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Stratalize Governance

get_federal_court_cases

Read-only

Use when screening a company, executive, vendor, or counterparty for federal litigation exposure before a contract award, acquisition, investment, board appointment, or enterprise partnership. Returns active and historical federal court dockets across all US district and appellate courts — case names, docket numbers, courts, filing dates, nature of suit, and active status. Example: Acme Corp — 4 active federal cases: patent infringement N.D. Cal. (filed 2023), FLSA collective action S.D.N.Y. with 847 plaintiffs (filed 2023), FTC antitrust investigation D.D.C. (filed 2024), securities class action S.D.N.Y. (filed 2024) — aggregate litigation liability exposure estimated above $200M. Source: CourtListener, 1M+ federal court documents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
courtNoCourt identifier e.g. ca9, scotus, dcd, nyed, ndca
party_nameYes
years_backNo

TDQS

A4.1/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, so the description adds significant context: it details the output (case names, docket numbers, courts, filing dates, nature of suit, active status), provides an illustrative example with estimated liability, and names the data source (CourtListener, 1M+ documents). This exceeds the baseline expected given annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the use case and returns information. The example is long but adds concrete value, and the source attribution is useful. It is somewhat verbose but every sentence earns its place; only minor trimming could improve conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, output fields, use context, and data source, which is strong for a read-only screening tool. However, it lacks parameter semantics and does not mention any limitations (e.g., coverage gaps, rate limits), so it is not fully complete given the low schema coverage and absence of an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33% (only 'court' has a description). The description does not explain 'party_name' or 'years_back', and no parameter details are given beyond the example. With low schema coverage, the description should compensate but does not, leaving the agent unclear on required format or defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Use when screening a company, executive, vendor, or counterparty for federal litigation exposure' and specifies the resource: 'federal court dockets across all US district and appellate courts.' This distinguishes it from siblings like OFAC or SAM exclusion screening tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description opens with explicit 'Use when...' guidance, listing concrete scenarios (contract award, acquisition, investment, etc.). It does not explicitly mention when not to use or alternative tools, but the context is clear enough for an agent to differentiate from sibling tools.

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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TDQS

A4.1/5.0
Disambiguation5/5

Every tool targets a distinct regulatory domain or data source—from OFAC sanctions to CRA ratings to NIST AI RMF—with clear boundaries. Even the three screening tools (OFAC, OIG, SAM) differ by governing agency and list, and their descriptions explicitly disambiguate them.

Naming Consistency5/5

All tools follow the consistent lower_snake_case pattern 'get_<domain>_<focus>', such as get_ofac_sanctions_screening and get_us_state_ai_legislation. There are no mixed conventions, vague verbs, or unexpected abbreviations.

Tool Count4/5

18 tools is slightly above the ideal 3-15 range but appropriate for a broad governance data server covering federal, state, and international regulatory sources. Each tool corresponds to a meaningful dataset, so the count feels justified rather than padded.

Completeness4/5

The surface covers a wide array of governance and compliance domains, including AI regulation, financial enforcement, sanctions, and legal screening, with no critical dead ends for typical lookups. However, it lacks some common regulatory areas (e.g., SEC, HIPAA, GDPR) and offers only read-only access, which is acceptable but not exhaustive.

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