Resolve a reporter citation (e.g. '347 U.S. 483', '347 U. S. 483', '384 U.S. 436') to the case it identifies. Matches official and parallel citations. Returns the case metadata including its CAP id for use with caselaw_opinion_text.
| Name | Required | Description | Default |
|---|---|---|---|
| citation | Yes | A reporter citation, e.g. '347 U.S. 483'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the read-only, idempotent, non-destructive nature of the tool. The description adds behavioral scope: it matches official and parallel citations and acknowledges spacing variants. It also reveals that the CAP id is the actionable output, which is useful context beyond the annotations. It does not discuss not-found or ambiguity behavior, but for a safe lookup this is a minor 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?
Three short, high-signal sentences: the first states the action with examples, the second defines matching scope, and the third explains the output and its downstream use. No filler or redundant restatement of the tool name.
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, read-only, idempotent lookup with full schema coverage, the description is complete. It tells the agent what input to provide, what matching behavior to expect, and what to do with the output CAP id. Even without an output schema, it names the key return field needed for the follow-up 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?
There is only one parameter and the schema already documents it with an example, so the baseline applies. The description adds a few extra example formats and says official/parallel citations match, but it does not materially extend what the schema already conveys.
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 states a specific action ('Resolve a reporter citation'), names the target resource, supplies concrete examples, and clarifies the output (case metadata with a CAP id). It also indirectly distinguishes itself from caselaw_opinion_text by positioning this tool as the resolver that feeds that sibling.
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 makes the input condition clear: the agent should call this when it has a reporter citation and needs case metadata or a CAP id. It also points to the downstream sibling caselaw_opinion_text. It does not explicitly exclude alternatives like caselaw_search or court_citation_resolver, but the single-parameter design makes the intended use obvious.
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.