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Find Related Cases

find_related_cases

Find all documents sharing the same case number and get a derived procedural timeline. Ideal for understanding the full scope of a legal case.

Instructions

Finds other documents sharing the same case number as the given case, plus a derived procedural timeline. No AI credits consumed. This is NOT similarity search, it only follows the shared case number; for conceptually similar cases use semantic_search_cases instead. Returns relatedDocuments (each with id, title, caseNumber, court, decisionDate, caseType, isCurrent), capped at 50, and timeline entries (date, status, statusLabel, documentId), where status is one of Case Initiated, Hearings / Orders, or Final Judgment. Only documents that have both a decision date and a stored PDF appear in the timeline. meta carries caseNumber, totalDocuments and timelineEvents.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesCase identifier: either a 24 character MongoDB ObjectId hex string, or a case number string. Lookup order: the server first tries to parse this as an ObjectId; only if that parse fails does it fall back to looking up by case number. This means a syntactically valid but nonexistent ObjectId returns 404 without ever trying the case number path.
Behavior5/5

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

With no annotations provided, the description carries the full burden and does it well. It discloses that no AI credits are consumed, explains the 50-result cap, inclusion criteria for timeline entries, and provides the return structure with status enums. This goes well beyond a basic 'finds related documents'.

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 structured and every sentence earns its place. It covers purpose, exclusions, limitations, and return details without fluff, making it dense but immediately scannable.

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?

There is no output schema, so the description must explain the return value, and it does comprehensively: the relatedDocuments array with fields, timeline entries with statuses, inclusion criteria, and meta fields. This makes the tool fully usable without external documentation.

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?

The schema description already fully covers the single parameter's format and lookup order (100% coverage). The tool description adds no additional parameter-specific semantics, so baseline 3 is appropriate.

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 states the tool finds documents sharing the same case number and derives a procedural timeline. It distinguishes itself from semantic search, providing a specific verb and resource.

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?

Explicitly explains when not to use it (not similarity search) and directs users to semantic_search_cases for conceptually similar cases. This gives clear usage context and 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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