Click on "Install 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., "@Ambivo MCP Server show me leads created this week with attribution_source google_ads"
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.
This Claude MCP (Model Context Protocol) server provides access to Ambivo API endpoints for natural language querying of entity data with Claude AI.
Natural Language Queries: Execute natural language queries against entity data using the /entity/natural_query endpoint
JWT Authentication: Secure access using Bearer token authentication
Rate Limiting: Built-in rate limiting to prevent API abuse
Token Caching: Efficient token validation with caching
Error Handling: Comprehensive error handling with detailed error messages
Retry Logic: Automatic retry with exponential backoff for failed requests
Related MCP server: Dataverse MCP Server
set_auth_tokenSet the JWT Bearer token for authentication with the Ambivo API.
Parameters:
token (string, required): JWT Bearer token
Usage:
{
"token": "your-jwt-token-here"
}natural_queryExecute natural language queries against Ambivo entity data.
Parameters:
query (string, required): Natural language query describing what data you want
response_format (string, optional): Response format - "table", "natural", or "both" (default: "both")
Example queries:
"Show me leads created this week"
"Find contacts with gmail addresses"
"List opportunities worth more than $10,000"
"Show me leads with attribution_source google_ads from the last 7 days"
Usage:
{
"query": "Show me leads created this week with attribution_source google_ads",
"response_format": "both"
}This is a pure Claude-based MCP server implementation for the Ambivo API, designed to work seamlessly with Claude Desktop and other Claude-compatible MCP clients. It enables natural language interaction with your Ambivo CRM data through Claude's powerful language understanding capabilities.
pip install ambivo-mcp-servergit clone https://github.com/ambivo-corp/ambivo-mcp-server.git
cd ambivo-mcp-server
pip install -e .# If installed via pip
ambivo-mcp-server
# Or using Python module
python -m ambivo_mcp_server.serverThe server uses the following default configuration:
Base URL: https://goferapi.ambivo.com
Timeout: 30 seconds
Content Type: application/json
You can modify these settings in the AmbivoAPIClient class if needed.
First, set your authentication token using the set_auth_token tool
The token will be included in all subsequent API requests as a Bearer token
The token should be a valid JWT token from your Ambivo API authentication
The server provides comprehensive error handling:
Authentication errors: Clear messages when token is missing or invalid
HTTP errors: Detailed HTTP status codes and response messages
Validation errors: Parameter validation with helpful error messages
Network errors: Timeout and connection error handling
This MCP server interfaces with these Ambivo API endpoints:
/entity/natural_queryMethod: POST
Purpose: Process natural language queries for entity data retrieval
Authentication: Required (JWT Bearer token)
Content-Type: application/json
/entity/dataMethod: POST
Purpose: Direct entity data access with structured parameters
Authentication: Required (JWT Bearer token)
Content-Type: application/json
Set Authentication:
{
"tool": "set_auth_token",
"arguments": {
"token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
}
}Natural Language Query:
{
"tool": "natural_query",
"arguments": {
"query": "Show me all leads created in the last 30 days with phone numbers",
"response_format": "both"
}
}Direct Entity Query:
{
"tool": "entity_data",
"arguments": {
"entity_type": "contact",
"filters": {"email": {"$regex": "@gmail.com$"}},
"limit": 100,
"sort": {"created_date": -1}
}
}To extend this MCP server:
Add new tools: Implement additional tools in the handle_list_tools() and handle_call_tool() functions
Modify API client: Extend the AmbivoAPIClient class to support additional endpoints
Update configuration: Modify default settings in the configuration section
Common Issues:
"Authentication required" error: Ensure you've called set_auth_token first
HTTP 401/403 errors: Verify your JWT token is valid and not expired
Connection timeout: Check network connectivity and API endpoint availability
Invalid parameters: Review the tool schemas for required and optional parameters
Logging:
The server logs important events and errors. Check the console output for debugging information.
Execute natural language queries against Ambivo entity data. This tool processes natural language queries and returns structured data about leads, contacts, opportunities, and other entities.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language query describing what data you want to retrieve. Examples: 'Show me leads created this week', 'Find contacts with gmail addresses', 'List opportunities worth more than $10,000' | |
| response_format | No | Format of the response: 'table' for structured data, 'natural' for natural language description, 'both' for both formats | both |
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 states it processes queries and returns structured data, but does not disclose side effects, idempotency, rate limits, or behavior for ambiguous queries. This is insufficient for a tool that accepts free-form natural language.
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 two sentences with no filler. It efficiently conveys the tool's purpose and parameter usage. Every sentence adds value.
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 simple input schema (2 params) and no output schema, the description is fairly complete. It explains what the tool does and the parameters. However, it could mention the return format (e.g., JSON) or potential limitations, making it slightly incomplete.
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% and includes examples. The description adds some context (e.g., listing entity types) but largely overlaps with the schema's parameter descriptions. Baseline 3 is appropriate as the description does not significantly enhance understanding beyond the schema.
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 tool executes natural language queries against Ambivo entity data, specifying supported entities like leads, contacts, opportunities. It is specific and distinguishes from the sibling tool 'set_auth_token' which handles authentication.
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 (for querying entity data) but does not provide explicit guidance on when to use versus alternatives, nor does it mention when not to use it. Since there is only one sibling (auth token), the context is limited but could benefit from clarification.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Set the authentication token for API requests. This must be called before using other tools to authenticate with the Ambivo API.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes | JWT Bearer token for authentication with Ambivo API |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It only says 'Set' with no details on whether the token overwrites, persists, or if there are validation checks. This is minimal disclosure for a mutation tool.
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?
Two sentences, each serving a purpose: action and usage context. No extraneous wording, efficient and direct.
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 a simple tool with one parameter and no output schema, the description adequately covers the prerequisite nature and basic action. It could mention error behavior or state persistence, but overall it's sufficient.
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 coverage is 100% and the description mirrors the schema's description of 'JWT Bearer token'. It adds no new meaning beyond what the schema already provides, so baseline 3 is appropriate.
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 verb 'Set' and the resource 'authentication token for API requests', and specifies it must be called before other tools, distinguishing it from the sibling tool 'natural_query'.
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?
It explicitly states that this tool must be called before using other tools for authentication, providing clear context. No alternatives or exclusions are mentioned, but the prerequisite advice is strong enough for a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The two tools have completely distinct purposes: one handles authentication setup, the other executes queries. No overlap or confusion possible.
Both tools use consistent snake_case naming, and each name clearly indicates its function ('set_auth_token' for authentication, 'natural_query' for querying).
Only 2 tools for a domain that involves multiple entities (leads, contacts, opportunities). This is insufficient to cover typical operations, though the natural_query tool may encapsulate many actions.
The server claims to handle leads, contacts, and opportunities, but provides no CRUD tools or entity-specific operations. A single query tool is severely incomplete for data management tasks.
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