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Glama

Aris Ingest Model Graph

aris_ingest_model_graph
Idempotent

Ingest one model, its objects typed by leaf ontology class, and its connections—reified relations plus flowsTo—into the knowledge graph for graph-based analysis.

Instructions

Ingest one model with its objects (typed by their leaf ontology class) and its connections (reified, plus :flowsTo) into the knowledge graph.

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": {
      "model_id": "<model_id>"
    },
    "name": "aris_ingest_model_graph"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_idYesModel GUID (or AML Model.ID).

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.0.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare this is a write (readOnlyHint=false), idempotent, and non-destructive, so the safety profile is covered. The description adds genuine value by specifying what is written and how it is modeled (leaf ontology class typing, reified connections, :flowsTo edges). It does not say what happens to pre-existing graph content for the same model or whether source-side data must exist first.

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?

A single sentence with no filler; the most important information (scope = one model, its objects and connections) is front-loaded. The stacked parentheticals are dense but each carries real semantic content about the resulting graph shape.

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

Completeness3/5

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 described, and the annotations cover mutability. What remains missing for a write tool of this kind is selection guidance among the three ingest siblings and any statement about merge/replace behavior in the knowledge graph.

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 exists and the schema already documents it at 100% coverage ("Model GUID (or AML Model.ID)"). The description adds nothing about model_id formats or where to obtain a valid ID, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (ingest) and a precisely bounded resource: one model, its objects typed by leaf ontology class, and its connections reified plus :flowsTo. That is far more informative than a restatement of the name. It does not, however, distinguish itself from the near-identical siblings aris_ingest_model_inventory and aris_ingest_model_export.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not-to-use guidance, and the sibling list contains three separate ingest tools whose names alone cannot tell an agent which path to pick. The description gives no prerequisite (e.g. whether the model must first be located via aris_list_models) and no exclusion conditions.

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