Ramp MCP Server

What does “Ramp” MCP Server do?

The Ramp MCP (Model Context Protocol) Server acts as a bridge between AI assistants and the Ramp financial data platform, enhancing workflows for developers and AI agents. It enables external access to Ramp’s Developer API, allowing the retrieval, analysis, and manipulation of financial data such as transactions, reimbursements, bills, and more. The server implements an ETL pipeline and uses an in-memory SQLite database for efficient data processing and analysis by large language models (LLMs). By exposing database operations, direct data fetching, and data loading tools, Ramp MCP empowers AI clients to perform custom queries, manage datasets, and automate financial-related tasks, all while abstracting API complexity and respecting token/input size limitations.

List of Prompts

No explicit prompt templates are mentioned in the repository or documentation.

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List of Resources

No explicit MCP “resources” are listed in the documentation or code. However, the server provides access to Ramp data objects such as:

• Transactions
• Reimbursements
• Bills
• Locations
• Departments
• Bank Accounts
• Vendors
• Vendor Bank Accounts
• Entities
• Spend Limits
• Spend Programs
• Users

Database tools

• process_data: Sets up and processes data in an ephemeral in-memory database for analysis.
• execute_query: Executes SQL queries against the in-memory database.
• clear_table: Deletes data from tables in the in-memory database.

Fetch tools

• get_ramp_categories: Fetches category data from the Ramp API.
• get_currencies: Fetches supported currencies.

Load tools

• load_transactions: Loads transactions data (requires transactions:read scope).
• load_reimbursements: Loads reimbursements data (requires reimbursements:read scope).
• load_bills: Loads bills data (requires bills:read scope).
• load_locations: Loads location data (requires locations:read scope).
• load_departments: Loads department data (requires departments:read scope).
• load_bank_accounts: Loads bank account data (requires bank_accounts:read scope).
• load_vendors: Loads vendor data (requires vendors:read scope).
• load_vendor_bank_accounts: Loads vendor bank account data (requires vendors:read scope).
• load_entities: Loads entity data (requires entities:read scope).
• load_spend_limits: Loads spend limits (requires limits:read scope).
• load_spend_programs: Loads spend programs (requires spend_programs:read scope).
• load_users: Loads user data (requires users:read scope).

Use Cases of this MCP Server

• Automated Financial Data Analysis: Retrieve and process transaction, reimbursement, and bill data for analytics or reporting.
• Expense Management Automation: Query and manipulate data related to spend limits, departments, and users to streamline expense approvals and controls.
• Custom Workflow Integration: Integrate Ramp data with internal tools or dashboards using LLM-powered agents for tailored business insights.
• Vendor and Payment Tracking: Fetch and analyze vendor, bank account, and payment information for audit or reconciliation workflows.
• Audit and Compliance Automation: Use LLMs to surface anomalies or trends in Ramp’s datasets programmatically, reducing manual review workload.

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How to set it up

Windsurf

No platform-specific instructions found for Windsurf.

Claude

1. Clone the Ramp MCP repository to your local machine.
2. Install uv as the package manager.
3. Edit or create your claude_desktop_config.json in the appropriate directory (typically /Library/Application Support/Claude/).
4. Add the following configuration for the Ramp MCP server:

{
  "mcpServers": {
    "ramp-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/<ABSOLUTE-PATH-TO>/ramp-mcp",
        "run",
        "ramp-mcp",
        "-s",
        "transactions:read,reimbursements:read"
      ],
      "env": {
        "RAMP_CLIENT_ID": "<CLIENT_ID>",
        "RAMP_CLIENT_SECRET": "<CLIENT_SECRET>",
        "RAMP_ENV": "<demo|qa|prd>"
      }
    }
  }
}

5. Save the configuration and (re)start Claude Desktop to load the MCP server.

Securing API Keys:
Use environment variables as shown in the env section above to avoid hard-coding sensitive keys.

Cursor

No platform-specific instructions found for Cursor.

Cline

No platform-specific instructions found for Cline.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "ramp-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “ramp-mcp” to the actual MCP server name and replace the URL with your own MCP server URL.

Overview

Roots support: Not mentioned

Sampling support: Not mentioned

Our opinion

Ramp MCP is a focused, well-scoped MCP server for financial data pipelines and automation. It offers a robust toolset for data loading, querying, and management, but lacks explicit prompt and resource definitions, as well as roots/sampling documentation. Setup and security practices are solid, but broader platform integration and advanced MCP features are not detailed.
Overall, this is a practical, reliable MCP server for Ramp API use cases, but not a reference implementation for the full MCP spec.

MCP Score