该项目提供了一个模型上下文协议 (MCP) 服务器，用于与 Omi API 交互。该服务器提供了读取对话和记忆以及创建新对话和记忆的工具。
克隆存储库
使用npm install安装依赖项
使用以下变量创建.env文件：
API_KEY=your_api_key
APP_ID=your_app_idRelated MCP server: Omi Memories MCP Server
要通过Smithery自动为 Claude Desktop 安装 Omi MCP 服务器：
npx -y @smithery/cli install @fourcolors/omi-mcp --client claudenpm run buildnpm run start对于使用热重载进行开发：
npm run dev其中包含一个简单的测试客户端，用于与 MCP 服务器交互。构建项目后，运行：
npm run test或者直接：
./test-mcp-client.js这将启动 MCP 服务器并提供一个交互式菜单来测试可用的工具。测试客户端使用默认测试用户 ID ( test-user-123 ) 执行所有操作。
清理构建目录并从头开始重建：
npm run rebuild要通过 Anthropic Console 或 API 将此 MCP 服务器与 Claude 一起使用：
本地启动 MCP 服务器：
npm run start设置 Claude 对话时，请配置 MCP 连接：
{
"mcp_config": {
"transports": [
{
"type": "stdio",
"executable": {
"path": "/path/to/your/omi-mcp-local/dist/index.js",
"args": []
}
}
]
}
}给 Claude 的示例提示：
Please fetch the latest 5 conversations for user "user123" using the Omi API.Claude 将使用 MCP 执行read_omi_conversations工具：
{
"id": "req-1",
"type": "request",
"method": "tools.read_omi_conversations",
"params": {
"user_id": "user123",
"limit": 5
}
}要将此 MCP 服务器与 Cursor 一起使用：
在终端中启动 MCP 服务器：
npm run start在 Cursor 中，前往“设置”>“扩展”>“MCP 服务器”
使用以下设置添加新的 MCP 服务器：
名称：Omi API
网址：stdio:/path/to/your/omi-mcp-local/dist/index.js
启用服务器
现在，您可以直接在 Cursor 中使用 Omi 工具。例如：
@Omi API Please fetch memories for user "user123" and summarize them.Cursor 将与您的 MCP 服务器通信以执行必要的 API 调用。
MCP 服务器提供以下工具：
使用可选过滤器从 Omi 检索特定用户的对话。
参数：
user_id （字符串）：用于获取对话的用户 ID
limit （数字，可选）：返回的最大对话数量
offset （数字，可选）：分页时要跳过的对话数
include_discarded （布尔值，可选）：是否包含丢弃的对话
statuses （字符串，可选）：以逗号分隔的状态列表，用于过滤对话
从 Omi 检索特定用户的记忆。
参数：
user_id (string): 获取记忆的用户 ID
limit （数字，可选）：返回的最大记忆数量
offset （数字，可选）：分页时要跳过的内存数量
在 Omi 中为特定用户创建新对话。
参数：
text （字符串）：对话的全文内容
user_id （字符串）：创建对话的用户 ID
text_source （字符串）：文本内容的来源（选项：“audio_transcript”、“message”、“other_text”）
started_at （字符串，可选）：对话/事件开始的时间（ISO 8601 格式）
finished_at （字符串，可选）：对话/事件结束的时间（ISO 8601 格式）
language （字符串，可选）：语言代码（默认值：“en”）
geolocation （对象，可选）：对话的位置数据
latitude （数字）：纬度坐标
longitude （数字）：经度坐标
text_source_spec （字符串，可选）：有关来源的附加规范
为特定用户在近江创建新的回忆。
参数：
user_id （字符串）：要创建记忆的用户 ID
text （字符串，可选）：将从中提取记忆的文本内容
memories （数组，可选）：直接创建的显式内存对象数组
content （字符串）：内存的内容
tags （字符串数组，可选）：内存的标签
text_source （字符串，可选）：文本内容的来源
text_source_spec （字符串，可选）：有关来源的附加规范
要测试 MCP 服务器，您可以使用提供的测试客户端：
node test-mcp-client.js这将启动一个交互式测试客户端，允许您：
获取对话
获取回忆
创建对话
辞职
测试客户端对所有操作使用默认测试用户 ID ( test-user-123 )。
MCP 服务器内置日志记录功能，可将日志写入控制台和日志文件。这对于调试和监控服务器活动非常有用。
日志会写入项目目录下的logs/mcp-server.log文件。日志文件包含时间戳和以下详细信息：
服务器启动和关闭
所有 API 请求和响应
错误消息和堆栈跟踪
对 Omi 的 API 调用
请求参数和响应数据
您可以使用tail命令实时查看日志：
tail -f logs/mcp-server.log这将向您显示服务器处理请求并与 Omi API 交互时的实时更新。
每个日志条目都遵循以下格式：
[2024-03-21T12:34:56.789Z] Log message here时间戳采用 ISO 8601 格式，可轻松关联事件和调试问题。
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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