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Search Indian Court Cases

search_indian_court_cases

Search Indian court decisions using exact keywords, case numbers, party names, citations, or judge names. Covers 310M+ records and yields precise matches without consuming AI credits.

Instructions

Keyword and boolean search over the full 310M plus record OpenSearch index of Indian court cases. Fast, exact match, does not consume AI credits. Prefer this over semantic_search_cases for case numbers, party names, citations, judge names and exact phrases. Use semantic_search_cases instead when the request is a natural language question about legal concepts, doctrines or fact patterns rather than exact terms. Note: the caseNumber field is accepted and echoed back but does not actually filter results, put case number text in query instead. Note: sortBy only accepts relevance or date at the validation layer, but the underlying search engine only understands relevance, recent or oldest internally, so date is accepted yet may not reorder results as expected.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number, default 1.
yearNoYear or list of years, each 1947 to the current year, as an integer or a 4 digit string.
courtNoCourt name or list of court names to filter by.
judgeNoAlias for judgeName, see judgeName.
limitNoResults per page, 1 to 100, default 20.
queryYesSearch text: keywords, a phrase, a case number, or a party name.
judgesNoAlias for judgeName, see judgeName.
sortByNoSort order, default relevance. Only relevance and date pass validation here; see the tool description for a real behaviour caveat about date.
toDateNoEnd date, inclusive, YYYY-MM-DD.
caseTypeNoCase type or list of case types to filter by.
fromDateNoStart date, inclusive, YYYY-MM-DD.
judgeNameNoJudge name or list of judge names to filter by. Aliases judges and judge are also accepted; if more than one of judgeName, judges, judge is supplied only the first set one is used, in that order. Use search_judges first to get the exact spelling.
caseNumberNoCase number or list of case numbers. Accepted and echoed back in the response meta.filters, but this does not actually filter results in the current API. Put the case number in query instead.
searchAfterNoDeep pagination cursor. Pass back the JSON encoded pagination.nextCursor array from a previous response. Prefer this over page for paging beyond the first few thousand results.
Behavior4/5

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

With no annotations provided, the description carries the full disclosure burden and reveals important behaviors: fast, exact match, does not consume AI credits, and two known limitations (caseNumber not filtering, sortBy date not reliably reordering). This goes well beyond a typical tool description, though it stops short of describing return format.

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?

The description is dense but efficient: every sentence contributes either purpose, usage guidance, or a caveat. It is front-loaded with the core function and uses clear 'Note:' markers for gotchas.

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?

Given the tool's 14 parameters and absence of an output schema, this description is remarkably complete: it covers what data is searched, performance characteristics, when to choose alternatives, and known quirks. The only minor gap is no explicit mention of response structure, but the schema already handles parameter details.

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?

The input schema already documents all 14 parameters with 100% coverage, so the baseline is 3. The description adds value by explaining the sortBy validation-vs-internal mismatch and reinforcing that caseNumber should be placed in query, giving agents critical operational knowledge.

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 identifies the tool as performing keyword and boolean search over a 310M-record index of Indian court cases. It names the specific resource (OpenSearch index) and distinguishes it from semantic_search_cases, making its purpose unmistakable.

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?

It explicitly instructs when to prefer this tool over semantic_search_cases (for case numbers, party names, citations, judge names, exact phrases) and when to use the alternative (natural language questions about legal concepts). This is textbook usage guidance with named alternatives.

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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