The Omi MCP Server enables interaction with the Omi API for managing conversations and memories for specific users.
Key capabilities include:
Retrieve Conversations: Fetch user conversations with pagination, filtering by status, and options to include discarded conversations
Retrieve Memories: Access user memories with pagination options
Create Conversations: Generate new conversations with text content, metadata (geolocation, timestamps), and source information
Create Memories: Create memories by extracting from text or directly providing memory objects with optional tags
Integration Options: Compatible with Claude (via Anthropic Console or API) and Cursor for streamlined API calls
Logging and Testing: Includes built-in logging for debugging and a test client for validating functionality
Handles environment variable configuration for the MCP server to store API keys and application IDs for Omi API authentication
Used for package management and running script commands to build, start, and test the MCP server
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., "@Omi MCP Server fetch the last 3 conversations for user alice-jones"
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 project provides a Model Context Protocol (MCP) server for interacting with the Omi API. The server provides tools for reading conversations and memories, as well as creating new conversations and memories.
Clone the repository
Install dependencies with npm install
Create a .env file with the following variables:
API_KEY=your_api_key
APP_ID=your_app_idRelated MCP server: Omi Memories MCP Server
To install Omi MCP Server for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @fourcolors/omi-mcp --client claudenpm run buildnpm run startFor development with hot-reloading:
npm run devA simple test client is included to interact with the MCP server. After building the project, run:
npm run testOr directly:
./test-mcp-client.jsThis will start the MCP server and provide an interactive menu to test the available tools. The test client uses a default test user ID (test-user-123) for all operations.
To clean the build directory and rebuild from scratch:
npm run rebuildTo use this MCP server with Claude via Anthropic Console or API:
Start the MCP server locally:
npm run startWhen setting up your Claude conversation, configure the MCP connection:
{
"mcp_config": {
"transports": [
{
"type": "stdio",
"executable": {
"path": "/path/to/your/omi-mcp-local/dist/index.js",
"args": []
}
}
]
}
}Example prompt to Claude:
Please fetch the latest 5 conversations for user "user123" using the Omi API.Claude will use the MCP to execute the read_omi_conversations tool:
{
"id": "req-1",
"type": "request",
"method": "tools.read_omi_conversations",
"params": {
"user_id": "user123",
"limit": 5
}
}To use this MCP server with Cursor:
Start the MCP server in a terminal:
npm run startIn Cursor, go to Settings > Extensions > MCP Servers
Add a new MCP server with these settings:
Name: Omi API
URL: stdio:/path/to/your/omi-mcp-local/dist/index.js
Enable the server
Now you can use the Omi tools directly within Cursor. For example:
@Omi API Please fetch memories for user "user123" and summarize them.Cursor will communicate with your MCP server to execute the necessary API calls.
The MCP server provides the following tools:
Retrieves conversations from Omi for a specific user, with optional filters.
Parameters:
user_id (string): The user ID to fetch conversations for
limit (number, optional): Maximum number of conversations to return
offset (number, optional): Number of conversations to skip for pagination
include_discarded (boolean, optional): Whether to include discarded conversations
statuses (string, optional): Comma-separated list of statuses to filter conversations by
Retrieves memories from Omi for a specific user.
Parameters:
user_id (string): The user ID to fetch memories for
limit (number, optional): Maximum number of memories to return
offset (number, optional): Number of memories to skip for pagination
Creates a new conversation in Omi for a specific user.
Parameters:
text (string): The full text content of the conversation
user_id (string): The user ID to create the conversation for
text_source (string): Source of the text content (options: "audio_transcript", "message", "other_text")
started_at (string, optional): When the conversation/event started (ISO 8601 format)
finished_at (string, optional): When the conversation/event ended (ISO 8601 format)
language (string, optional): Language code (default: "en")
geolocation (object, optional): Location data for the conversation
latitude (number): Latitude coordinate
longitude (number): Longitude coordinate
text_source_spec (string, optional): Additional specification about the source
Creates new memories in Omi for a specific user.
Parameters:
user_id (string): The user ID to create memories for
text (string, optional): The text content from which memories will be extracted
memories (array, optional): An array of explicit memory objects to be created directly
content (string): The content of the memory
tags (array of strings, optional): Tags for the memory
text_source (string, optional): Source of the text content
text_source_spec (string, optional): Additional specification about the source
To test the MCP server, you can use the provided test client:
node test-mcp-client.jsThis will start an interactive test client that allows you to:
Get conversations
Get memories
Create a conversation
Quit
The test client uses a default test user ID (test-user-123) for all operations.
The MCP server includes built-in logging functionality that writes to both the console and a log file. This is useful for debugging and monitoring server activity.
Logs are written to logs/mcp-server.log in your project directory. The log file includes timestamps and detailed information about:
Server startup and shutdown
All API requests and responses
Error messages and stack traces
API calls to Omi
Request parameters and response data
You can view the logs in real-time using the tail command:
tail -f logs/mcp-server.logThis will show you live updates as the server processes requests and interacts with the Omi API.
Each log entry follows this format:
[2024-03-21T12:34:56.789Z] Log message hereThe timestamp is in ISO 8601 format, making it easy to correlate events and debug issues.
Creates a new Omi conversation with text content and metadata
| Name | Required | Description | Default |
|---|---|---|---|
| finished_at | No | When the conversation/event ended in ISO 8601 format. Optional. | |
| geolocation | No | Location data for the conversation. Optional object containing latitude and longitude. | |
| language | No | Language code (e.g., "en" for English). Optional, defaults to "en". | en |
| started_at | No | When the conversation/event started in ISO 8601 format. Optional. | |
| text | Yes | The full text content of the conversation | |
| text_source | Yes | Source of the text content. Required. Options: "audio_transcript", "message", "other_text". | |
| text_source_spec | No | Additional specification about the source. Optional. | |
| user_id | Yes | The user ID to create the conversation for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a creation operation, implying it's a write/mutation tool, but doesn't disclose any behavioral traits like permission requirements, rate limits, side effects, or what happens on success/failure. For a creation tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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 a single, efficient sentence that gets straight to the point without unnecessary words. It's appropriately sized for a creation tool, though it could potentially benefit from slightly more context given the lack of annotations and usage guidelines.
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 complexity (8 parameters, nested objects, no output schema, and no annotations), the description is insufficient. It doesn't explain what a successful creation returns, error conditions, or behavioral constraints. For a creation tool with multiple parameters and no structured safety hints, more descriptive context is needed to be complete.
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?
The schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'text content and metadata', which loosely maps to the 'text' and other fields. Since the schema does the heavy lifting, the baseline score of 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 'creates' and the resource 'new Omi conversation', specifying it includes 'text content and metadata'. This distinguishes it from sibling tools like 'read_omi_conversations' (read vs. create) and 'create_omi_memories' (conversation vs. memories). However, it doesn't explicitly differentiate from 'create_omi_memories' beyond the resource name.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over 'create_omi_memories' or whether it's for initial conversation creation versus updates. There's no context about prerequisites, dependencies, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Creates Omi memories by extracting from text or using explicit memory objects
| Name | Required | Description | Default |
|---|---|---|---|
| memories | No | An array of explicit memory objects to be created directly. Either this or text must be provided. | |
| text | No | The text content from which memories will be extracted. Either this or memories must be provided. | |
| text_source | No | Source of the text content. Optional. Options: "email", "social_post", "other". | |
| text_source_spec | No | Additional specification about the source. Optional. | |
| user_id | Yes | The user ID to create memories for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the creation action and two input methods, it doesn't disclose important behavioral traits like whether this is a write operation (implied but not stated), what permissions are needed, whether it's idempotent, what happens on failure, or what the return format looks like. For a creation tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 a single, efficient sentence that clearly states the tool's purpose and two key input methods. It's front-loaded with essential information and contains no redundant or unnecessary words, making it easy to parse quickly.
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 that this is a creation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'Omi memories' are in this context, what the tool returns (e.g., success/failure, created memory IDs), or any behavioral constraints (e.g., rate limits, authentication needs). For a tool with 5 parameters and significant functionality, more context is needed to use it 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%, meaning all parameters are well-documented in the input schema itself. The description adds minimal value beyond the schema by mentioning 'extracting from text' (hinting at the 'text' parameter) and 'using explicit memory objects' (hinting at the 'memories' parameter), but doesn't provide additional semantic context like examples, edge cases, or relationships between parameters. This meets the baseline for high schema coverage.
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 ('creates Omi memories') and specifies two methods ('extracting from text' or 'using explicit memory objects'), which gives a good sense of what the tool does. However, it doesn't differentiate itself from sibling tools like 'create_omi_conversation' or 'read_omi_memories', leaving some ambiguity about when to use this specific memory creation tool versus other memory/conversation tools.
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 mentioning two input methods ('extracting from text' or 'using explicit memory objects'), which provides some context for when to use it. However, it doesn't explicitly state when to choose this tool over alternatives like 'create_omi_conversation' or 'read_omi_memories', nor does it mention any prerequisites or exclusions. The guidance is present but incomplete.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieves user conversations from Omi with pagination and filtering options
| Name | Required | Description | Default |
|---|---|---|---|
| include_discarded | No | Whether to include discarded conversations (default: false) | |
| limit | No | Maximum number of conversations to return (max: 1000, default: 100) | |
| offset | No | Number of conversations to skip for pagination (default: 0) | |
| statuses | No | Comma-separated list of statuses to filter conversations by | |
| user_id | Yes | The user ID to fetch conversations for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'pagination and filtering options', which hints at some behavior, but fails to cover critical aspects like authentication requirements, rate limits, error handling, or what the return format looks like (e.g., JSON structure). For a retrieval tool with 5 parameters, this leaves significant gaps.
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 a single, efficient sentence that front-loads the core purpose ('retrieves user conversations from Omi') and adds relevant details ('with pagination and filtering options'). There is no wasted verbiage, making it highly concise and well-structured.
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 complexity of a retrieval tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on authentication, rate limits, error cases, and the structure of returned data (e.g., conversation objects). Without annotations or an output schema, the agent has insufficient information to handle this tool effectively in context.
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 input schema fully documents all 5 parameters with descriptions. The description adds minimal value by mentioning 'pagination and filtering options', which loosely corresponds to parameters like 'limit', 'offset', and 'statuses', but doesn't provide additional semantics beyond what the schema already specifies. This meets the baseline for high schema coverage.
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 ('retrieves') and resource ('user conversations from Omi'), making the purpose evident. It also mentions 'pagination and filtering options' which adds specificity. However, it doesn't explicitly distinguish this tool from its sibling 'read_omi_memories', which might cause confusion about when to retrieve conversations versus memories.
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 provides no guidance on when to use this tool versus alternatives like 'read_omi_memories' or 'create_omi_conversation'. It mentions filtering options but doesn't specify scenarios or prerequisites for usage, leaving the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieves user memories from Omi with pagination options
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of memories to return (max: 1000, default: 100) | |
| offset | No | Number of memories to skip for pagination (default: 0) | |
| user_id | Yes | The user ID to fetch memories for |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'pagination options', which adds some context about how results are handled, but it does not cover other aspects like rate limits, authentication needs, error conditions, or what the return format looks like. This leaves gaps in understanding the tool's behavior beyond basic retrieval.
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 a single, efficient sentence that directly states the tool's function and key feature (pagination). It is front-loaded with the core purpose and avoids unnecessary words, making it highly concise and well-structured for quick comprehension.
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's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and hints at pagination but lacks details on return values, error handling, or usage context. This leaves the agent with incomplete information for effective tool invocation, though it meets a baseline for a read operation.
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?
The input schema has 100% description coverage, documenting all three parameters (limit, offset, user_id) with details like defaults and constraints. The description adds no additional meaning beyond this, as it only mentions 'pagination options' without elaborating on parameter usage. This meets the baseline for high schema coverage but does not enhance parameter understanding.
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 ('Retrieves') and resource ('user memories from Omi'), making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'read_omi_conversations', which might retrieve a different type of data, so it lacks sibling differentiation for a perfect score.
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 provides no guidance on when to use this tool versus alternatives, such as when to choose it over 'read_omi_conversations' or other siblings. It mentions pagination options but does not specify scenarios or prerequisites for usage, leaving the agent without contextual direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: create vs. read operations for two distinct resources (conversations and memories). The separation between conversations and memories is explicit, and the create/read distinction is unambiguous, leaving no room for confusion or misselection.
All tool names follow a consistent verb_noun pattern with 'create' or 'read' as the verb and 'omi_conversations' or 'omi_memories' as the noun. The naming is perfectly uniform, using snake_case throughout, making the set highly predictable and readable.
With 4 tools, the count is reasonable for a server focused on Omi conversations and memories. It covers create and read operations for both resources, which is well-scoped, though it might feel slightly thin if update or delete operations are expected in the domain, but it's not a significant issue.
The tool set provides create and read operations for both conversations and memories, covering basic CRUD elements. However, there are notable gaps: no update or delete tools for either resource, which could limit agent workflows if modifications or deletions are needed, making the surface incomplete for full lifecycle management.
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