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Praket7

mastercam-mcp

by Praket7

run_simulation

Read-only

Runs a Mastercam simulation and returns provenance-tagged evidence, giving you verifiable proof of machining operations.

Instructions

Run Mastercam simulation and return provenance-tagged evidence

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
dataNo
liveNo
toolYes
errorNo
receiptNo
documentRevisionNo
operationFingerprintNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed11 schema fields changedv0.3.0
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / properties / confirmed
      Removed value: -{
      -  "type": "boolean"
      -}
    • removedInput schema / properties / dryRun
      Removed value: -{
      -  "type": "boolean"
      -}
    • removedInput schema / properties / feed
      Removed value: -{
      -  "type": "number"
      -}
    • removedInput schema / properties / name
      Removed value: -{
      -  "type": "string"
      -}
    • removedInput schema / properties / operationId
      Removed value: -{}
    • removedInput schema / properties / operationIds
      Removed value: -{
      -  "items": {},
      -  "type": "array"
      -}
    • removedInput schema / properties / outputPath
      Removed value: -{
      -  "type": "string"
      -}
    • removedInput schema / properties / speed
      Removed value: -{
      -  "type": "number"
      -}
    • removedInput schema / properties / toolId
      Removed value: -{}
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "$schema": "https://json-schema.org/draft/2020-12/schema",
      +  "additionalProperties": false,
      +  "properties": {
      +    "data": {},
      +    "documentRevision": {
      +      "type": "string"
      +    },
      +    "error": {
      +      "additionalProperties": false,
      +      "properties": {
      +        "code": {
      +          "type": "string"
      +        },
      +        "message": {
      +          "type": "string"
      +        },
      +        "remediation": {
      +          "type": "string"
      +        },
      +        "retryable": {
      +          "type": "boolean"
      +        }
      +      },
      +      "required": [
      +        "code",
      +        "message"
      +      ],
      +      "type": "object"
      +    },
      +    "live": {
      +      "type": "boolean"
      +    },
      +    "ok": {
      +      "type": "boolean"
      +    },
      +    "operationFingerprint": {
      +      "type": "string"
      +    },
      +    "receipt": {},
      +    "tool": {
      +      "type": "string"
      +    }
      +  },
      +  "required": [
      +    "ok",
      +    "tool"
      +  ],
      +  "type": "object"
      +}
  2. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds that the tool returns provenance-tagged evidence, which indicates a behavior about the output, but it does not disclose potential runtime cost, dependence on part state, or any side effects beyond what annotations provide. Since annotations cover the important safety aspects, the additional context is modest.

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 a single, front-loaded sentence with no filler. Every word contributes to the essential purpose and output. It is appropriately sized for a tool with no parameters and a clear action.

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 no parameters and an existing output schema, the description is nearly complete. It names the action and the output nature. The only minor gap is the lack of explicit preconditions (e.g., having a Mastercam document loaded), but the output schema and annotations cover most needed information, making the description sufficient for such a simple 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?

The tool has zero parameters, and the schema coverage is 100% (empty properties). Per the rubric, a no-parameter tool earns a baseline of 4. The description correctly does not attempt to define parameters that don't 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 states a specific verb ('Run') and resource ('Mastercam simulation'), and specifies the return type ('provenance-tagged evidence'). No sibling tool mentions simulation, so the purpose is clearly distinguished from the other tools in the context.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus alternatives such as capture_view, inspect, or detect_collisions. There is also no mention of any exclusions or preconditions (e.g., requiring an active Mastercam session). The usage context must be inferred entirely by the agent.

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