Service Fusion MCP Server and API by Cotlet

Create invoice in Service Fusion

service_fusion_create_invoice

Read-onlyIdempotent

Create invoice in the user's own Service Fusion account from ChatGPT, Claude or any AI agent. Returns how to do it in Service Fusion step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's Service Fusion Cloud Browser. Service Fusion's official API: Service Fusion has an Open API, but only on the Pro plan, and it has no official MCP server (checked 11 Oct 2026).

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": "service_fusion_create_invoice"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 9, 2026

TDQS

A3.9/5.0

Behavior4/5

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

The annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds important context: it opens nothing by itself and returns UI-level instructions. The initial 'Create invoice...' phrasing is immediately corrected, so there is no annotation contradiction, though the description could more directly state that no invoice is created.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably structured but contains some noise: naming ChatGPT, Claude, and any AI agent is unnecessary, and the API plan/MCP server detail is only marginally relevant to invoking this tool. The core behavior is present but could be front-loaded more directly.

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 the lack of parameters, output schema absence, and rich annotations, the description does enough: it explains what the tool returns and what to do next. It could be more complete by clarifying its relationship to cotlet_app_guide, but an agent has enough information to use it correctly.

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, so parameter semantics are not a major burden. The description appropriately focuses on output and workflow rather than inventing parameters, which matches the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the actual behavior: it returns step-by-step Service Fusion instructions rather than performing the invoice creation. However, the tool name and title ('create invoice') suggest a mutating action, and the description does not explicitly distinguish itself from the cotlet_app_guide sibling.

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

Usage Guidelines4/5

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

It explains the intended workflow: first get the steps, then execute them with the cotlet_browser_* tools in the Cloud Browser. It does not explicitly state when not to use this tool or how it relates to cotlet_app_guide, but the operational guidance is clear.

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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