datajud-mcp

Get case by CNJ number

get_case

Read-onlyIdempotent

Fetch public cover data for a Brazilian court case from CNJ's DataJud by CNJ number, returning class, subjects, court unit, filing date, instance, and recent movement per instance.

Instructions

Fetch the public cover data of a Brazilian court case from CNJ's DataJud: class, subjects, court unit, filing date, instance and its most recent movement. Returns one record per instance. Party and lawyer names are not available in DataJud. Expect 20-60 s latency.

Input Schema

Name	Required	Description	Default
`court`	No	DataJud court alias, e.g. 'tjsp', 'trf1', 'trt2', 'tre-sp', 'stj'. Optional when looking up a case number: the origin court is derived from the number. Pass it explicitly to read the record held by a higher court (e.g. 'stj'), since appeals keep the origin case number.
`cnjNumber`	Yes	CNJ unified case number, formatted (NNNNNNN-DD.AAAA.J.TR.OOOO) or as 20 digits. Check digits are validated.

Output Schema

Name	Required	Description
`court`	Yes	Court alias that was queried
`found`	Yes
`records`	Yes	One record per instance (e.g. first instance and appeal) held by this court
`cnjNumber`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 26, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Beyond the annotations, the description adds critical behavioral traits: the scope (Brazilian court), data limitations (lack of names), and performance metrics (20-60s latency). This is rich behavioral context.

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 highly dense but perfectly utilized. Each clause contributes specific, actionable information (what is returned, what is not, how long it takes).

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 that it specifies the data source, the input requirements, the granular return (one record per instance), and performance, the definition is functionally complete for an agent to decide whether to use it and how.

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?

Since the schema coverage is 100%, the baseline score is 3. The description adds minor value by contextually explaining the CNJ number format and the role of the 'court' alias.

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 verb and the resource (public cover data of a Brazilian court case). By naming DataJud and defining the scope (CNJ's system), it clearly distinguishes this tool from generic search or list operations.

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?

It provides explicit limitations on the data returned, noting that 'Party and lawyer names are not available in DataJud.' This is a crucial usage guideline preventing the agent from expecting specific fields that this tool cannot fulfill.

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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