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aris-mcp

aris-mcp

by Knuckles-Team
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Aris Ingest Model Inventory

aris_ingest_model_inventory
Idempotent

Ingests model inventory into the ARIS knowledge graph as model nodes, optionally filtering by name to target specific models.

Instructions

Ingest the model inventory into the knowledge graph as model nodes.

Best-effort: ingested is null when no engine is reachable. CONCEPT:AU-KG.ingest.enterprise-source-extractor.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "aris_ingest_model_inventory"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameNoOnly models whose name contains this.

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 readOnlyHint=false, idempotentHint=true, and destructiveHint=false, so the mutation/idempotency profile is covered structurally. The description adds one genuinely useful behavior beyond that: the call is best-effort and 'ingested' is null when no engine is reachable, which tells the agent how to interpret a degraded result. It says nothing about what happens to pre-existing nodes, what permissions are needed, or the size/rate characteristics of the write.

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?

Two short sentences, front-loaded with the action and resource, with the failure-mode note immediately after. The trailing 'CONCEPT:AU-KG.ingest.enterprise-source-extractor.' tag and backtick markup are internal metadata noise that do not help an agent select or call the tool.

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 the description need not explain return values, and it correctly references the 'ingested' field rather than restating the schema. However, for a write tool sitting in a cluster of three similar ingest tools, the description is incomplete on tool selection and on what the inventory source actually is.

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?

There is one optional parameter with 100% schema description coverage, so the schema fully documents that 'name' filters by substring. The description adds no extra meaning about the filter (matching rules, case sensitivity, or what happens when it is omitted), 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?

The description gives a specific verb ('Ingest'), resource ('model inventory'), and destination ('knowledge graph as model nodes'), which is far better than a tautology of the title. It stops short of distinguishing itself from the near-identical siblings aris_ingest_model_graph and aris_ingest_model_export, so an agent cannot tell from this text alone which ingest variant to pick.

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 guidance on when to use this tool versus the three other ingest siblings or the list/get model tools. The 'best-effort' note hints at runtime behavior but does not tell the agent when this call is appropriate or what prerequisites (a reachable engine) it needs.

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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