MCP in the Wild: Cross-Domain Knowledge Discovery through Multi-Server Orchestration

The first empirical study of multi-server Model Context Protocol (MCP) orchestration with a 7-model cross-domain synthesis benchmark.

What is MCP?

Model Context Protocol (MCP) is an open standard by Anthropic (released late 2024) that lets LLM agents discover and invoke external tools via JSON-RPC. Think of it as a USB-C for AI: any MCP server exposes tools that any LLM agent can use, regardless of language or platform. As of March 2026, there are 106 official server integrations (GitHub, Postgres, Slack, Docker, etc.) and 50+ academic papers studying the protocol.

This paper asks: what happens when you connect multiple MCP servers simultaneously for cross-domain tasks?

Key Findings

• 17 real MCP tool calls across 6 servers, 0% failure rate, 9 cross-domain insights
• 7 LLMs benchmarked (GPT-5.4, DeepSeek R1, Mistral Large 3, Llama 4 Maverick, Gemini 2.5 Flash, Claude Sonnet 4.5, Claude Haiku 4.5) on identical MCP-collected data
• All 7 models independently identified the same gap: composition patterns for multi-server MCP workflows are undocumented
• 5 composition patterns proposed: Sequential Pipeline, Parallel Fan-Out, Cross-Reference Verification, Iterative Refinement, Domain Bridging

Benchmark Results

Model	Latency	Tokens	KG Entities	KG Relations
GPT-5.4	54.7s	2,352	14	15
DeepSeek R1	33.9s	4,296	6	4
Mistral Large 3	6.5s	1,857	9	8
Llama 4 Maverick	3.0s	1,374	3	3
Gemini 2.5 Flash	15.6s	4,592	12	11
Claude Sonnet 4.5	21.3s	2,411	13	11
Claude Haiku 4.5	9.3s	2,136	8	6

Composition Patterns

1. Sequential Pipeline -- Server A output feeds Server B query
2. Parallel Fan-Out -- Same query to multiple servers at once
3. Cross-Reference Verification -- Validate findings across servers
4. Iterative Refinement -- Cross-server context guides query refinement
5. Domain Bridging -- Synthesize insights from unrelated domains

MCP Servers Used

All open-source, no API keys required:

uvx arxiv-mcp-server                            # Academic preprints
npx @cyanheads/pubmed-mcp-server                # Biomedical literature
npx @modelcontextprotocol/server-memory          # Knowledge graph
npx firecrawl-mcp                                # Web search & scrape
npx @upstash/context7-mcp                        # Library documentation
npx @modelcontextprotocol/server-filesystem      # File operations

Reproduce

Run the 7-model benchmark

# Set your API keys
export AZURE_API_KEY="your-azure-key"
export GEMINI_API_KEY="your-gemini-key"
# AWS Bedrock must be configured for Claude models

python benchmark.py

Paper

The paper is available as paper.pdf in this repo.

• Zenodo: 10.5281/zenodo.18917784
• SSRN: Abstract ID 6374760

Citation

@article{dogan2026mcp,
  title={MCP in the Wild: Cross-Domain Knowledge Discovery through Multi-Server Orchestration},
  author={Dogan, Arif},
  journal={Zenodo},
  doi={10.5281/zenodo.18917784},
  year={2026}
}

License

MIT