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JustlyAI

DocketBird MCP Server

by JustlyAI

docketbird_list_court_systems

Read-onlyIdempotent

List all federal and state court systems covered by the DocketBird API, returning human-readable names and court counts. Use a court_system_id to browse the courts within a system.

Instructions

List every court system DocketBird covers (live from the API).

Returns the federal system plus each state court system, with a
human-readable name and how many covered courts each holds. Use a
court_system_id with docketbird_list_courts(court_system=...) to browse the
courts inside a system, including unlisted courts that don't appear in the
default listing. Changes rarely; safe to cache.

When to use:
- Discovering what state-court coverage exists
- Getting the identifier to browse one system's courts

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

The description adds the behavioral trait 'Changes rarely; safe to cache' beyond the annotations (readOnlyHint, idempotentHint, etc.). It also notes the data is live from the API, giving a complete picture of freshness and cacheability. 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.

Conciseness5/5

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

The description is front-loaded with the primary purpose, uses bullet points for usage guidance, and every sentence adds relevant information. It is concise without being terse.

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 zero parameters and an output schema, the description fully covers what the tool does, when to use it, and how to interpret and apply the results. No gaps remain.

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 tool has zero parameters, so baseline is 4. The description adds value by explaining the output structure (federal + state systems with names and court counts) and how to use the returned identifiers with docketbird_list_courts.

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 lists every court system DocketBird covers, specifying the federal system and state court systems with human-readable names and court counts. It distinguishes from sibling tools like docketbird_list_courts by explicitly mentioning how to use a court_system_id to browse courts within a system.

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 includes a 'When to use' section with concrete scenarios: discovering state-court coverage and getting identifiers to browse courts. It also references docketbird_list_courts as the follow-up tool, providing clear context and differentiation.

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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