AwardCast Federal Contracts

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 describes the content returned but does not disclose behavioral traits such as data freshness, read-only status, pagination, or any limitations. The description is not misleading, but it lacks transparency about how the tool behaves beyond its output coverage.

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: the first lists the tool's outputs and the second states when to use it. Every sentence is useful, there is no redundancy, and the key information is front-loaded.

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 tool with one parameter and no output schema, the description adequately covers what the tool does and when to use it. It lists the main data points but does not specify return format or any caveats, though that is not a major gap for this straightforward profile 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 description coverage is 100% because the only parameter 'agency' has a description indicating both name and slug forms work. The tool description adds no additional meaning to the parameter, so the baseline 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 defines the tool as a 'buying profile of a US federal agency' and enumerates the specific data points it provides (contracts awarded, obligations, competition share, typical price per NAICS, largest contractors, expiring contracts). This is a specific verb+resource description that distinguishes it from sibling tools like contractor_profile.

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 gives explicit usage guidance: 'Call this when the user asks how an agency buys, what it pays, or who already sells to it.' This provides clear context for when to invoke the tool, though it does not mention exclusions or alternatives, which prevents 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It lists what data is returned but does not explain behaviors such as the time range for 'recent awards', handling of invalid UEIs, or any side effects. It is accurate but not deeply transparent.

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 long, front-loaded with the tool's output content, and includes a direct usage directive. It contains no filler or repetition, making every sentence valuable.

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 single-parameter tool with no output schema, the description sufficiently covers both the return content and the user-intent context. It explains what the agent will get (award counts, totals, agencies, etc.) and when to invoke it (competitor, incumbent, market winners), which is complete for its simplicity.

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%. The 'uei' parameter is already fully described in the schema, including format and how to obtain it if only the company name is known. The tool description adds no additional parameter semantics, so the baseline score of 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 defines the tool's purpose: providing federal award history for a contractor, with specific metrics (contract count, total obligated, median win value, agencies, recent awards, set-aside status). This is distinct from sibling tools like agency_profile, which focuses on agencies.

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?

Explicit usage triggers are given: 'Call this when the user asks about a competitor, about the incumbent on a contract, or who has already won in a given market.' However, it does not mention when not to use it or name alternatives beyond the implicit contrast, so it lacks the full when/when-not/alternatives structure.

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?

No annotations are provided, so the description carries the full burden. It implies a read-only retrieval action through 'open' and describes the content of the returned records, which adds behavioral context. However, it does not explicitly state whether the operation is safe, idempotent, or requires permissions, and does not disclose any side effects or rate limits. The examples of record contents are useful but fall short of full transparency.

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 exactly two sentences. The first sentence states the core purpose with illustrative examples, and the second provides a direct usage directive. No filler or redundancy exists; every word contributes to understanding the tool's role.

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?

This is a simple single-parameter tool with a clear output schema, so the description doesn't need to detail return values. It sufficiently explains when to use the tool and what kind of information can be expected. The examples of record types give context beyond the schema. It lacks explicit limitations or when-not-to-use guidance, but for a fetch-by-id utility, the coverage is strong.

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 input schema already provides 100% coverage for the single 'id' parameter, describing it as 'The exact id returned by search, unchanged.' The description reinforces this by mentioning 'an id returned by search' and advising to call before citing a number, but it does not add new syntactic or semantic details beyond what the schema already states. Baseline of 3 is appropriate given full 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: 'Open the full record behind an id returned by search.' It provides concrete examples of record types (agency profile, contractor award history, solicitation) and explicitly positions it as the detail-retrieval counterpart to search. This distinguishes it from sibling tools like agency_profile or contractor_profile, which may focus on specific entity types.

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 gives explicit usage context: 'Call this whenever you need the detail behind a search result, and always before citing a number.' This tells the agent when to use the tool. However, it does not mention when not to use it or name alternative tools explicitly, so it lacks the full exclusions/alternatives that would merit 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?

With no annotations, the description carries the burden. It transparently names the data source (SAM.gov), the data fields, and a clear limitation (no price forecast). It does not mention potential caveats like data freshness or that sector/agency 'hunting' is not filterable via parameters, but for a read-only list, the transparency is solid.

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, each purposeful: the first defines the tool's output, the second gives usage triggers, the third states a limitation and redirects. No fluff, well front-loaded.

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 list tool, the description covers the essential context: what is returned, when to use, and a key limitation. However, it could more explicitly differentiate from sibling tools like 'upcoming_recompetes' and clarify that sector/agency filtering isn't a direct parameter. Still, it is more than adequate.

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 input schema already fully documents the only parameter (limit) with bounds and default. The description goes further with usage guidance ('Use 5 to 10 for an exploratory question, and the ceiling only when the user asked for the full list'), which adds real value 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 returns US federal solicitations currently open, sourced from SAM.gov, with specific fields (agency, NAICS, place of performance). It distinguishes itself from siblings by emphasizing 'OPEN right now' and the bidding context.

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 tells when to call this tool: when the user asks what is open, hunts for opportunities in a sector/agency, or wants to know what they can bid on. It also provides an explicit alternative for price forecasts by pointing to an external URL, which counts as a when-not-to-use.

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 behavioral burden. It discloses that the data is aggregated history, not a forecast, and points to the paid forecast offering. It does not mention edge cases like no data availability, but for a simple read-only lookup, the provided context is solid.

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 the core purpose, followed by usage and limitation. Every sentence adds value with no redundancy or fluff. Highly concise and well-structured.

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 (two required params, no output schema), and the description explains what it returns, when to use it, and its limitations. It is complete for the tool's complexity without over-explaining.

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 baseline is 3. The description adds minimal param semantics beyond the schema, only referring to the pair generically. The schema already provides full descriptions for both agency and naics, so the description is not required to compensate.

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 provides historical closing-price range for an agency and NAICS pair, with specific metrics (10th percentile, median, 90th percentile, average competing offers). It distinguishes itself from siblings by focusing on price benchmarking and explicitly clarifying it is not a forecast.

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 says when to call this tool ('when the user asks what a contract in this market usually goes for, or wants a benchmark before pricing a bid') and what not to use it for (forecasts are paid and located elsewhere). This gives clear usage guidance and 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?

With no annotations provided, the description carries the burden. It discloses the return format (results with an id and citable URL) and the recommended follow-up action, which are key behavioral traits. It does not explicitly state read-only safety, but the nature of a search tool and the focus on returning results makes the absence less critical. Overall, it provides useful behavioral context beyond the bare fact of searching.

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 extremely concise—two sentences that front-load the purpose and then provide usage guidance and output behavior. Every sentence contributes unique value without redundancy or 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?

For a search tool with two simple parameters, an output schema, and a clear workflow, the description is fully complete. It explains what to search, when to use it, what to expect in results, and how to proceed. No significant gaps remain that would hinder agent selection or invocation.

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 description coverage is 100%, with both 'query' and 'limit' clearly explained including examples and constraints. The description adds no new parameter-level meaning; it only reinforces the general scope of what can be searched. This meets the baseline for high schema coverage, but does not elevate beyond it.

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 ('Search') and clearly identifies the resource (AwardCast's US federal contracting record), specifying categories (buying agencies, contractors, solicitations). It also distinguishes itself from siblings by presenting itself as the first call for procurement questions, and the note about passing IDs to the fetch tool helps differentiate workflow from 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 gives explicit guidance on when to use: 'Call this first for any question about who buys, who sells, or what is open in US federal procurement.' It also provides a clear alternative pathway by instructing to pass the returned id to the fetch tool for full details, indicating how this tool relates to others.

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 the tool's function (scoring forecasts) and the dependency on FPDS publication timing. It also specifies the return content (coverage and median error by value band), making the behavior transparent for a read-only analysis tool, though it stops short of explicitly stating it is read-only.

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 tool's purpose, then provides usage context and return details. Every sentence earns its place with no wasted words.

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 absence of annotations, output schema, and parameters, the description is complete. It covers what the tool does, when to use it, what it returns, and the FPDS data dependency, giving an agent everything needed to invoke 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?

The tool has zero parameters, so there is no schema to clarify. The description does not need to add parameter information, and the baseline for 0 parameters is 4.

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 the tool as an accuracy scorecard for AwardCast's forecasts, scored against real contract values. This distinguishes it from sibling tools like agency_profile and contractor_profile, which focus on entity profiles rather than forecast reliability.

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 call the tool: when users ask about forecast reliability, margin of error, or evidence before trusting a number. It does not mention alternatives or exclusions, but the context is unambiguous and sufficient.

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 transparency burden. It clarifies the data nature (pre-solicitation opportunities) and notes each record includes incumbent and current contract value. However, it doesn't explicitly state whether the operation is read-only or mention return format/pagination, leaving a slight gap.

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 information-dense. Four sentences cover purpose, output contents, when to call, and sibling distinction without any fluff or repetition.

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 tool's simplicity (one parameter, no output schema), the description fully covers what the tool returns, when to use it, and how it differs from a key sibling. No critical context is missing.

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% for the only parameter (limit), with the schema describing it as 'How many contracts to return, 1 to 50.' The description adds no additional parameter semantics, so the baseline of 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 returns US federal contracts nearing expiry that are likely to be recompeted, before they become published solicitations. It uses a specific verb ('returns') and resource ('upcoming recompetes'), and explicitly distinguishes itself from sibling open_solicitations.

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 provides explicit when-to-use guidance: 'Call this when the user plans pipeline, asks what is coming, or wants to position ahead of competitors.' It also gives a direct comparison to open_solicitations, indicating when NOT to use it (when opportunities are already open for bids).

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