How do I use housecallpro-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@housecallpro-mcp Register a new customer named Alice Johnson with phone 555-9876" 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.

housecallpro-mcp

by NoBanks

Overview Schema Related Servers Score Discussions

Python

Remote

housecallpro-mcp

License: MIT Python MCP

MCP server for Housecall Pro, the mid-market trades vertical SaaS (HVAC, plumbing, electrical, garage, pest, landscaping). 5 tools for AI agents to register new customers, schedule jobs, create estimates, log inbound leads, and generate invoices (env-gated) against active jobs.

As of May 2026, Housecall Pro does not ship an official MCP server. Two low-visibility community attempts exist. This is the production-quality, install-ready Python rail.

The 5 tools

Tool	Purpose	Gated?
`create_pro_customer`	Register a new customer in the CRM	No, safe
`schedule_new_job`	Schedule a new trade job for an existing customer	No, safe
`create_estimate`	Generate a pricing estimate or proposal	No, safe
`submit_new_lead`	Log a new marketing or inbound prospect lead	No, safe
`generate_job_invoice`	Post an official invoice against an active job	YES, `HOUSECALLPRO_ALLOW_INVOICE=true` required

Related MCP server: ghl-mcp-server-v2

Install

pip install housecallpro-mcp

Configure

export HOUSECALLPRO_API_KEY="your-housecallpro-api-key"
export HOUSECALLPRO_ALLOW_INVOICE="false"   # set to "true" to enable invoice creation

Get an API key in your Housecall Pro account settings. The key is sent as a Bearer token; keep it server-side.

Use with Claude Desktop

{
  "mcpServers": {
    "housecallpro": {
      "command": "housecallpro-mcp",
      "env": {
        "HOUSECALLPRO_API_KEY": "your-housecallpro-api-key",
        "HOUSECALLPRO_ALLOW_INVOICE": "false"
      }
    }
  }
}

Restart Claude Desktop. The 5 Housecall Pro tools are now available.

Use case: AI receptionist + sales agent for a trades business

Typical agent flow for inbound calls and quotes:

Call create_pro_customer(first_name, last_name, mobile_phone) to register the caller in CRM
Call schedule_new_job(customer_id, description, start_time, end_time) to book the service window
After the diagnostic call: create_estimate(customer_id, note) to propose pricing
For web-form / chat inbound: submit_new_lead(customer_id, description) to log the prospect in the sales funnel
(After job completion + with HOUSECALLPRO_ALLOW_INVOICE=true): generate_job_invoice(job_id) to bill the customer

Safety note

The invoice action is intentionally gated behind an explicit env var. The default install does not bill customers. Set HOUSECALLPRO_ALLOW_INVOICE="true" only when the AI agent workflow has been explicitly approved for billing actions.

Architecture

Public MIT-licensed wrapper around the Housecall Pro REST API
Async HTTP via httpx
pydantic v2 input validation
Bearer API key auth, server-side only
Rate-limit aware (429 returns a clean error)
Invoice action gated by env flag

Development

git clone https://github.com/NoBanks/housecallpro-mcp.git
cd housecallpro-mcp
pip install -e ".[dev]"
pytest

License

MIT. See LICENSE.

Author

Ryan Hammer (NoBanks). Solo founder + engineer. Built this and 14 other MCP servers as part of a sprint to expose AI agent rails for the products and platforms shipping daily.

GitHub: @NoBanks
X/Twitter: @livingagentic
Site: livingagentic.me, nohumannearby.com

Open to AI engineering roles, contract or full-time, remote-only.

Available Tools

5 tools

create_estimateA

Generate a pricing estimate or service proposal for a customer. Used by AI sales agents to quote work after diagnostic conversations.

ParametersJSON Schema

Name	Required	Description	Default
`customer_id`	Yes	Customer ID to associate the estimate with
`note`	Yes	Memo description for the estimate

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses the tool creates an estimate, implying mutation, but does not detail authorization needs, error states, idempotency, or side effects. Basic transparency is present, but deeper behavioral traits are missing.

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 two sentences long, with no redundant words. It front-loads the primary verb and object ('Generate a pricing estimate or service proposal') and immediately follows with usage context. Every sentence earns its place.

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?

For a simple tool with only two required parameters and no output schema, the description is fairly complete. It covers purpose and usage context. However, it lacks information about return values (e.g., estimate ID) and potential error conditions, which would improve completeness.

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

Parameters3/5

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

Schema coverage is 100% with clear descriptions for both parameters. The description adds little beyond the schema, simply restating that the note is a 'memo description.' It does not provide additional semantics, thus baseline score of 3 is appropriate.

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 clearly states the tool's purpose: 'Generate a pricing estimate or service proposal for a customer.' It distinguishes from siblings like generate_job_invoice by specifying it is for quoting after diagnostic conversations, making it easy for an agent to select correctly.

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?

The description provides explicit usage context: 'Used by AI sales agents to quote work after diagnostic conversations.' This tells an agent when to use the tool, though it does not explicitly mention when not to use it or direct alternatives.

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

create_pro_customerA

Register a new customer in the Housecall Pro CRM. Used by AI receptionists to capture inbound caller details before scheduling a job.

ParametersJSON Schema

Name	Required	Description
`first_name`	Yes	Customer first name
`last_name`	Yes	Customer last name
`email`	No	Customer email address
`mobile_phone`	Yes	Customer mobile phone number (E.164 format preferred)

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It describes a creation action but lacks details on side effects (e.g., duplicate handling), authorization requirements, or output behavior. This is insufficient for a write operation with no annotations.

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 two concise sentences with no superfluous information. It is front-loaded with the core action and use case, earning its place efficiently.

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

Completeness3/5

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

The description is adequate for a simple creation tool with four parameters, but it lacks information about return values, error states, or any constraints. Without an output schema, additional detail on what the tool returns (e.g., customer ID) would enhance completeness.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents each parameter. The description adds 'capture inbound caller details before scheduling a job' but does not provide additional meaning or usage nuances beyond what the schema already conveys.

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 clearly states the tool registers a new customer in the Housecall Pro CRM and specifies the use case for AI receptionists capturing inbound caller details before scheduling a job. This verb+resource pairing is specific and distinguishes it from sibling tools like create_estimate or schedule_new_job.

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?

The description explicitly mentions the tool is used by AI receptionists to capture inbound caller details before scheduling a job, providing clear context. While it doesn't explicitly state when not to use it or list alternatives, the context effectively implies its purpose relative to siblings.

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

generate_job_invoiceA

Post an official invoice against an active job. GATED behind HOUSECALLPRO_ALLOW_INVOICE env var. Returns an error if not enabled. This action creates a billable customer-facing invoice.

ParametersJSON Schema

Name	Required	Description	Default
`job_id`	Yes	Active job ID to invoice

TDQS

A3.7/5.0

Behavior4/5

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

Discloses that it is gated behind an environment variable and returns an error if not enabled, and that it creates a billable customer-facing invoice. Without annotations, this provides useful behavioral context beyond just the action.

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

Conciseness4/5

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

Three sentences, no redundancy. Front-loaded with the main action. The gating condition is relevant but could potentially be integrated more succinctly.

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 tool's simplicity (one parameter, no output schema), the description covers the core purpose, a key behavioral gate, and the nature of the invoice created. Missing details like success response or effects on job status but acceptable for a straightforward action.

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

Parameters3/5

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

Schema coverage is 100% with parameter description 'Active job ID to invoice'. The tool description reinforces 'active' but adds minimal meaning beyond the schema for the single parameter.

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?

Description states 'Post an official invoice against an active job,' clearly indicating the verb, resource, and context. It distinguishes from siblings like create_estimate or schedule_new_job which serve different purposes.

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?

No guidance on when to use or alternatives. The description mentions a gating env var but does not specify situations where this tool is appropriate versus other invoicing or billing tools.

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

schedule_new_jobB

Create and schedule a new trade job associated with an existing customer. Used by AI dispatchers to book service windows on the calendar.

ParametersJSON Schema

Name	Required	Description
`customer_id`	Yes	Unique customer ID from create_pro_customer
`description`	Yes	Job description and requirements
`start_time`	Yes	ISO 8601 start date
`end_time`	Yes	ISO 8601 end date

TDQS

B3.4/5.0

Behavior2/5

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

No annotations provided, so description must fully disclose behavior. Mentions creation and scheduling but lacks details on side effects, authorization requirements, or constraints like overlapping times or customer existence validation.

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?

Two concise sentences, front-loaded with key action and audience. No unnecessary words or repetition.

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?

With no output schema, no annotations, and four required parameters, the description is too sparse. Lacks details on return values, error conditions, or prerequisites (e.g., customer must exist). Incomplete for a scheduling tool.

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

Parameters3/5

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

Schema coverage is 100%; description adds minimal value beyond schema—only notes customer_id comes from create_pro_customer. Baseline 3 appropriate as schema already documents parameters thoroughly.

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?

Clearly states the verb 'create and schedule' and the resource 'trade job associated with an existing customer'. Differentiates from siblings like create_estimate or create_pro_customer by focusing on scheduling a job for an existing customer.

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?

Provides context that it is used by AI dispatchers to book service windows, but does not explicitly state when to use versus alternatives or when not to use. Implied usage but no exclusion criteria.

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

submit_new_leadA

Log a new marketing or inbound prospect lead in the Housecall Pro sales funnel. Used by AI agents handling inbound chat or web form submissions.

ParametersJSON Schema

Name	Required	Description	Default
`customer_id`	Yes	Associated customer profile ID
`description`	Yes	Product interest or issue detail

TDQS

A3.8/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It only says 'Log a new...lead' without disclosing behavioral traits such as side effects, idempotency, permissions needed, or error handling. The description lacks transparency beyond the basic action.

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 two sentences long, front-loaded with the core action, and every word is informative. No redundant or unnecessary text.

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 tool's simplicity (2 required parameters, no output schema), the description adequately covers its purpose and usage context. It does not need to explain return values. The description is sufficient for an AI agent to understand when and how to use it.

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

Parameters3/5

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

Schema coverage is 100% and the input schema already describes the parameters ('customer_id' as 'Associated customer profile ID' and 'description' as 'Product interest or issue detail'). The description does not add any additional meaning beyond the schema, so it meets the baseline of 3.

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 clearly states the tool's purpose: 'Log a new marketing or inbound prospect lead in the Housecall Pro sales funnel.' It specifies the verb 'log' and the resource 'lead', and distinguishes from siblings like 'create_estimate' or 'schedule_new_job' by focusing on inbound marketing leads.

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?

The description gives context: 'Used by AI agents handling inbound chat or web form submissions.' This implies when to use, but does not explicitly state when not to use or provide alternatives. However, sibling tools like 'create_pro_customer' or 'generate_job_invoice' serve as implicit alternatives.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

5 tool updatesv0.1.0May 29, 2026
- First observedcreate_estimate
- First observedcreate_pro_customer
- First observedgenerate_job_invoice
- First observedschedule_new_job
- First observedsubmit_new_lead

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4/5.0

Disambiguation5/5

Each tool targets a distinct business function (estimate, customer, invoice, job, lead) with no overlapping purposes, ensuring clear differentiation for an agent.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern (create_, generate_, schedule_, submit_), making the surface predictable and easy to navigate.

Tool Count5/5

With 5 tools, the server covers core CRM workflows without being bloated or sparse—appropriate for a focused home service management integration.

Completeness4/5

The tools cover essential actions (create, schedule, invoice, lead) but lack update/delete operations or search, leaving minor gaps for advanced workflows.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

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