AI-callable calculators and engineering models with real formulas. No hallucinated math.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.2/5 across 3 of 3 tools scored.
Each tool has a distinct, non-overlapping purpose: listing projects, fetching schema, and running computations. The workflow is clear and sequential.
All tools follow a consistent verb_noun pattern with the same prefix 'metamodel_', making them easy to distinguish.
With only 3 tools for a focused modeling service, each earns its place. The count is ideal for discoverability and straightforward usage.
The tool set covers the full lifecycle for the server's purpose: discover projects, inspect schema, and run computations. No obvious gaps for a read-only computational service.
metamodel_computeARun 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. Models marked "presentation": true in results are display scaffolding, not engineering outputs. 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true. The description adds context about auto-routing of inputs and that presentation models are display scaffolding, which are behavioral nuances beyond annotations. 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?
Four sentences, front-loaded with the main purpose, no filler. Every sentence adds essential information. Highly efficient.
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 no output schema, the description adequately explains that outputs are computed results from all models and notes presentation scaffolding. Could be more explicit about output format, but overall sufficient for the tool's complexity.
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 covers 100% of parameters with descriptions. The description adds value by explaining 'Inputs are auto-routed to the correct model by property name' and reinforcing that omitted inputs use defaults. This supplements the schema.
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 starts with 'Run a computation on a MetaModel project,' clearly stating the verb and resource. It adds specifics about auto-routing inputs and distinguishing presentation models from engineering outputs, making the purpose distinct from sibling tools (get_schema, list_projects).
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 advises using metamodel_get_schema first to discover inputs/outputs, providing a clear prerequisite. However, it lacks explicit 'when not to use' guidance, but given the sibling set, the usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
metamodel_get_schemaAGet the input/output schema for a published MetaModel project. Returns model names, input parameters (with types, defaults, validation), and output fields. Models marked "presentation": true are display scaffolding (3D scenes etc.) — skip them for engineering analysis. Use a token from metamodel_list_projects.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes | The publish token of the project (from metamodel_list_projects) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, destructiveHint=false, aligning with description's 'Get' operation. Description adds behavioral nuance about filtering presentation models, enhancing transparency 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?
Two concise sentences with no wasted words, front-loaded with purpose and key directives (skip presentation models, use token from list).
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 single-parameter tool with detailed annotations and no output schema, the description fully covers purpose, behavior, usage nuance, and parameter provenance.
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% with the token parameter already described. The tool description repeats the source of the token (metamodel_list_projects) but does not add new semantic information beyond the schema.
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 the tool retrieves input/output schema for a published MetaModel project, listing specific return contents and distinguishing from sibling tools via reference to metamodel_list_projects for token acquisition.
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?
Provides explicit guidance to skip presentation models for engineering analysis and instructs to use a token from metamodel_list_projects. Lacks explicit when-not-to-use or comparison with metamodel_compute, but direction is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
metamodel_list_projectsABrowse available MetaModel calculators and engineering models. Returns project names, descriptions, tokens, and model names for each published project.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and non-destructive behavior. The description adds useful context about the return contents (project names, descriptions, etc.) but does not disclose any additional behavioral traits (e.g., no mention of authentication, rate limits, or side effects). With annotations covering safety, the added value is moderate.
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 with no wasted words. It front-loads the purpose and follows with specifics, achieving maximum conciseness for the information provided.
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 tool with no parameters and annotations covering safety, the description adequately explains what is returned. It could mention pagination or ordering, but overall it is complete enough for an agent to use correctly.
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?
The input schema has zero parameters with 100% coverage, so the description does not need to add parameter information. Baseline for no parameters is 4, and the description does not detract from this.
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 lists available MetaModel calculators and engineering models, specifying the returned data (names, descriptions, tokens, model names). It uses a specific verb 'browse' and resource, distinguishing it from sibling tools that compute or get schemas.
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?
No guidance is provided on when to use this tool versus siblings like metamodel_compute or metamodel_get_schema. There is no explicit context for its place in a workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
Sign in to create a connector for this server.