metamodel
Server Details
AI-callable calculators and engineering models with real formulas. No hallucinated math.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- metamodel-app/mcp-server
- GitHub Stars
- 0
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Tool Definition Quality
Average 4.4/5 across 3 of 3 tools scored.
Each tool has a distinct role: listing projects, fetching schemas, and running computations. No overlap or ambiguity.
Consistent 'metamodel_' prefix, but 'compute' breaks the verb_noun pattern used by the other two (list_projects, get_schema). Still clear and predictable.
Three tools is appropriate for a focused server that discovers, inspects, and runs models. No bloat or missing essentials.
Covers the full workflow: listing available projects, getting their schemas, and executing computations. No obvious gaps for the stated purpose.
Available Tools
3 toolsmetamodel_computeARead-onlyInspect
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. Use metamodel_get_schema first to discover available inputs and outputs.
| Name | Required | Description | Default |
|---|---|---|---|
| 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}). Auto-routed to the correct model. Omitted inputs use defaults. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds useful context: inputs are auto-routed by property name and omitted inputs use defaults. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two dense sentences with no wasted words. Front-loads the primary action and provides essential guidance concisely.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, description directs to use metamodel_get_schema for outputs. Explains input routing and defaults. Adequate for a compute tool with good annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%. Description adds behavioral meaning for inputs: auto-routing and default handling, which goes beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Run a computation on a MetaModel project' with specific verb and resource. It distinguishes from siblings by referencing metamodel_get_schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance to 'Use metamodel_get_schema first to discover available inputs and outputs.' Also advises on model parameter: 'When omitted, returns outputs from ALL models (recommended).'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
metamodel_get_schemaARead-onlyInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes | The publish token of the project (from metamodel_list_projects) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description details returned content (model names, input parameters with types/defaults/validation, output fields) beyond annotations which already mark it as read-only and non-destructive. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with purpose, no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity (one required parameter, no output schema, annotations provided), the description sufficiently explains input, output, and usage context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Single parameter 'token' is described in schema and in description. Schema coverage is 100%, so description adds minimal extra meaning beyond reiterating the source.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Get the input/output schema for a published MetaModel project.' Differentiates from siblings (metamodel_compute, metamodel_list_projects) 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use a token from metamodel_list_projects,' providing clear context for when to use this tool. Implicitly indicates not to use for computation or listing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
metamodel_list_projectsARead-onlyInspect
Browse available MetaModel calculators and engineering models. Returns project names, descriptions, tokens, and model names for each published project.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so the tool's safety is clear. The description adds value by detailing the return fields (names, descriptions, tokens, model names), which is useful behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, concise, and front-loaded with the key action and output. Every sentence adds information without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list operation with no parameters and no output schema, the description covers the essential return fields. It does not mention pagination or ordering, but given the low complexity, completeness is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters (0 parameters), and schema description coverage is 100%. Per guidelines, baseline for 0 parameters is 4, and the description correctly does not add parameter info since none exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: 'Browse available MetaModel calculators and engineering models.' It specifies the return fields (project names, descriptions, tokens, model names), making the purpose concrete and well-differentiated from sibling tools (metamodel_compute, metamodel_get_schema).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for listing projects but does not explicitly state when to prefer this over siblings or when not to use it. Context from sibling names provides differentiation, but the description itself lacks usage guidance.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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