Este proyecto proporciona un servidor de Protocolo de Contexto de Modelo (MCP) para interactuar con la API de Omi. El servidor proporciona herramientas para leer conversaciones y memorias, así como para crear nuevas conversaciones y memorias.
Clonar el repositorio
Instalar dependencias con npm install
Crea un archivo .env con las siguientes variables:
API_KEY=your_api_key
APP_ID=your_app_idRelated MCP server: Omi Memories MCP Server
Para instalar Omi MCP Server para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install @fourcolors/omi-mcp --client claudenpm run buildnpm run startPara desarrollo con recarga en caliente:
npm run devSe incluye un cliente de prueba sencillo para interactuar con el servidor MCP. Tras compilar el proyecto, ejecute:
npm run testO directamente:
./test-mcp-client.jsEsto iniciará el servidor MCP y proporcionará un menú interactivo para probar las herramientas disponibles. El cliente de prueba utiliza un ID de usuario de prueba predeterminado ( test-user-123 ) para todas las operaciones.
Para limpiar el directorio de compilación y reconstruir desde cero:
npm run rebuildPara utilizar este servidor MCP con Claude a través de Anthropic Console o API:
Inicie el servidor MCP localmente:
npm run startAl configurar su conversación de Claude, configure la conexión MCP:
{
"mcp_config": {
"transports": [
{
"type": "stdio",
"executable": {
"path": "/path/to/your/omi-mcp-local/dist/index.js",
"args": []
}
}
]
}
}Ejemplo de mensaje para Claude:
Please fetch the latest 5 conversations for user "user123" using the Omi API.Claude utilizará el MCP para ejecutar la herramienta read_omi_conversations :
{
"id": "req-1",
"type": "request",
"method": "tools.read_omi_conversations",
"params": {
"user_id": "user123",
"limit": 5
}
}Para utilizar este servidor MCP con Cursor:
Inicie el servidor MCP en una terminal:
npm run startEn Cursor, vaya a Configuración > Extensiones > Servidores MCP
Agregue un nuevo servidor MCP con estas configuraciones:
Nombre: Omi API
URL: stdio:/ruta/a/su/omi-mcp-local/dist/index.js
Habilitar el servidor
Ahora puedes usar las herramientas de Omi directamente desde Cursor. Por ejemplo:
@Omi API Please fetch memories for user "user123" and summarize them.El cursor se comunicará con su servidor MCP para ejecutar las llamadas API necesarias.
El servidor MCP proporciona las siguientes herramientas:
Recupera conversaciones de Omi para un usuario específico, con filtros opcionales.
Parámetros:
user_id (cadena): El ID del usuario para el que se buscarán las conversaciones
limit (número, opcional): Número máximo de conversaciones a devolver
offset (número, opcional): Número de conversaciones a omitir para la paginación
include_discarded (booleano, opcional): si se deben incluir las conversaciones descartadas
statuses (cadena, opcional): lista de estados separados por comas para filtrar conversaciones
Recupera recuerdos de Omi para un usuario específico.
Parámetros:
user_id (cadena): El ID del usuario para el que se buscarán los recuerdos
limit (número, opcional): Número máximo de memorias a devolver
offset (número, opcional): Número de memorias a omitir para la paginación
Crea una nueva conversación en Omi para un usuario específico.
Parámetros:
text (cadena): El contenido de texto completo de la conversación
user_id (cadena): El ID del usuario para el que se creará la conversación
text_source (cadena): Fuente del contenido del texto (opciones: "audio_transcript", "message", "other_text")
started_at (cadena, opcional): cuándo comenzó la conversación/evento (formato ISO 8601)
finished_at (cadena, opcional): cuándo finalizó la conversación/evento (formato ISO 8601)
language (cadena, opcional): Código de idioma (predeterminado: "en")
geolocation (objeto, opcional): Datos de ubicación para la conversación
latitude (número): coordenada de latitud
longitude (número): coordenada de longitud
text_source_spec (cadena, opcional): Especificación adicional sobre la fuente
Crea nuevos recuerdos en Omi para un usuario específico.
Parámetros:
user_id (cadena): El ID del usuario para el que se crearán recuerdos
text (cadena, opcional): El contenido del texto del que se extraerán las memorias
memories (matriz, opcional): una matriz de objetos de memoria explícitos que se crearán directamente
content (cadena): El contenido de la memoria
tags (matriz de cadenas, opcional): etiquetas para la memoria
text_source (cadena, opcional): Fuente del contenido del texto
text_source_spec (cadena, opcional): Especificación adicional sobre la fuente
Para probar el servidor MCP, puede utilizar el cliente de prueba proporcionado:
node test-mcp-client.jsEsto iniciará un cliente de prueba interactivo que le permitirá:
Obtener conversaciones
Consigue recuerdos
Crear una conversación
Abandonar
El cliente de prueba utiliza un ID de usuario de prueba predeterminado ( test-user-123 ) para todas las operaciones.
El servidor MCP incluye una función de registro integrada que escribe tanto en la consola como en un archivo de registro. Esto resulta útil para depurar y supervisar la actividad del servidor.
Los registros se guardan en logs/mcp-server.log en el directorio del proyecto. El archivo de registro incluye marcas de tiempo e información detallada sobre:
Inicio y apagado del servidor
Todas las solicitudes y respuestas de API
Mensajes de error y seguimientos de pila
Llamadas API a Omi
Parámetros de solicitud y datos de respuesta
Puede ver los registros en tiempo real utilizando el comando tail :
tail -f logs/mcp-server.logEsto le mostrará actualizaciones en vivo a medida que el servidor procesa solicitudes e interactúa con la API de Omi.
Cada entrada de registro sigue este formato:
[2024-03-21T12:34:56.789Z] Log message hereLa marca de tiempo está en formato ISO 8601, lo que facilita la correlación de eventos y la depuración de problemas.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0create_omi_conversationcreate_omi_memoriesread_omi_conversationsread_omi_memoriesScored across 4 tools
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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