Search federal and state court opinions by keyword, court, judge, party name, or date range. Returns case summaries with citations.
| Name | Required | Description | Default |
|---|---|---|---|
| court | No | Court ID, e.g. 'scotus', 'ca9', 'nysd'. | |
| judge | No | Judge name filter. | |
| limit | No | Max results (1-50, default 10). | |
| party | No | Party name filter. | |
| query | No | Free-text search query. | |
| date_filed_after | No | ISO date YYYY-MM-DD. | |
| date_filed_before | No | ISO date YYYY-MM-DD. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already disclose read-only, open-world, idempotent, and non-destructive behavior. The description adds one useful behavioral fact—returns summaries with citations rather than full text—but it does not cover pagination, default limits, coverage limitations, or how strictly filters are applied. No contradiction with 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?
Two short, front-loaded sentences with no filler. Every phrase carries information about scope, filters, or 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?
The description states the search surface and return type, but given a large sibling set of court tools and no output schema, it leaves the tool's relationship to court_opinion_search, court_citation_resolver, and court_docket_lookup unexplained. An agent might select the wrong tool without additional disambiguation.
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?
With 100% schema description coverage, the schema documents all seven parameters. The description echoes the filter dimensions (keyword, court, judge, party, date range) but adds no additional parameter-level meaning such as syntax, defaults, or interaction between filters.
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?
States a specific action ('Search') and resource ('federal and state court opinions'), with explicit filter dimensions. However, it does not distinguish itself from sibling court_opinion_search or court_citation_resolver, so an agent cannot tell which legal-search tool to pick based on this description alone.
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 it—when searching opinions by keyword, court, judge, party, or date range—but it does not mention alternatives or exclusion cases. There is no explicit guidance such as 'for full opinion text use court_opinion_search' or 'for citation lookup use court_citation_resolver.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.