MRC Data -- Integration Guide

How to connect MRC Data to agent orchestration frameworks and memory systems.

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Agent Frameworks

ByteDance Deer Flow

Deer Flow natively supports remote HTTP MCP servers.

Add to extensions_config.json:

{
  "mcpServers": {
    "meacheal": {
      "enabled": true,
      "type": "http",
      "url": "https://api.meacheal.ai/mcp",
      "description": "MRC Data - Chinese apparel supply chain intelligence"
    }
  }
}

Docs: backend/docs/MCP_SERVER.md in the deer-flow repo.

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Microsoft Agent Framework

Supports both declarative YAML and programmatic approaches.

YAML (Python declarative):

tools:
  - kind: mcp
    name: meacheal-mcp
    description: MRC Data - Chinese apparel supply chain
    url: https://api.meacheal.ai/mcp
    approvalMode: never

C# code:

await using var mcpClient = await McpClient.CreateAsync(
    new HttpClientTransport(new() {
        Endpoint = new Uri("https://api.meacheal.ai/mcp"),
        Name = "MEACHEAL MCP"
    }));
var tools = await mcpClient.ListToolsAsync();

Python code:

from agent_framework import McpClient
client = McpClient(url="https://api.meacheal.ai/mcp")
tools = await client.list_tools()

Docs: python/samples/02-agents/mcp/ in the agent-framework repo.

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Nous Research Hermes Agent

Hermes Agent natively supports remote HTTP MCP servers with automatic tool discovery.

Config file (~/.hermes/config.yaml):

mcp_servers:
  mrc-data:
    url: "https://api.meacheal.ai/mcp"
    headers:
      Authorization: "Bearer YOUR_API_KEY"

Or via CLI:

hermes mcp add mrc-data --url "https://api.meacheal.ai/mcp"
# Then set your API key in ~/.hermes/.env:
# MCP_MRC_DATA_API_KEY=mrc_your_key_here

Optional — filter tools for token efficiency:

mcp_servers:
  mrc-data:
    url: "https://api.meacheal.ai/mcp"
    headers:
      Authorization: "Bearer YOUR_API_KEY"
    tools:
      include: [search_suppliers, search_fabrics, get_stats]
      prompts: false

All 19 MRC Data tools are auto-discovered and registered as mcp_mrc_data_<tool_name>. Verify with hermes mcp test mrc-data.

Docs: MCP configuration in the Hermes Agent repo.

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Agent Memory Systems

supermemory (Recommended)

Cloud-hosted memory service. Works as a sibling MCP server -- the agent stores and recalls context across sessions automatically.

Setup: Register both MRC Data and supermemory as MCP servers:

{
  "mcpServers": {
    "mrc-data": {
      "url": "https://api.meacheal.ai/mcp",
      "headers": { "Authorization": "Bearer YOUR_MRC_KEY" }
    },
    "supermemory": {
      "url": "https://mcp.supermemory.ai/mcp",
      "headers": { "Authorization": "Bearer YOUR_SM_KEY" }
    }
  }
}

How it works:

1. Agent calls search_suppliers(province='guangdong', product_type='sportswear') from MRC Data
2. Agent stores relevant findings via supermemory's memory tool
3. Next session, agent calls recall to retrieve past sourcing preferences
4. Result: "This user sources cotton knits from Shaoxing cluster" persists across conversations

No custom code needed. The agent decides what to remember.

Get a key at supermemory.com.

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OpenViking (Advanced)

Self-hosted context database from ByteDance. Filesystem paradigm for organizing agent context. More powerful for complex multi-session workflows, but heavier setup.

Architecture:

• L0/L1/L2 tiered context loading (saves tokens)
• Directory-based organization (suppliers by cluster, fabrics by category)
• Automatic session memory extraction
• Requires VLM + embedding model

Setup:

pip install openviking --upgrade

Configure ~/.openviking/ov.conf with your model provider (supports OpenAI, Anthropic via LiteLLM, Volcengine).

Integration pattern: Export supplier data as structured documents into OpenViking's filesystem, then agents query OpenViking for context before calling MRC Data MCP tools.

Docs: OpenViking repo.

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Quick Comparison

Feature	supermemory	OpenViking
Hosting	Cloud (managed)	Self-hosted
Setup effort	5 minutes	1-2 hours
MCP native	Yes (sibling server)	No (separate system)
Structured data	Text/embeddings only	Filesystem paradigm
Best for	Simple memory across sessions	Complex multi-agent workflows
Cost	SaaS pricing	Free (self-host) + model costs