MCP Server Architecture Patterns for LLM-Integrated Applications

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Carson Rodrigues, Oysturn Vas
• Venue: arXiv: cs.SE
• Published: 2026-06-29
• Collected: 2026-06-30
• URL: http://arxiv.org/abs/2606.30317v1
• PDF: https://arxiv.org/pdf/2606.30317v1
• DOI: UNKNOWN

Plain-Language Brief

This industry experience paper presents five architectural patterns observed in the use of Model Context Protocol (MCP) servers, which connect large language models to external tools, data sources, and services. The paper also documents four anti-patterns and cross-cutting concerns related to authentication, versioning, and observability.

What the Document Says

• The Model Context Protocol (MCP) was introduced by Anthropic in November 2024 as a standardized interface for connecting large language models (LLMs) to external tools, data sources, and services.
• By June 2026, hundreds of MCP servers were built and published on GitHub. However, no software-maintenance literature has described the production structure of this ecosystem.
• The paper presents five recurring MCP server architectural patterns: Resource Gateway, Tool Orchestrator, Stateful Session Server, Proxy Aggregator, and Domain-Specific Adapter.
• Each pattern is described using a structured format (context, problem, solution, and consequences) similar to that of Gamma et al.
• The paper also documents four anti-patterns and cross-cutting concerns related to authentication, versioning, and observability.
• A quantitative evaluation was performed with three measurements: inter-rater reliability, transport overhead, and a tool-count study.
• The code, corpus, and prompts are released as a replication package.

Key Concepts Mentioned

• Model Context Protocol (MCP)
• Large Language Models (LLMs)
• Architectural patterns
• Anti-patterns
• Quantitative evaluation

Claims Made by the Document

• Hundreds of MCP servers were built and published on GitHub by June 2026.
• Five recurring MCP server architectural patterns have been observed in independently developed servers.
• A set of cross-cutting concerns around authentication, versioning, and observability have been documented.
• Inter-rater reliability of the taxonomy was measured at Cohen's kappa = 0.76.

Evidence Used

• hundreds of community-built MCP servers appeared on GitHub
• enumerated corpus of fifteen independently developed servers
• documented four anti-patterns and a set of cross-cutting concerns

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

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.