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triz_import_source

Imports local BOM, OpenAPI, Quint, Lean, or Julia inputs and returns a draft interaction table and model with unconfirmed provenance so you can review ratings and assumptions before analysis.

Instructions

Local-only import of supplied BOM, OpenAPI, Quint, Lean or Julia inputs. Prefer modelingtoolkit-json and agents-json exports from instantiated Julia models; raw julia is a partial static fallback. Does not fetch references, execute source or load checkpoints. Returns a draft interaction table and model with embedded unconfirmed provenance; review all ratings and assumptions before analysis. Source-code outlines are partial syntactic references, not verified causal or finite behavior models.

Input Schema

NameRequiredDescriptionDefault
formatYes
sourceYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations supplied, the description carries the full burden and does so well: it discloses no network fetching, no execution, no checkpoint loading, and that output is a draft with unconfirmed provenance requiring review. It stops short of stating whether the import persists state or is idempotent, which matters for a tool that produces a 'model'.

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?

Five tight sentences, each doing distinct work: purpose, format preference, exclusions, return shape, and a caveat on fidelity. Purpose is front-loaded and no sentence is filler despite the dense technical vocabulary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, and the description does explain the return ('draft interaction table and model with embedded unconfirmed provenance'). For a tool with a nested source object and eight format variants, the remaining gap is the meaning of the source identifier and whether prior imports are affected.

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 0%, so the description must compensate. It maps format families (BOM, OpenAPI, Quint, Lean, Julia) onto the enum and hints that modelingtoolkit-json/agents-json are the preferred values, but the nested 'source.id' field is undocumented in both schema and description, and 'text' semantics come only from the schema's byte-limit note.

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?

Opens with a specific verb and scope: 'Local-only import of supplied BOM, OpenAPI, Quint, Lean or Julia inputs.' The formats and the 'local-only' qualifier let an agent distinguish it from siblings like triz_export and triz_review_import without opening the schema.

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?

Gives explicit format preference ('Prefer modelingtoolkit-json and agents-json exports from instantiated Julia models; raw julia is a partial static fallback') and clear exclusions ('Does not fetch references, execute source or load checkpoints'). It does not name a sibling alternative for the excluded cases, so routing to a different tool is left implicit.

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