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Source note2 min read

MCP Server Architecture Patterns for LLM-Integrated Applications

Source

arXiv

arxiv.org/abs/2606.30317v1

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Plain-Language Brief

This paper presents five common architectural patterns of Model Context Protocol (MCP) servers used to integrate large language models with external tools and services, based on an analysis of fifteen independently developed servers. The authors also discuss four anti-patterns and various cross-cutting concerns such as authentication, versioning, and observability.

What the Document Says

  • The paper is about MCP server architectures for integrating large language models with external tools and services.
  • The authors identify five recurring patterns: Resource Gateway, Tool Orchestrator, Stateful Session Server, Proxy Aggregator, and Domain-Specific Adapter.
  • They also discuss four anti-patterns and cross-cutting concerns like authentication, versioning, and observability.
  • The analysis is based on fifteen independently developed servers.
  • Quantitative evaluation includes inter-rater reliability, transport overhead measurements, and a tool-count study.

Key Concepts Mentioned

  • Model Context Protocol (MCP)
  • Large Language Models (LLMs)
  • MCP server architectures
  • anti-patterns
  • quantitative evaluation

Claims Made by the Document

  • The authors have identified five recurring MCP server architectural patterns.
  • They have discussed four anti-patterns.
  • They have conducted a quantitative evaluation of their taxonomy.

Evidence Used

  • The analysis is based on fifteen independently developed servers.
  • The quantitative evaluation results are provided, including inter-rater reliability, transport overhead measurements, and a tool-count study.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

The Model Context Protocol (MCP), introduced by Anthropic in November 2024, defines a standardized interface for connecting large language models (LLMs) to external tools, data sources, and services. Within months of release, hundreds of community-built MCP servers appeared on GitHub, but no software-maintenance literature has yet described how the ecosystem is being structured in production. This industry experience paper catalogues five recurring MCP server architectural patterns observed across an enumerated corpus of fifteen independently developed servers (five production servers from the ANSYR voice AI platform plus ten public servers from the official MCP registry): Resource Gateway, Tool Orchestrator, Stateful Session Server, Proxy Aggregator, and Domain-Specific Adapter. Each pattern is described in the structured form of Gamma et al.: context, problem, solution, and consequences. We also document four anti-patterns and a set of cross-cutting concerns around authentication, versioning, and observability. The quantitative evaluation contributes three measurements: inter-rater reliability of the taxonomy across two independent LLM raters on 54 held-out servers (Cohen's kappa = 0.76), which also localizes three pattern-boundary ambiguities; transport overhead measured end-to-end on loopback and modeled for cross-host paths; and a tool-count study showing tool-selection accuracy drops below 90% between 10 and 15 tools per context for Claude Haiku 4.5 and between 20 and 30 tools for Sonnet 4. Code, corpus, and prompts are released as a replication package.