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Search US Court Opinions

legal.caselaw.search
Read-onlyIdempotent

Search US federal and state court opinions — filter by court (scotus, ca9, dcd...), date range, relevance. Largest free US case law archive. 7,000+ AI-related opinions (CourtListener / Free Law Project)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoSearch type: "o" (opinions, default) or "r" (RECAP dockets)
courtNoCourt filter (e.g. "scotus" for Supreme Court, "ca9" for 9th Circuit, "dcd" for DC District)
limitNoNumber of results (default 10, max 20)
queryYesSearch query for US court opinions (e.g. "artificial intelligence liability", "Fourth Amendment digital privacy")
order_byNoSort: relevance (default), newest, oldest
filed_afterNoOnly cases filed after this date (YYYY-MM-DD)
filed_beforeNoOnly cases filed before this date (YYYY-MM-DD)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior2/5

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

Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior. Description adds minimal behavioral context (only 'largest archive' claim). No mention of rate limits, pagination, or other constraints.

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

Conciseness5/5

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

Two sentences: front-loaded with action and resource, no wasted words. Efficient and easy to parse.

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

Completeness3/5

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

Output schema exists, so description need not explain return values. Provides archive size and AI-opinion count as context. Lacks guidance on when to use, but covers basic search and filter capabilities adequately.

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

Parameters3/5

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

Input schema covers 100% of parameters with descriptions. Description repeats filter categories (court, date, relevance) but adds no meaning beyond the schema. Baseline 3 is appropriate.

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

Purpose4/5

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

Clearly states the verb 'Search' and resource 'US court opinions' with scope (federal and state) and filter capabilities (court, date, relevance). Does not explicitly differentiate from sibling tools like legal.caselaw.dockets but is sufficiently specific.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives (e.g., legal.caselaw.dockets for dockets, legal.caselaw.opinion for a single opinion). No when-not or alternative tool references.

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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