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Normalized document

normalize
Read-onlyIdempotent

Resolve a document: apply templates, assign all node/link IDs, and put numbers in canonical form. Use it for stable IDs before patching or to see the document as rules and drawings see it.

Instructions

Return the resolved form of a document: templates ('use') applied, every node and link id assigned, numbers in canonical form. Use it to read a document exactly as the rules and drawings see it, or to get stable ids before apply_patch; it does not apply the electrical rules (see check).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
documentYesThe uniwire document of the electrical installation: a JSON object, or its JSON text, as returned by get_example, apply_patch or normalize (a file path is accepted when the server runs locally).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.3-dev
    • addedInput schema / properties / document / description
      Added value: +"The uniwire document of the electrical installation: a JSON object, or its JSON text, as returned by get_example, apply_patch or normalize (a file path is accepted when the server runs locally)."
  2. First observedv0.1.1

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds value beyond that by disclosing what the resolution actually does (templates via 'use', id assignment, canonical numbers) and by stating what it does NOT do (electrical rules). It doesn't mention determinism caveats or performance, but the annotation-covered safety plus the transformation semantics make this strong.

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?

Three dense clauses with zero filler; the transformation list is front-loaded, usage conditions follow, and the exclusion is last. Every phrase carries information.

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 values need not be explained, and the description appropriately focuses on behavior and usage instead. Combined with the annotations, an agent has everything needed to call this correctly.

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?

Only one parameter (document) and schema coverage is 100%, so the schema already documents accepted forms (JSON object, JSON text, file path when local). The description adds nothing about the input format beyond the schema, so the baseline of 3 applies.

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 (return the resolved form) and enumerates the three transformations it performs: template expansion, id assignment, and number canonicalization. It clearly distinguishes itself from check (electrical rules) and apply_patch (which consumes the stable ids).

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?

Explicitly names two use cases ('read a document exactly as the rules and drawings see it' and 'get stable ids before apply_patch') and an exclusion ('it does not apply the electrical rules (see check)'). This routes the agent to the correct sibling without inference.

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