kickserv-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@kickserv-mcp show me my overdue invoices"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
kickserv-mcp
An MCP server for Kickserv field service management — lets AI agents (Claude, ChatGPT, and any MCP-compatible client) read and manage customers, jobs, and invoices.
Ask your AI assistant things like:
"Which invoices are overdue and who do I need to call?" "Create a job for Kelly Nakamura — toilet rebuild, estimate $265." "What's my active job pipeline worth right now?"
Zero dependencies. Plain Node 18+. Nothing to install.
Quick start (demo / mock mode)
node src/index.jsWith no credentials set, the server runs in mock mode — a realistic sample plumbing-company dataset — so you can demo the full agent experience without touching a live account.
Connect to Claude Desktop
Add to claude_desktop_config.json:
{
"mcpServers": {
"kickserv": {
"command": "node",
"args": ["/absolute/path/to/kickserv-mcp/src/index.js"]
}
}
}Restart Claude Desktop and ask: "What's in my Kickserv account?"
Related MCP server: kaseya-bms-mcp
Live mode
export KICKSERV_ACCOUNT=your-account-slug
export KICKSERV_API_TOKEN=your-api-token
node src/index.jsLive mode targets Kickserv's REST API using HTTP Basic auth with your API token. Verify endpoint paths against your account's developer docs before production use.
Tools
Tool | What it does |
| List/search customers by name, contact, email, or address |
| Full detail for one customer |
| List jobs, filter by status or customer |
| Full detail for one job |
| Create a new job for a customer |
| List invoices, filter by status (open/paid/overdue) or customer |
| Open, overdue, and paid totals + active job pipeline value |
Tests
npm testSpawns the server over stdio and exercises the full protocol: initialize → tools/list → tool calls. 8 checks.
Why this exists
AI agents are becoming the interface to business software. Field service platforms that agents can't talk to will lose deals to ones they can. This server is a working demonstration of what "agent-ready" looks like for a field service product — built as a portfolio piece by Will Brownfield, who builds MCP integrations for SaaS companies.
Want your product to be agent-ready? Email me: willbrownfield@icloud.com
License
MIT
Available Tools
7 toolscreate_jobC
Create a new job for a customer. Returns the created job record.
Inputs: name (required), customer_id (required), estimate, description.
get_customerA
Get full details for one customer by ID.
Inputs: id (required).
get_jobA
Get full details for one job by ID.
Inputs: id (required).
list_customersA
List customers, optionally filtered by a search term (name, contact, email, or address).
Inputs: search.
list_invoicesA
List invoices, filterable by status (open | paid | overdue) and/or customer.
Inputs: status, customer_id.
list_jobsA
List jobs, filterable by status and/or customer.
Inputs: status, customer_id.
revenue_snapshotA
Summarize money state: total open, overdue, and paid invoice amounts, plus pipeline value of unfinished jobs.
Inputs: no named parameters.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose: creating jobs, fetching individual customers or jobs, listing entities with filters, and a revenue summary. No two tools overlap in function.
All tools use lowercase with underscores; most follow verb_noun pattern (e.g., create_job, list_customers). 'revenue_snapshot' is a noun_noun exception, but the overall style is consistent and predictable.
With 7 tools covering customers, jobs, invoices, and a revenue report, the count is well-scoped for a focused business management service. It's neither too sparse nor overwhelming.
The tool set lacks update and delete operations for all entities, and there is no way to create a customer or invoice. This creates significant gaps in typical CRUD workflows that agents would likely need.
Maintenance
Related MCP Connectors
Kickserv MCP server for ChatGPT, Claude and any AI agent: create job, list customers, create estimate, send invoice and log time entry in your own Kickserv account. Cotlet works in the account through your Cloud Browser, a real Chrome where you sign in to Kickserv yourself; Cotlet does not store your password and you need no Kickserv API key. About the official Kickserv API: Kickserv has an XML API, but only on premium plans (checked 11 Oct 2026). It covers customers, jobs, job charges, notes, schedule events and time entries; it has no endpoints for estimates, invoices or payments. Setup and FAQ: https://cotlet.dev/integrations/kickserv-api-mcp-server
One MCP server for the services your AI needs: memory, files, Microsoft 365, kanban and more.
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
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