Eine Model Context Protocol (MCP)-Serverimplementierung für die Smallest.ai API-Integration. Dieses Projekt bietet eine standardisierte Schnittstelle für die Interaktion mit dem Wissensdatenbank-Managementsystem von Smallest.ai.
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Client App │◄────┤ MCP Server │◄────┤ Smallest.ai │
│ │ │ │ │ API │
└─────────────────┘ └─────────────────┘ └─────────────────┘Implementiert das MCP-Clientprotokoll
Verarbeitet die Anforderungsformatierung
Verwaltet die Antwortanalyse
Bietet Fehlerbehandlung
Protokollhandler
Verwaltet die MCP-Protokollkommunikation
Verwaltet Clientverbindungen
Leitet Anfragen an die entsprechenden Tools weiter
Tool-Implementierung
Tools zur Wissensdatenbankverwaltung
Parametervalidierung
Antwortformatierung
Fehlerbehandlung
API-Integration
Smallest.ai API-Kommunikation
Authentifizierungsverwaltung
Anfrage-/Antwortverarbeitung
Wissensdatenbankverwaltung
Datenspeicherung und -abruf
Authentifizierung und Autorisierung
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
Dieses Projekt implementiert einen MCP-Server, der als Middleware zwischen Clients und der Smallest.ai-API fungiert. Es bietet eine standardisierte Möglichkeit zur Interaktion mit den Wissensdatenbank-Verwaltungsfunktionen von Smallest.ai über das Model Context Protocol.
[Client Application] <---> [MCP Server] <---> [Smallest.ai API]MCP-Server
Bearbeitet Clientanfragen
Verwaltet die API-Kommunikation
Bietet standardisierte Antworten
Implementiert die Fehlerbehandlung
Wissensdatenbank-Tools
listKnowledgeBases : Listet alle Wissensdatenbanken auf
createKnowledgeBase : Erstellt neue Wissensdatenbanken
getKnowledgeBase : Ruft spezifische Wissensdatenbankdetails ab
Dokumentationsressource
Verfügbar unter docs://smallest.ai
Bietet Nutzungsanweisungen und Beispiele
Node.js 18+ oder Bun-Laufzeit
Smallest.ai API-Schlüssel
TypeScript-Kenntnisse
Klonen Sie das Repository:
git clone https://github.com/yourusername/MCP-smallest.ai.git
cd MCP-smallest.aiInstallieren Sie Abhängigkeiten:
bun installErstellen Sie eine .env Datei im Stammverzeichnis:
SMALLEST_AI_API_KEY=your_api_key_hereErstellen Sie eine config.ts Datei mit Ihrer Smallest.ai-API-Konfiguration:
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.tsWissensdatenbanken auflisten
await client.callTool({
name: "listKnowledgeBases",
arguments: {}
});Wissensdatenbank erstellen
await client.callTool({
name: "createKnowledgeBase",
arguments: {
name: "My Knowledge Base",
description: "Description of the knowledge base"
}
});Wissensdatenbank abrufen
await client.callTool({
name: "getKnowledgeBase",
arguments: {
id: "knowledge_base_id"
}
});Alle Antworten folgen dieser Struktur:
{
content: [{
type: "text",
text: JSON.stringify(data, null, 2)
}]
}Der Server implementiert eine umfassende Fehlerbehandlung:
HTTP-Fehler
API-Fehler
Parametervalidierungsfehler
Typsichere Fehlerantworten
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 fileDefinieren Sie das Tool in index.ts :
server.tool(
"toolName",
{
param1: z.string(),
param2: z.number()
},
async (args) => {
// Implementation
}
);Aktualisieren Sie die Dokumentation in der Ressource:
server.resource(
"documentation",
"docs://smallest.ai",
async (uri) => ({
contents: [{
uri: uri.href,
text: `Updated documentation...`
}]
})
);API-Schlüssel werden in Umgebungsvariablen gespeichert
Alle Anfragen werden authentifiziert
Parametervalidierung ist implementiert
Fehlermeldungen werden bereinigt
Forken Sie das Repository
Erstellen Sie Ihren Feature-Zweig ( git checkout -b feature/amazing-feature )
Übernehmen Sie Ihre Änderungen ( git commit -m 'Add some amazing feature' )
Pushen zum Zweig ( git push origin feature/amazing-feature )
Öffnen einer Pull-Anfrage
Dieses Projekt ist unter der MIT-Lizenz lizenziert – Einzelheiten finden Sie in der Datei LICENSE .
| 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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