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bpmn-generator-mcp

bpmn-generator-mcp

by johnleyva28
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

create_process_from_image

Generate a BPMN process model by extracting steps from an image using OCR. Converts visual diagrams into structured BPMN processes.

Instructions

Crea un proceso BPMN extrayendo pasos de una imagen usando OCR.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "create_process_from_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
process_nameNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0-typescript

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It does disclose the OCR-based extraction mechanism, which is useful, but it is silent on failure modes, OCR availability, how the image is supplied, and whether the generated BPMN process is returned or saved.

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?

A single, front-loaded sentence with no filler; the action, source, and method are conveyed efficiently. It is concise without being tautological.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations, no output schema, and only one parameter, the description leaves critical gaps: how the image is specified given the schema only has process_name, whether OCR is built in, and what the tool returns. The basic purpose is clear, but this is not enough for reliable invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is one parameter, process_name, and the schema provides zero description coverage. The description does not explain what process_name means, whether it is required, or how it relates to the image input, so the agent cannot reliably populate it.

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 names a specific action (creates a BPMN process) plus the source (image) and method (OCR). This makes it clearly distinct from siblings like create_process_from_text_description and extract_text_from_image, so an agent can tell what the tool does at a glance.

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

Usage Guidelines3/5

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

The description implies the tool should be used when the input is an image containing process steps, but it never states that explicitly or names alternatives/exclusions. It also does not mention checking is_ocr_available first, even though that sibling exists, leaving usage context to inference.

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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