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MetaModel MCP Server

MetaModel MCP Server

Official
by metamodel-app
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JavaScript
Remote

metamodel_compute

Read-only

Run computations on a MetaModel project by sending input values and receiving computed outputs from all models or a specific one, with inputs auto-routed to the correct model.

Instructions

Run a computation on a MetaModel project. Send input values and get computed outputs from all models. Inputs are auto-routed to the correct model by property name. Optionally specify a single model to target. Use metamodel_get_schema first to discover available inputs and outputs.

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": {
      "token": "<token>"
    },
    "name": "metamodel_compute"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoTarget a specific model by name. When omitted, returns outputs from ALL models (recommended).
tokenYesThe publish token of the project
inputsNoInput values as key-value pairs (e.g., {"width": 12, "height": 8}). Auto-routed to the correct model. Omitted inputs use defaults.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.2

TDQS

A4.4/5.0
Behavior4/5

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

Annotations indicate read-only and non-destructive behavior. Description adds useful context: inputs auto-routed by property name, omitted inputs use defaults, and targeting a single model vs all models. No contradictions with annotations.

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?

Four sentences, each earning its place: purpose, input/output behavior, auto-routing, model targeting, and prerequisite. Front-loaded with key action. No fluff.

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?

Given three parameters, annotations, and no output schema, the description covers the main behavior, parameter guidance, and prerequisite. Could mention that return values are computed outputs, but overall complete enough for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all parameters (100%). Description adds meaning: explains auto-routing for 'inputs' and recommends omitting 'model' for all outputs. This adds value beyond the schema's literal definitions.

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?

Clearly states the tool runs a computation on a MetaModel project with input sending and output retrieving. It distinguishes from sibling tools (metamodel_list_projects, metamodel_get_schema) by describing the core computation action and auto-routing behavior.

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?

Explicitly tells the agent to use metamodel_get_schema first to discover inputs/outputs, and provides guidance on the 'model' parameter (omit for all models, recommended). Lacks explicit when-not-to-use or alternative exclusions, but clear context is present.

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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