligible.' It also notes a limitation: 'Public registration sections only.' However, it does not explicitly state when not to use it or name alternative tools, missing the full 'when/when-not/alternatives' criterion for 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 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 declare readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read. The description adds valuable behavioral context beyond annotations: the shared daily budget, the option to supply a personal SAM.gov API key, and the 1-year maximum posting window. This helps set rate-limit and data-range expectations.

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 only three sentences: the first states the core purpose and filters, the second gives a direct usage trigger, and the third covers the budget/API key context. Every sentence adds value with no repetition, filler, or 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?

For a 9-parameter tool with no output schema, the description covers the purpose, filter dimensions, usage scenario, and rate-limit behavior well. It doesn't describe the response format or pagination behavior, but the detailed schema and annotations compensate. Slightly more detail on return values would push it higher.

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%, meaning every parameter is already documented in the input schema. The main description adds a high-level summary of filterable fields and mentions the budget/key behavior, but does not significantly augment the per-parameter schema descriptions. Baseline 3 is appropriate given the schema does the heavy lifting.

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 active federal contract opportunities on SAM.gov, enumerates the specific notice types and filter dimensions, and distinguishes it from siblings like search_awards by emphasizing bidding opportunities and market intelligence. The verb 'search' plus the resource 'active federal contract opportunities' is specific and 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?

The description explicitly tells the agent when to call this tool: 'Call this when the user wants to find federal bidding opportunities or market intelligence on what agencies are buying.' This gives a clear use case, but it does not explicitly mention alternatives or when not to use it relative to sibling tools like search_awards, which could cause ambiguity given the inclusion of award notices.

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.