Skip to main content
Glama
JustlyAI

DocketBird MCP Server

by JustlyAI

docketbird_fulltext_search

Read-onlyIdempotent

Search the body text of court filings across all US courts and cases. Find documents matching specific terms, phrases, or legal citations.

Instructions

Full-text search of the BODIES of court filings across DocketBird's entire document index — all courts, all cases, not just your account.

This searches what the filings actually say, not what they are titled. To
match docket-entry titles within one case, use docketbird_search_documents.

When to use:
- Scanning recent filings for a company, industry, or legal topic
  (e.g. new suits naming a target company — set sort='recency')
- Finding every filing whose text mentions a term, phrase, or citation
- Practice research inside your firm's own cases (set my_cases_only=True)

Scope boundary: with my_cases_only=False (default) this is cross-corpus
RESEARCH over public court records. With my_cases_only=True it is limited
to cases associated with your firm's account (resolved from your API key).

Query syntax:
- space / and: all terms must appear.  or: either term.
- -term: exclude (the word 'not' is NOT supported).
- term* or term!: word endings (end of word only).
- /n, /s, /p: terms within n words / same sentence / same paragraph.
- "...": exact phrase. Email addresses and legal symbols (§, ¶) work.

Pagination contract (upstream cursors, bounded requests):
- next_cursor is the ONLY end-of-results signal. A page may hold fewer
  than `size` documents — even zero — while more remain, because
  restricted documents are removed after matching. Keep following the
  cursor until it is null. The 'found' count may include documents that
  will never be returned. Result window is the first 10,000 matches —
  narrow with court_id/case_id/dates if you need the deep tail.

Args:
    query: Full-text query (max 500 chars), e.g. '"summary judgment" and forfeit* -insurance'
    court_id: Comma-separated court restriction; each entry may be a slug
              ('nysd'), an abbreviation ('S.D.N.Y.'), or a full court name.
    case_id: Restrict to a single case by DocketBird case ID.
    filed_after: Only documents filed on/after this date (YYYY-MM-DD).
    filed_before: Only documents filed on/before this date (YYYY-MM-DD).
    my_cases_only: True = only your firm's cases (practice scope);
                   False = the whole corpus (research/marketing scope).
    sort: 'relevance' (default) or 'recency' (most recently filed first).
    size: Results per page (default 25, max 50).
    cursor: Pagination cursor from a previous response's next-page note.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNo
sortNorelevance
queryYes
cursorNo
case_idNo
court_idNo
filed_afterNo
filed_beforeNo
my_cases_onlyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description discloses important behavioral details: cross-corpus vs. account-scoped search, pagination behavior (cursor, how to handle empty pages, result window limit of 10,000), and the fact that found count may be inaccurate due to removed documents. These enrich the agent's understanding of the tool's actions and constraints.

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 (intro, differentiation, when-to-use, scope, query syntax, pagination, args). It is relatively long but every sentence serves a purpose, given the complexity of the tool (9 parameters, query syntax, pagination rules). Minor redundancy (scope boundary repeats some of 'when to use') prevents a perfect score.

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 (9 parameters, query syntax, pagination, scope boundaries), the description covers all necessary aspects: purpose, usage, parameter semantics, behavioral constraints, and edge cases. An output schema exists but is not shown; still, the description provides sufficient context for an AI agent to invoke the tool correctly without needing further clarification.

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 input schema has 0% description coverage, so the description must fully compensate. It provides an extensive 'Args' section that explains every parameter with meaning, constraints (e.g., query max 500 chars, date format YYYY-MM-DD, court_id formats), defaults, and examples (e.g., query: '"summary judgment" and forfeit* -insurance'). This adds substantial value 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 it performs full-text search of the bodies of court filings across the entire DocketBird index, and explicitly distinguishes itself from the sibling tool docketbird_search_documents by contrasting what is searched (text content vs. docket entry titles). This meets the standard of specific verb+resource with sibling differentiation.

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

Usage Guidelines4/5

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

The description provides explicit scenarios for when to use the tool (e.g., scanning recent filings, finding documents mentioning a term, practice research with my_cases_only=True) and contrasts it with docketbird_search_documents. It lacks a direct 'when not to use' statement, but the given guidance is clear and helpful for decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/JustlyAI/docketbird-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server