Trying to figure out MCP by actually building an app from scratch with open source and SLMs
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Trying to figure out MCP by actually building an app from scratch with open source and SLMs
- 1. Trying to figure out MCP by actually building an app from scratch with open source and SLMs Julien Simon, Chief Evangelist julien@arcee.ai https://www.julien.org https://github.com/juliensimon/smolagents-mcp-demo
- 2. Just another remote procedure call protocol 👴 1982: UNIX RPC – First mainstream RPC, used in NFS. 1994: CORBA – Early cross-language object middleware. 2000: REST – Dominant HTTP API style. 2000: SOAP – Enterprise XML-based APIs. 2005: gRPC – Protobuf + HTTP/2, high-performance. 2007: Thri f – Mul ti -language RPC. 2010: WebSockets – Real- ti me, bidirec ti onal communica ti on. 2014: GraphQL – Client-driven data queries. 2023: MCP – Standardizes AI data integra ti on ⬅ YOU ARE HERE
- 3. Food for thought Security, Trust, Audi ti ng - CRITICAL for enterprise adop ti on • What measures are in place to authen ti cate and validate the servers your applica ti on communicates with? • How do you ensure each func ti on is accessible only to authorized users under appropriate condi ti ons? • How do you establish clear iden ti ty management to a tt ribute ac ti ons accurately within MCP systems? Discoverability & Rou ti ng • How does your applica ti on discover and connect to remote servers dynamically? • How do you determine the most suitable server and func ti on(s) for each task within your applica ti on? Versioning & Compa ti bility • How can you ensure that updates do not disrupt exis ti ng func ti onali ti es for users? • How does MCP live alongside other protocols (REST, OpenAI-style func ti on calling, etc.)? Documenta ti on & Usability • How do you ensure that func ti on descrip ti ons are detailed and understandable for models? Performance & Cost Management • How do you op ti mize latency and token consump ti on in agen ti c systems?
- 4. Arcee AI - Post-trained models State-of-the-art tech stack based on open-source libraries Spectrum (continuous pre-training), MergeKit (merging), DistilKit (distillation), EvolKit (dataset improvement) Best-in-class models based on open-source architectures Hugging Face OpenLLM Leaderboard benchmarks Llama 3.1 70B 🥇 Best 70B model Qwen2 1.5B 🥇 Best 1.5B model Llama 3.1 8B 🥇 Best 8B model Qwen2.5 14B 🥇 Best 14B model Qwen2 72B 🥇 Best Arabic model
- 7. AFM-4.5B-Preview vs. Qwen-3-4B 8/10. Tie on Industrials, loss on Communication Services. 200 questions generated by Claude Sonnet 3.7 20 questions for each one of the top 10 industries in the S&P 500 Judge: DeepSeek-R1 (670B) https://github.com/juliensimon/radar-evaluator
- 8. AFM-4.5B-Preview vs. Google Gemma-3n-E4B-it 8/10, tie on Healthcare, loss on IT 200 questions generated by Claude Sonnet 3.7 20 questions for each one of the top 10 industries in the S&P 500 Judge: DeepSeek-R1 (670B) https://github.com/juliensimon/radar-evaluator
- 9. AFM-4.5B-Preview vs. Llama-3.2-8B 10/10 😃 200 questions generated by Claude Sonnet 3.7 20 questions for each one of the top 10 industries in the S&P 500 Judge: DeepSeek-R1 (670B) https://github.com/juliensimon/radar-evaluator
- 10. AFM-4.5B-Preview vs. Mixtral-8x7B-Instruct Almost tied (4/10) with 8% of Mixtral’s size 200 questions generated by Claude Sonnet 3.7 20 questions for each one of the top 10 industries in the S&P 500 Judge: DeepSeek-R1 (670B) https://github.com/juliensimon/radar-evaluator
- 11. Julien Simon, Chief Evangelist julien@arcee.ai https://www.julien.org https://github.com/juliensimon/smolagents-mcp-demo Models on Hugging Face; OpenRouter and Together AI Chat with AFM AFM blog post