m8ty_client_mcprag

MCP RAG client for semantic tool search, embeddings, and tool catalog access. Enables AI-powered discovery of API tools via vector similarity search.

Before you start

Check the documentation of the m8ty_client_mcprag library.

Import all needed libraries

import 'package:dio/dio.dart';
import 'package:m8ty_client_mcprag/m8ty_client_mcprag.dart';

Client Setup

final client = M8tyClientMcprag();
client.setOAuthToken('ApiOAuth2', accessToken);

final ragApi = client.getMcpRagApi();

Use basePathOverride for tests, staging, or tenant-specific routing:

final client = M8tyClientMcprag(
  basePathOverride: 'https://staging.example.com/api/v1',
);

API Surface

McpRagApi

Method	Description
`createMcpRagEmbedding`	Create text embeddings for semantic search
`getMcpRagCatalog`	Get the full MCP tool catalog
`getMcpRagVectorizedCatalog`	Get tool catalog with pre-computed vectors
`searchMcpRagTools`	Search tools by natural language or vector

How to perform different tasks

Here some examples how the dart client can be used

Create an embedding

final request = McpEmbedRequestModel((b) => b
  ..text = 'Find tools that can summarize PDF documents'
  ..inputType = 'query');

try {
  final response = await ragApi.createMcpRagEmbedding(
    mcpEmbedRequestModel: request,
  );

  final embedding = response.data;
  if (embedding == null) {
    throw StateError('Embedding response was empty.');
  }
  final vector = embedding.vector;
} on DioException catch (e) {
  throw StateError('Embedding request failed: ${e.response?.statusCode}');
}

Search tools by natural language

final request = McpToolSearchRequestModel((b) => b
  ..text = 'Find a tool that can retrieve a portfolio position'
  ..topK = 10
  ..minScore = 0.25);

final response = await ragApi.searchMcpRagTools(
  mcpToolSearchRequestModel: request,
);

final matches = response.data?.matches;
if (matches == null || matches.isEmpty) {
  return;
}

Search tools by precomputed vector

final request = McpToolSearchRequestModel((b) => b
  ..vector.addAll(queryVector)
  ..topK = 5);

final response = await ragApi.searchMcpRagTools(
  mcpToolSearchRequestModel: request,
);

Restrict search to specific tool names

final request = McpToolSearchRequestModel((b) => b
  ..text = 'Create an order'
  ..toolNames.addAll(['orders_create_order'])
  ..topK = 3);

final response = await ragApi.searchMcpRagTools(
  mcpToolSearchRequestModel: request,
);

Get tool catalog

final response = await ragApi.getMcpRagCatalog();
final catalog = response.data;

if (catalog == null) {
  return;
}

for (final tool in catalog.tools) {
  print('${tool.toolName}: ${tool.summary}');
}

Get vectorized catalog for synchronization

final response = await ragApi.getMcpRagVectorizedCatalog();
final catalog = response.data;

for (final item in catalog!.tools) {
  print('Tool: ${item.tool.toolName}, Vector length: ${item.vector.length}');
}

03 June 2026