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JustlyAI

DocketBird MCP Server

by JustlyAI

docketbird_ask_litigation_graph

Read-onlyIdempotent

Ask natural-language questions about relationships between parties, attorneys, law firms, judges, and courts in federal civil cases. Returns entity IDs and connections.

Instructions

Ask a natural-language question of DocketBird's litigation graph: parties, attorneys, law firms, judges, courts, and their connections.

This is the ONLY source of party/attorney/firm/judge relationships — the
docket endpoints do not return them. Examples: "What attorneys appeared
for Google in the Northern District of California?", "Every case where
Firm A appeared opposite Firm B", "What judges has Quinn Emanuel appeared
before?".

COVERAGE CEILING — read before trusting absence: the graph covers federal
civil cases active in DocketBird's data flows since July 2025 (roughly 30%
of federal civil cases). No criminal, bankruptcy, or state-court matters.
Zero records means "not in the graph," NEVER "no such cases exist" — do
not present an empty result as a finding that something doesn't exist.

Behavior:
- Slow: responses can take 10-25 seconds (an AI model interprets the
  question, then queries the graph).
- Result shape varies with the question; entity IDs accompany names.
- At most 200 records per response; 'truncated' means more matches exist —
  narrow the question to see the rest.
- Attorney email addresses are never included. This tool reports exactly
  what the API returned — it never invents contact details.

Args:
    question: The natural-language question (max 1000 characters).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds substantial behavioral context: slow 10-25s response, variable result shape, 200-record limit with truncated flag, no email addresses, and no invented data. No contradictions 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.

Conciseness4/5

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

The description is well-structured with clear sections (overview, coverage, behavior, args) and front-loaded with purpose. While every sentence adds value, it could be slightly more concise (e.g., the coverage ceiling explanation is repeated in two different ways).

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?

Given the tool's complexity (NL query over graph, variable output), the description covers key expectations: coverage limits, slowness, result shape, truncation, and data policies. The existence of an output schema (mentioned in context) reduces the need for exhaustive return field documentation.

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

Parameters5/5

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

The single parameter 'question' has 0% schema description coverage. The description compensates fully by specifying it is natural-language, max 1000 characters, and provides usage context through examples. This adds clear meaning beyond the schema.

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 answers natural-language queries about the litigation graph, listing entities (parties, attorneys, etc.) and explicitly distinguishes it from siblings: 'This is the ONLY source of party/attorney/firm/judge relationships — the docket endpoints do not return them.' The verb 'ask' and resource 'litigation graph' are specific.

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?

The description provides explicit guidance on when to use the tool (for relationship queries), includes examples, and explains coverage limitations and empty result interpretation. It indirectly tells when not to use (not for criminal/bankruptcy/state) and names alternatives (docket endpoints).

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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