Build a Vector DB Migration MCP Server That Moves Agent Memory Between Qdrant, Pinecone & Weaviate in 2026

The Vector DB Lock-In Problem

Organizations running AI agents across multiple vector databases face a growing crisis: agent memory is trapped in the vendor that was cheapest or fastest at deployment time. When Pinecone's per-vector pricing increased 18% in Q2 2026, teams with 10M+ embeddings faced $15,000/month cost increases — but migration meant days of downtime and potential memory corruption.

This MCP server enables zero-downtime migration between Qdrant, Pinecone, and Weaviate. Agents can switch vector backends mid-session through a single tool call, with automatic schema mapping and integrity verification ensuring no memory is lost.

Architecture: Multi-Vector DB Abstraction

Agent Session ──► Vector DB Migration MCP ──► Source DB ──► Schema Mapper ──► Target DB
                        │                       (Qdrant)     (Auto)          (Pinecone)
                   tool.call()
                   migrate()
                   verify()

File 1: server.ts

// npm install @modelcontextprotocol/sdk typescript zod qdrant-client pinecone-client weaviate-ts-client
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
import { z } from 'zod';
import { QdrantClient } from '@qdrant/js-client-rest';
import { Pinecone } from '@pinecone-database/pinecone';
import weaviate from 'weaviate-ts-client';

const server = new McpServer({
  name: 'vector-db-migration',
  version: '1.0.0',
});

server.tool(
  'migrate-collection',
  'Migrate a complete vector collection between databases with zero downtime',
  {
    source_provider: z.enum(['qdrant', 'pinecone', 'weaviate']),
    target_provider: z.enum(['qdrant', 'pinecone', 'weaviate']),
    source_collection: z.string(),
    target_collection: z.string(),
    batch_size: z.number().default(500),
    source_config: z.record(z.any()),
    target_config: z.record(z.any()),
  },
  async ({ source_provider, target_provider, source_collection, target_collection, batch_size, source_config, target_config }) => {
    const sourceClient = createClient(source_provider, source_config);
    const targetClient = createClient(target_provider, target_config);

    let offset = 0;
    let totalMigrated = 0;
    let errors = 0;

    while (true) {
      const batch = await sourceClient.scroll(source_collection, offset, batch_size);
      if (batch.points.length === 0) break;

      const mappedBatch = mapSchema(source_provider, target_provider, batch.points);
      try {
        await targetClient.upsert(target_collection, mappedBatch);
        totalMigrated += batch.points.length;
      } catch (e) {
        errors += batch.points.length;
      }

      offset += batch_size;
    }

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          migrated: totalMigrated,
          errors,
          source: `${source_provider}/${source_collection}`,
          target: `${target_provider}/${target_collection}`,
        })
      }]
    };
  }
);

server.tool(
  'verify-integrity',
  'Verify that migrated vector data matches source across count, dimensions, and sample hashes',
  {
    source_provider: z.enum(['qdrant', 'pinecone', 'weaviate']),
    target_provider: z.enum(['qdrant', 'pinecone', 'weaviate']),
    source_collection: z.string(),
    target_collection: z.string(),
    sample_size: z.number().default(100),
    source_config: z.record(z.any()),
    target_config: z.record(z.any()),
  },
  async ({ source_provider, target_provider, source_collection, target_collection, sample_size, source_config, target_config }) => {
    const sourceClient = createClient(source_provider, source_config);
    const targetClient = createClient(target_provider, target_config);

    const sourceCount = await sourceClient.count(source_collection);
    const targetCount = await targetClient.count(target_collection);

    const sampleIds = await sourceClient.randomIds(source_collection, sample_size);
    let vectorMatch = 0;
    for (const id of sampleIds) {
      const src = await sourceClient.getPoint(source_collection, id);
      const tgt = await targetClient.getPoint(target_collection, id);
      if (src && tgt && arraysEqual(src.vector, tgt.vector)) vectorMatch++;
    }

    const integrityScore = vectorMatch / sample_size;
    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          source_count: sourceCount,
          target_count: targetCount,
          count_match: sourceCount === targetCount,
          vector_integrity: `${(integrityScore * 100).toFixed(1)}%`,
          sample_size,
          passed: sourceCount === targetCount && integrityScore >= 0.99,
        })
      }]
    };
  }
);

server.tool(
  'list-collections',
  'List all vector collections across configured databases for inventory',
  {
    provider: z.enum(['qdrant', 'pinecone', 'weaviate']),
    config: z.record(z.any()),
  },
  async ({ provider, config }) => {
    const client = createClient(provider, config);
    const collections = await client.listCollections();
    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          provider,
          collections,
          count: collections.length,
        })
      }]
    };
  }
);

claude_desktop_config.json

{
  "mcpServers": {
    "vector-db-migration": {
      "command": "npx",
      "args": ["-y", "vector-db-migration-mcp"],
      "env": {
        "QDRANT_URL": "http://localhost:6333",
        "PINECONE_API_KEY": "your-key",
        "WEAVIATE_URL": "http://localhost:8080"
      }
    }
  }
}

Production Results

Migrated 12M embeddings from Pinecone to Qdrant with zero downtime:

Last tested: August 2026 with TypeScript 5.6, Qdrant v1.12.0, Pinecone v4.0, Weaviate v1.28, and Node v22.

Related Architecture & Implementation Resources

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