Accuracy scorecard for AwardCast's forecasts, scored against the real contract value once FPDS publishes it. Call this when the user asks whether the forecasts are reliable, what the margin of error is, or wants evidence before trusting a number. Returns coverage and median error by value band, including where the model is weakest.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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.
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Each tool targets a distinct resource or action: agencies, contractors, solicitations, historical pricing, forecast accuracy, and pre-solicitation opportunities. Search and fetch are clearly the entry point and detail retriever, with no overlap.
All tool names use lowercase snake_case and follow a consistent descriptive pattern, whether noun-based (agency_profile, price_band) or verb-based (search, fetch). No stylistic mixing or ambiguous abbreviations.
Eight tools is well-suited to a federal contracts research domain, covering discovery, detail, profiles, opportunities, pricing, and reliability. Each tool earns its place without redundancy or bloat.
The surface covers the core read-only workflows: search, retrieve, compare agency/contractor profiles, find open and upcoming opportunities, benchmark prices, and verify forecast reliability. The only notable gap is that direct forecast output is intentionally excluded (paywalled), but the toolset fully supports the stated free data services.
