Hugging Face Trending Papers
MCPGen: Benchmarking LLMs on Executable MCPWorkflow Development
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arXiv AI
Sep 23
MCPGen: Benchmarking LLMs on Executable MCPWorkflow Development
arXiv:2609.23925v1 Announce Type: cross Abstract: We study whether LLMs can produce executable workflow artifacts that remain consistent across graph structure, tool implementation, schema bindings,...
arXiv AI
Jul 17
StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
arXiv:2607. 14896v1 Announce Type: cross Abstract: Addressing a structural-engineering request requires more than a single answer; it requires a chain of interdependent artifacts: interpreted requirements, a computable model, validation records, solver outputs, code-check records, and a final report.
arXiv Machine Learning
Aug 12
FlowScout: From Execution Feedback to Reliable Tool-Using Agent Workflows
arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.
arXiv AI
Aug 26
Hybrid Semantic Tool Discovery for Enterprise MCP Gateway: Architecture and Implementation
Hybrid Semantic Tool Discovery for Enterprise MCP Gateway presents SCOUT, a system that addresses two major challenges in large language model (LLM) agent tool usage: a context‑engineering bottleneck and a tool discoverability barrier. SCOUT reframes tool exposure as a context‑selection problem, injecting only relevant tools into the model’s context window and providing two MCP meta‑tools—tool_search and execute_tool—to perform hybrid retrieval via BM25 and dense vector search. In production at PayPal, SCOUT cuts MCP tool‑token consumption by 99%, dramatically reducing per‑query inference cost while remaining model‑agnostic and requiring no client‑side changes.
Hugging Face Trending Papers
Jun 29
MCP Server Architecture Patterns for LLM-Integrated Applications
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.
arXiv Machine Learning
Sep 22
MCP-GRANITE Benchmark: GRANularity Interface TEsting for MCP-Based LLM Agents
arXiv:2609.24161v1 Announce Type: cross Abstract: As LLM agents increasingly interact with external tools through standardized protocols such as MCP, tool-interface design becomes a critical yet unde...