Invompt MCP
This server exposes one tool to create Invompt invoices from raw IML YAML and return a hosted invoice link.
Create an invoice with required raw IML YAML (
iml).Optionally override the template to
professional,minimal, ormodern.Get back the created
invoiceIdand hosted invoiceurl.Consult
invompt://spec/iml/v1for IML schema guidance.The README describes broader conversational invoicing: drafting/revising invoices, quotes, estimates, and pro formas, plus reusing saved clients/business settings; email sending requires a registered account.
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., "@Invompt MCPCreate a professional invoice for 8 hours of design work at $125 per hour"
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.
Invompt MCP
Create and manage invoices from a conversation with your AI assistant. Review every detail before you share a document with a client.
Install Invompt for your AI assistant
Start without an account. Create one later to keep your invoices.
Quick start
Open the installation guide and choose your AI assistant.
Connect to Invompt in your browser. Choose Continue without an account, Sign in, or Create account. You can also Deny the connection.
Ask your assistant to draft an invoice, then review it before sharing.
The hosted connection uses OAuth, including when you start without an account. Your AI assistant handles the authorization flow.
Related MCP server: Invoices Generator MCP Server
From conversation to invoice
Describe the work. Include the client, service, quantity, and rate; add tax or payment terms when needed.
Review the draft. Check the client, line items, totals, and dates. Ask for changes in the same conversation.
Choose what happens next. Open the hosted invoice to download its PDF. Sending by email requires a registered account.
Draft an invoice from the project details we just discussed. Set payment terms to 14 days.
What it does
Invompt creates invoices, quotes, estimates, and pro formas from a conversation. It can revise a selected document and reuse saved clients and business settings.
Hosted connection
Use the hosted MCP server at:
https://mcp.invompt.com/mcpThe hosted OAuth connection is the recommended way to get started.
Connection modes
The repository's local-beta CLI and Guest stdio bridge are secondary development options. See the local-beta CLI reference for developer setup details.
Accounts and access
You can begin without an account. When you're ready, ask your connected assistant to help create an Invompt account and keep invoices made through your current connection. Open the link it provides and complete the account flow. Creating an account elsewhere does not automatically find unrelated guest histories. Sending invoices by email requires a registered account.
Security
The hosted connection uses your AI assistant's OAuth flow. The local-beta CLI stores Guest credentials in the macOS Keychain by default; read its security guidance before using that option.
Resources
License
Available Tools
1 toolcreate_invoiceCreate InvoiceB
Create an Invompt invoice from raw IML YAML. Read invompt://spec/iml/v1 first if you need schema guidance.
| Name | Required | Description | Default |
|---|---|---|---|
| iml | Yes | Raw Invoice Markup Language (IML) YAML. | |
| templateId | No | Optional template override. |
Output Schema
| Name | Required | Description |
|---|---|---|
| url | Yes | |
| invoiceId | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions creating an invoice but doesn't disclose behavioral traits like permissions needed, whether the operation is idempotent, error handling, or what the output contains. The reference to external documentation adds some context but insufficiently compensates for the lack of built-in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences, front-loading the main action and following with guidance. There's no wasted text, but it could be slightly more structured by explicitly separating purpose from prerequisites.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema, the description doesn't need to explain return values. However, with no annotations and a mutation operation (create), the description lacks completeness in areas like behavioral context and usage prerequisites, making it adequate but with clear gaps for an agent to use effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 both parameters fully. The description adds minimal value beyond the schema by hinting at the need for schema guidance but doesn't provide additional semantics for the parameters. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create an Invompt invoice') and the input source ('from raw IML YAML'), providing specific verb+resource information. However, it doesn't differentiate from siblings since there are none, and the mention of 'invompt://spec/iml/v1' adds helpful context but doesn't fully clarify the purpose beyond the basic action.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by referencing external schema guidance ('Read invompt://spec/iml/v1 first if you need schema guidance'), which suggests when to prepare inputs. However, it lacks explicit when-to-use guidance, alternatives, or exclusions, leaving usage context somewhat vague.
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.
1 tool update
v0.1.0- First observed
create_invoice
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap with other tools, making disambiguation perfect.
A single tool inherently has consistent naming, as there are no other tools to compare it against for patterns or deviations.
One tool is too few for a server named 'Invompt MCP', which suggests a broader invoicing domain; this feels thin and likely incomplete for typical invoicing workflows.
The server appears focused on invoicing, but with only a create_invoice tool, it lacks essential operations like retrieving, updating, listing, or deleting invoices, making it severely incomplete.
Maintenance
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Invoicing tools for AI assistants: clients, draft invoices with computed tax, and email sending.
1
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