moldsim mcp

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully discloses output content ('analysis types, process conditions, mesh recommendations, and expected results'), but fails to indicate whether this operation is read-only, destructive, idempotent, or has side effects like persistence to a database.

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 consists of two efficient sentences with zero waste. The first sentence establishes the core function and input method; the second sentence details the output components. Every word earns its place.

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 the 100% schema coverage and lack of output schema, the description adequately covers the tool's purpose and output contents. However, for a generation tool with no annotations, it should ideally disclose the output format (JSON vs string) and safety characteristics, which are currently absent.

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 description coverage is 100%, establishing a baseline of 3. The description mentions 'natural language description of the part and requirements,' which reinforces the required 'description' parameter, but adds no additional semantic detail for the optional 'cad_format' parameter or guidance on input syntax beyond what the schema already provides.

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 generates a 'structured simulation specification' from 'natural language description,' specifying the transformation performed. It distinguishes from siblings like 'validate_process_parameters' (which checks existing values) and 'query_simulation_knowledge' (which retrieves data) by emphasizing the generative nature and natural language input requirement, though it doesn't explicitly contrast with the sibling 'generate_dfm_checklist'.

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 usage through the phrase 'from a natural language description,' indicating when to use this tool (when unstructured text input is available). However, it lacks explicit guidance on when NOT to use it or which sibling to use instead (e.g., 'use validate_process_parameters if you already have specific parameters to check').

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