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JustlyAI

DocketBird MCP Server

by JustlyAI

docketbird_get_document_text

Read-onlyIdempotent

Extract plain text from court filings for reading, summarizing, or quoting content. Supports pagination through long documents with offset and max_chars parameters.

Instructions

Get the extracted plain text of a court filing.

Returns what the document says (for summarizing, quoting, comparing) rather
than its metadata or PDF — those come from docketbird_get_document /
docketbird_download_document.

Availability varies: some documents aren't downloaded yet, some are scans
with no text layer, some docket entries are text-only stubs with no
document. When no text is available this returns a clear message — the PDF
may still be retrievable via docketbird_get_document.

When to use:
- Reading, summarizing, or quoting a filing's contents
- Pulling the complaint's text after docketbird_get_case points to it

Args:
    document_id: DocketBird document ID (e.g., 'txwd-1:2022-cv-00398-00177')
    offset: Character offset into the text to start from (for paging
            through long documents; default 0).
    max_chars: Maximum characters to return (default 50000, max 200000).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
offsetNo
max_charsNo
document_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds behavioral context about text availability (scans, text-only stubs, not downloaded) and explains that a clear message is returned when text is unavailable, without contradicting 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 sections and bullet points, front-loaded purpose. While slightly long, every sentence adds value, and no unnecessary words are present.

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 (3 parameters, output schema exists), the description covers purpose, usage guidelines, parameter semantics, availability nuances, and sibling differentiation. It is fully sufficient for an agent to select and invoke correctly.

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?

Schema coverage is 0%, but the description fully explains each parameter: document_id with example, offset for paging, max_chars with default and maximum. This adds essential meaning not present in 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 gets extracted plain text of a court filing and distinguishes it from sibling tools that return metadata or PDFs (docketbird_get_document, docketbird_download_document). It uses specific verbs and resources.

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?

Explicit 'When to use' section lists reading/summarizing/quoting and pulling text after docketbird_get_case. Also discusses availability limitations and when other tools are appropriate, providing clear context for 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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