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Find the case to cite for a proposition

find_authority
Read-onlyIdempotent

Find the case to cite for a proposition. Paste the proposition or the language you need authority for VERBATIM; do not convert it to keywords. The result names the source case to cite (source_case), the verbatim passage, and how many courts have adopted it (quoted_by_cases); match_kind says how it matched: exact_phrase (the passage contains your words), relaxed (it shares your distinctive words), proposition (a rule later courts cite that case for). A miss means no court has quoted or been cited for that wording; try find_issues for the doctrine, or search_cases with 3-4 distinctive words. Routing: a proposition or quoted language -> find_authority (pasted verbatim); a doctrine name or fact pattern -> find_issues; a party name, statute number or keywords -> search_cases. Quotation marks mean pasted from a tool result (opinion text, passage, best_match). If you typed a quote from memory, run verify_quote and use best_match. Run check_brief on the finished draft and apply its corrections. Copy cite_as for the citation (first page, then pin). The first 3 rows carry star_page and cite_as. A quotation inside the proposition (in quotation marks) is also searched on its own: passages that contain it word for word come first, match_kind quotation (matched_quotation names it). It also finds, by meaning, the PAGES later courts cite for a paraphrased proposition (match_kind cited_for_label: pin page, cite_as with the pin, a generated label of what the page is cited for, how many courts cite it, and the treatment of those citations), and returns string_cite: those pages one per case in Bluebook order with "(cited for …)" parentheticals to rewrite from the opinion. Up to 3 semantic_case rows at the end are whole opinions closest in meaning to the proposition: leads to read, not quotations. Every row carries match_sentence (the passage sentence or label that matched) and closeness: verbatim | close | related, measured on wording and meaning (related = it shares the subject and may paraphrase the rule or only touch it: read it before citing); rows only loosely related are left out. no_close_authority: true means no row matches the proposition closely in wording: read the related rows' match_sentence before citing one, or use search_cases with distinctive words, find_issues, or a rephrased proposition.

Input Schema

NameRequiredDescriptionDefault
stateNoOptional two-letter state code: relaxed and proposition matches are limited to that state's courts and the federal courts (a federal court sitting in the state cites both), that state's own courts first; other states' courts are left out (falls back to all courts when none qualify); verbatim exact-phrase matches from any court are kept, that state's first
propositionYesThe proposition or quoted language, verbatim (three or more words; up to ~300 characters are used)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / state / description
      Previous value: -"Optional two-letter state code: relaxed and proposition matches are limited to that state's courts (falls back to all courts when it has none); verbatim exact-phrase matches from any court are kept, that state's first"New value: +"Optional two-letter state code: relaxed and proposition matches are limited to that state's courts and the federal courts (a federal court sitting in the state cites both), that state's own courts first; other states' courts are left out (falls back to all courts when none qualify); verbatim exact-phrase matches from any court are kept, that state's first"
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only cover the safety profile (readOnly, idempotent, closed-world); the description adds substantial behavior the annotations cannot express: match_kind semantics (exact_phrase/relaxed/proposition/quotation/cited_for_label), the no_close_authority flag, closeness tiers, semantic_case lead rows, and that exact-phrase matches survive state filtering.

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

Conciseness3/5

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

Front-loaded with purpose and the verbatim rule, and most sentences carry real output semantics, but it is a single ~350-word wall of text covering many match kinds without structural separation (no bullets/headers), making it hard to scan. The volume is defensible given no output schema, but the presentation is dense.

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

Completeness5/5

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

With no output schema, the description carries the full burden of explaining returns and does so thoroughly: source_case, quoted_by_cases, match_kind variants, string_cite, semantic_case rows, match_sentence, closeness, and the no_close_authority signal, plus fallback actions on a miss.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3, but the description adds a genuinely operative instruction the schema lacks: paste the proposition VERBATIM and do not convert it to keywords. The state parameter's scoping is documented in the schema itself, so the description's contribution is the verbatim-input rule.

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?

States a specific verb and resource ('Find the case to cite for a proposition') and immediately distinguishes itself from siblings find_issues and search_cases via explicit routing rules. An agent can tell exactly what class of input this tool consumes (verbatim propositions/quotes) versus its alternatives.

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

Usage Guidelines5/5

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

Gives explicit when-to-use and when-not-to-use guidance with named alternatives: proposition/quoted language -> find_authority, doctrine/fact pattern -> find_issues, party name/statute/keywords -> search_cases. It also covers the miss case and the pasted-vs-typed-quote distinction (verify_quote for quotes from memory).

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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