Get 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.
Add one secure layer between your agents and this server.
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.
