How do I use MetaModel MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MetaModel MCP Server What calculators are available on MetaModel?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MetaModel MCP Server

Official

by metamodel-app

Overview Schema Related Servers Score Discussions

JavaScript

Remote

MetaModel MCP Server

npm MCP license

An MCP (Model Context Protocol) server that lets AI assistants use MetaModel's formula engine for certified computation. Turn published calculators, pricing tools, and engineering models into AI-callable tools — no hallucinated math.

All tools are read-only. This server fetches published project schemas and runs stateless computations. It never writes, modifies, or stores any data.

Quick Start

Remote (hosted) — easiest

No install. Add the URL as a custom connector:

https://www.metamodel.app/api/mcp

Claude.ai / Claude Desktop: Settings → Connectors → Add custom connector → paste the URL
Claude Code: claude mcp add --transport http metamodel https://www.metamodel.app/api/mcp

Works on web and mobile; no Node required. The hosted endpoint is stateless, read-only, and rate-limited.

Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "metamodel": {
      "command": "npx",
      "args": ["-y", "metamodel-mcp"]
    }
  }
}

Claude Code

claude mcp add metamodel -- npx -y metamodel-mcp

Other MCP Clients

Any MCP-compatible client can connect via stdio:

npx -y metamodel-mcp

Related MCP server: CalcsLive MCP Server

Tools

`metamodel_list_projects`

Browse available calculators and engineering models. Returns project names, descriptions, publish tokens, and model names.

Annotations: readOnlyHint: true

`metamodel_get_schema`

Discover inputs and outputs for a specific project. Returns model names, input parameters (with types, defaults, validation rules), output fields, and formulas.

Annotations: readOnlyHint: true

Parameter	Type	Required	Description
`token`	string	Yes	Project publish token (from `metamodel_list_projects`)

`metamodel_compute`

Run a computation with input values. Send inputs once — they're auto-routed to the correct model by property name. Outputs from all models are returned, including cross-model cascading.

Annotations: readOnlyHint: true

Parameter	Type	Required	Description
`token`	string	Yes	Project publish token
`model`	string	No	Target a specific model. When omitted, returns all models (recommended).
`inputs`	object	No	Input values as key-value pairs. Auto-routed to the correct model. Omitted inputs use defaults.

Examples

Example 1: List Available Projects

Prompt: "What calculators are available on MetaModel?"

Tool call: metamodel_list_projects()

Response (truncated):

{
  "projects": [
    {
      "token": "989bc6ae-e4d7-4585-8908-bfd03358043e",
      "name": "Deck Builder",
      "description": "Interactive deck builder with code-compliant sizing. Uses LOOKUP tables for beams, frost depths, and railing requirements.",
      "models": ["deck", "platformModel", "beamsModel", "postsModel", "railingModel", "summaryModel"]
    },
    {
      "token": "fdc5fbef-9d4b-46c3-b72d-1950e03d4bf4",
      "name": "Building Engineer",
      "description": "Complete building engineering calculator. Enter room dimensions to auto-size HVAC, electrical, and lighting systems with full cost estimation.",
      "models": ["room", "hvac", "electrical", "lighting", "costEstimator"]
    },
    {
      "token": "363c7753-ac4b-4de4-b1e7-e4536cb2f9bb",
      "name": "Life Expectancy Calculator",
      "description": "Estimate your life expectancy based on age, sex, and lifestyle factors.",
      "models": ["basics", "lifestyle", "results"]
    }
  ]
}

Example 2: Get Project Schema

Prompt: "What inputs does the Building Engineer calculator need?"

Tool call: metamodel_get_schema({ token: "fdc5fbef-9d4b-46c3-b72d-1950e03d4bf4" })

Response (truncated):

{
  "projectName": "Building Engineer",
  "models": [
    {
      "name": "room",
      "title": "Room Dimensions",
      "inputs": [
        { "name": "length", "type": "number", "defaultValue": 35.26 },
        { "name": "width", "type": "number", "defaultValue": 60 },
        { "name": "height", "type": "number", "defaultValue": 12 }
      ],
      "outputs": [
        { "name": "floorArea", "formula": "floorArea = length * width" },
        { "name": "volume", "formula": "volume = length * width * height" }
      ]
    },
    {
      "name": "hvac",
      "title": "HVAC Sizing",
      "inputs": [
        { "name": "climateZone", "defaultValue": "cold" },
        { "name": "btuFactor", "type": "number", "defaultValue": 25 }
      ],
      "outputs": [
        { "name": "btuRequired", "formula": "btuRequired = volume@room * btuFactor" },
        { "name": "tonnage", "formula": "tonnage = btuRequired / 12000" },
        { "name": "annualCost", "formula": "annualCost = tonnage * 120" }
      ]
    }
  ]
}

Note the cross-model references: volume@room means "use the volume output from the room model." MetaModel handles this cascading automatically.

Example 3: Run a Computation

Prompt: "Size the HVAC, electrical, and lighting for a 50×80 ft room with 14 ft ceilings"

Tool call: metamodel_compute({ token: "fdc5fbef-...", inputs: { length: 50, width: 80, height: 14 } })

Response:

{
  "models": {
    "room": {
      "outputs": { "floorArea": 4000, "volume": 56000, "wallArea": 3640 }
    },
    "hvac": {
      "outputs": { "btuRequired": 1400000, "tonnage": 116.7, "annualCost": 14000 }
    },
    "electrical": {
      "outputs": { "outletsNeeded": 304, "totalAmps": 456, "circuitCount": 23 }
    },
    "lighting": {
      "outputs": { "totalLumens": 201240, "fixtureCount": 51, "totalWattage": 2040 }
    },
    "costEstimator": {
      "outputs": {
        "hvacMaterial": 415917, "electricalMaterial": 18400,
        "lightingMaterial": 7650, "totalLabor": 89321, "grandTotal": 531288
      }
    }
  },
  "metadata": { "projectName": "Building Engineer", "evaluationMs": 7.1 }
}

One API call → room geometry, HVAC sizing, electrical load, lighting design, and full cost estimate. All computed from real formulas in ~7ms, not LLM-generated.

Development

To test against a local MetaModel instance:

# Clone and build
cd packages/mcp-server
npm install
npm run build

# Run with local URL
node dist/index.js --url http://localhost:3000

# Test with MCP Inspector
npx @modelcontextprotocol/inspector node dist/index.js --url http://localhost:3000

Privacy Policy

MetaModel's MCP server is stateless and read-only:

No authentication required — all published projects are public
No personal data collected — computations are anonymous
No data stored — inputs are evaluated and discarded immediately
No cookies or tracking — the server makes direct API calls to metamodel.app
No third-party data sharing — results go only to the requesting client

Full privacy policy: https://www.metamodel.app/privacy

About MetaModel

MetaModel lets you build calculators, pricing tools, and engineering models with a spreadsheet-like formula language. Describe what you want, AI builds it, publish in seconds. Models become API-callable computation endpoints that any AI assistant can use via this MCP server.

License

MIT

Troubleshooting

"Project not found or not published" — the token belongs to an unpublished or deleted project. Ask the project owner for a current publish token, or list what's available with metamodel_list_projects.
Unknown input names — inputs are matched by property name. Call metamodel_get_schema first; it returns the exact input names, types, and defaults for the project.
Inputs sent as a JSON string — pass inputs as a JSON object ({"width": 12}), not a stringified object. Stringified inputs fail with an "Unknown input" error naming a character index.
Rate limits (hosted endpoint) — the hosted URL is rate-limited per client. For heavy use, run the npm package locally with your own network egress.
Node version (local install) — the stdio server needs Node 18+.

Available Tools

3 tools

metamodel_computeA

Read-only

Inspect

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.

ParametersJSON Schema

Name	Required	Description
`model`	No	Target a specific model by name. When omitted, returns outputs from ALL models (recommended).
`token`	Yes	The publish token of the project
`inputs`	No	Input values as key-value pairs (e.g., {"width": 12, "height": 8}). Auto-routed to the correct model. Omitted inputs use defaults.

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.

metamodel_get_schemaA

Read-only

Inspect

Get the input/output schema for a published MetaModel project. Returns model names, input parameters (with types, defaults, validation), and output fields. Use a token from metamodel_list_projects.

ParametersJSON Schema

Name	Required	Description	Default
`token`	Yes	The publish token of the project (from metamodel_list_projects)

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description confirms a read-only operation ('Get'). It adds useful detail about the returned content (model names, input parameters, output fields), enhancing transparency without contradiction.

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?

The description is two short sentences: the first states the core function, the second provides a critical prerequisite. No excess words, front-loaded with essential 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?

For a simple tool with one parameter and no output schema, the description adequately covers purpose, prerequisite, and return content. It is complete given the tool's complexity and available structured information.

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?

The sole parameter 'token' is well-described in the schema, and the description adds context by referencing metamodel_list_projects as the source. With 100% schema coverage, the description adds marginal but helpful value beyond the schema.

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?

The description clearly states the tool's purpose: to get the input/output schema for a published MetaModel project. It specifies the resource (schema) and the verb (get), and distinguishes from siblings like metamodel_list_projects and metamodel_compute by focusing on schema retrieval.

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?

The description explicitly says 'Use a token from metamodel_list_projects,' providing a clear prerequisite and hinting at the workflow order. While it doesn't specify when not to use or list alternatives, this guidance is sufficient for correct invocation.

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

metamodel_list_projectsA

Read-only

Inspect

Browse available MetaModel calculators and engineering models. Returns project names, descriptions, tokens, and model names for each published project.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds value by specifying the exact return contents (project names, descriptions, tokens, model names), which is behavioral information not covered by 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?

Two concise sentences: the first clearly states the action, the second lists the return fields. No unnecessary words, front-loaded with purpose.

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?

For a tool with no parameters and no output schema, the description adequately explains what it does and returns. It could mention that it's a safe read operation, but that is covered by annotations. Overall complete for a simple list tool.

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?

There are no parameters (0 params), so by the baseline rule, the score is 4. The description does not need to add parameter meaning since none exist.

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?

The description uses a specific verb 'Browse' combined with a clear resource 'MetaModel calculators and engineering models', and lists the return fields. This clearly distinguishes it from siblings, which are about getting schema and computing.

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

Usage Guidelines3/5

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

The description implicitly suggests use before other tools to see available projects, but does not explicitly state when to use or when not to. It lacks direct comparison to siblings or exclusions.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

3 tool updatesv1.1.2Jul 29, 2026
- First observedmetamodel_compute
- First observedmetamodel_get_schema
- First observedmetamodel_list_projects

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.5/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: listing projects, getting schemas, and running computations. No overlap or ambiguity.

Naming Consistency5/5

All tools follow a consistent `metamodel_verb_noun` pattern (list_projects, get_schema, compute), making naming predictable and clear.

Tool Count5/5

Three tools is ideal for this focused domain. Each tool is essential for the workflow of discovering and using MetaModel projects.

Completeness5/5

The tool surface covers the full workflow: discover projects, understand their inputs/outputs, and run computations. No obvious gaps for the stated purpose.

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

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MetaModel MCP Server

Quick Start

Remote (hosted) — easiest

Claude Desktop

Claude Code

Other MCP Clients

Tools

metamodel_list_projects

metamodel_get_schema

metamodel_compute

Examples

Example 1: List Available Projects

Example 2: Get Project Schema

Example 3: Run a Computation

Development

Privacy Policy

About MetaModel

License

Troubleshooting

Available Tools

TDQS

TDQS

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

FI-MCP

CalcsLive MCP Server

SSCMFI Bond Analytics MCPofficial

precision-math-mcp

Latest Blog Posts

MCP directory API

`metamodel_list_projects`

`metamodel_get_schema`

`metamodel_compute`