UNIDROIT Principles 2016

law_search

Read-only

A semantic SEARCH over formalised law: a question or a notion in your own words → candidates with addresses — predicates with signatures, rules, article fragments. This is the layer of DISCOVERY, not of the answer: the result says WHAT to ask next (law_ask by signature, law_rules, law_sources) and establishes neither a fact nor a norm. The ranking is a deterministic lexical embedder, and similarity of text is not applicability of a norm. If you know the exact name, go straight to law_packages/law_ask; an empty result does NOT mean the norm does not exist.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "law_search"
  }
}

Input Schema

Name	Required	Description
`kinds`	No	narrow the kinds of candidate
`limit`	No	how many candidates (8, at most 25)
`query`	Yes	a question or a notion in your own words
`package`	No	search inside this instrument only

Output Schema

Name	Required	Description	Default
`model`	No
`query`	Yes
`fallback`	Yes
`candidates`	Yes
`indexUnits`	No
`jurisdiction`	Yes
`corpusFingerprint`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 14, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already declare readOnly/non-destructive, but the description goes well beyond them: it discloses the ranking mechanism (deterministic lexical embedder), warns that textual similarity is not normative applicability, clarifies the tool establishes neither fact nor norm, and pre-empts the empty-result false negative. This is exactly the extra behavioral context annotations cannot carry.

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?

Front-loads the core definition and follows with routing constraints, so almost every sentence earns its place. The ALL-CAPS emphasis, arrows, and em-dash fragments add stylistic noise that slightly obscures an otherwise tight structure.

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?

An output schema exists, so return-value format need not be re-explained, and the description still characterizes results as addressed candidates. Combined with the discovery-layer framing and sibling routing, nothing an agent needs to call this tool correctly is missing.

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?

Schema description coverage is 100%, so all four parameters (kinds, limit, query, package) are already documented in the schema, establishing a baseline of 3. The description reinforces query semantics ('a question or a notion in your own words') and implies the candidate kinds, but adds no syntax, default, or constraint detail beyond what the schema provides.

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?

States a specific verb and resource ('semantic SEARCH over formalised law') and concretely describes the output shape ('candidates with addresses — predicates with signatures, rules, article fragments'). It explicitly differentiates from siblings law_ask, law_rules, and law_sources, so an agent can route without opening another schema.

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?

Gives explicit when-to-use/when-not guidance: 'This is the layer of DISCOVERY, not of the answer', 'If you know the exact name, go straight to law_packages/law_ask', plus the critical caveat that an empty result does not mean the norm is absent. Alternatives are named with the condition that selects them.

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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