用于 Smallest.ai API 集成的模型上下文协议 (MCP) 服务器实现。该项目提供了与 Smallest.ai 知识库管理系统交互的标准化接口。
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Client App │◄────┤ MCP Server │◄────┤ Smallest.ai │
│ │ │ │ │ API │
└─────────────────┘ └─────────────────┘ └─────────────────┘实现 MCP 客户端协议
处理请求格式
管理响应解析
提供错误处理
协议处理程序
管理 MCP 协议通信
处理客户端连接
将请求路由到适当的工具
工具实现
知识库管理工具
参数验证
响应格式
错误处理
API 集成
Smallest.ai API 通信
身份验证管理
请求/响应处理
知识库管理
数据存储和检索
身份验证和授权
1. Client Request
└─► MCP Protocol Validation
└─► Tool Parameter Validation
└─► API Request Formation
└─► Smallest.ai API Call
└─► Response Processing
└─► Client Response┌─────────────────┐
│ Client Auth │
└────────┬────────┘
│
┌────────▼────────┐
│ MCP Validation │
└────────┬────────┘
│
┌────────▼────────┐
│ API Auth │
└────────┬────────┘
│
┌────────▼────────┐
│ Smallest.ai │
└─────────────────┘Related MCP server: Rememberizer MCP Server
该项目实现了一个 MCP 服务器，作为客户端和 Smallest.ai API 之间的中间件。它提供了一种通过模型上下文协议 (MCP) 与 Smallest.ai 知识库管理功能进行交互的标准化方式。
[Client Application] <---> [MCP Server] <---> [Smallest.ai API]MCP 服务器
处理客户端请求
管理 API 通信
提供标准化的响应
实现错误处理
知识库工具
listKnowledgeBases ：列出所有知识库
createKnowledgeBase ：创建新的知识库
getKnowledgeBase ：检索特定知识库详细信息
文档资源
可在docs://smallest.ai获取
提供使用说明和示例
Node.js 18+ 或 Bun 运行时
Smallest.ai API 密钥
TypeScript 知识
克隆存储库：
git clone https://github.com/yourusername/MCP-smallest.ai.git
cd MCP-smallest.ai安装依赖项：
bun install在根目录中创建一个.env文件：
SMALLEST_AI_API_KEY=your_api_key_here使用 Smallest.ai API 配置创建一个config.ts文件：
export const config = {
API_KEY: process.env.SMALLEST_AI_API_KEY,
BASE_URL: 'https://atoms-api.smallest.ai/api/v1'
};bun run index.tsbun run test-client.ts列出知识库
await client.callTool({
name: "listKnowledgeBases",
arguments: {}
});创建知识库
await client.callTool({
name: "createKnowledgeBase",
arguments: {
name: "My Knowledge Base",
description: "Description of the knowledge base"
}
});获取知识库
await client.callTool({
name: "getKnowledgeBase",
arguments: {
id: "knowledge_base_id"
}
});所有回复都遵循以下结构：
{
content: [{
type: "text",
text: JSON.stringify(data, null, 2)
}]
}服务器实现了全面的错误处理：
HTTP 错误
API 错误
参数验证错误
类型安全的错误响应
MCP-smallest.ai/
├── index.ts # MCP server implementation
├── test-client.ts # Test client implementation
├── config.ts # Configuration file
├── package.json # Project dependencies
├── tsconfig.json # TypeScript configuration
└── README.md # This file在index.ts中定义该工具：
server.tool(
"toolName",
{
param1: z.string(),
param2: z.number()
},
async (args) => {
// Implementation
}
);更新资源中的文档：
server.resource(
"documentation",
"docs://smallest.ai",
async (uri) => ({
contents: [{
uri: uri.href,
text: `Updated documentation...`
}]
})
);API 密钥存储在环境变量中
所有请求都经过身份验证
参数验证已实现
错误消息已净化
分叉存储库
创建你的功能分支（ git checkout -b feature/amazing-feature ）
提交您的更改（ git commit -m 'Add some amazing feature' ）
推送到分支（ git push origin feature/amazing-feature ）
打开拉取请求
该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅LICENSE文件。
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | ||
| name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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?
Tool has no description.
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.
v1.0.0createKnowledgeBasegetKnowledgeBaselistKnowledgeBasesScored across 3 tools
Each tool has a clearly distinct purpose: create, get, and list operations on knowledge bases. There is no overlap in functionality, and the action verbs (create, get, list) are unambiguous and standard for CRUD operations.
All tool names follow a consistent camelCase pattern with a verb-noun structure (createKnowledgeBase, getKnowledgeBase, listKnowledgeBases). The naming is predictable and uniform across all three tools.
With only 3 tools, the set feels thin for a knowledge base management server, as it lacks update and delete operations. However, it covers basic create, retrieve, and list functions, which is minimal but functional for a small scope.
The tools provide create, get, and list operations, but there are notable gaps such as update and delete for knowledge bases. This limits full lifecycle management, though core retrieval and creation are covered.
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