Pakistan Case Law

caselaw_search_questions

Read-onlyIdempotent

Find judgments by the QUESTION they settle, rather than by words they contain.

Every AI summary on this corpus records the questions that judgment actually decides — e.g.
"Does delay in lodging the FIR by itself entitle the accused to bail?" This searches those
questions, so it matches the shape a legal problem arrives in rather than the vocabulary a
1987 bench happened to use.

COVERAGE IS PARTIAL: questions exist for about 22,637 of 197,365 judgments (roughly one in
nine). An empty result means no *summarised* judgment matched — it does NOT mean no such
judgment exists. Fall back to caselaw_search, which covers the whole corpus.

The questions are written by an AI reading the judgment. They are a finding aid, not a
holding: cite the judgment, never the question. Pass an id to caselaw_get_case to read it.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`query`	Yes	The legal question or its subject, in the words a judgment would use — 'delay in lodging FIR', 'concurrent findings of fact', 'pre-arrest bail murder'
`offset`	No
`response_format`	No		markdown

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 6, 2026

TDQS

A4.1/5.0

Behavior1/5

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

Annotation Contradiction: the description explicitly says coverage is partial and that an empty result 'does NOT mean no such judgment exists', while the annotations set openWorldHint to false, implying closed-world semantics where absence of a result is meaningful. This directly contradicts the annotation and could mislead an agent that trusts the structured metadata over the description.

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 longer than average but every section earns its place: purpose, source of the questions, coverage caveat, fallback, and usage caution. It is front-loaded with the core distinction, and the all-caps caveats make the most important warnings hard to miss.

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?

For a read-only search tool with an output schema, the description covers everything an agent needs: what is searched, the coverage limitation, the alternative tool, the non-authoritative nature of the AI-written questions, and the next step via caselaw_get_case. Nothing essential is missing.

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?

Schema description coverage is only 25%, but the description substantially compensates for the critical required query parameter by explaining what kind of legal question to enter and providing concrete examples. The remaining parameters—limit, offset, and response_format—are left to names, defaults, and schema-defined choices, which is adequate for standard controls.

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 opening sentence states the verb, resource, and distinctive mechanism: 'Find judgments by the QUESTION they settle, rather than by words they contain.' This clearly separates it from caselaw_search without needing to inspect sibling definitions.

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 explicitly tells the agent when to prefer this tool—when matching the shape of a legal problem matters—and names the fallback: 'Fall back to caselaw_search, which covers the whole corpus.' It also explains what an empty result means, preventing the agent from drawing the wrong conclusion.

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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