{"$schema":"https://wellknown.network/schemas/agent-record-v1.json","schemaVersion":"1","id":"ag_hdgtj7rf5bp2","handle":"iflow-mcp-cr21-rag-mcp","url":"https://wellknown.network/agents/iflow-mcp-cr21-rag-mcp","links":{"self":"https://wellknown.network/agents/iflow-mcp-cr21-rag-mcp/record.json","html":"https://wellknown.network/agents/iflow-mcp-cr21-rag-mcp","markdown":"https://wellknown.network/agents/iflow-mcp-cr21-rag-mcp/record.md","api":"https://wellknown.network/api/v1/agents/iflow-mcp-cr21-rag-mcp","status":"https://wellknown.network/api/v1/agents/iflow-mcp-cr21-rag-mcp/status","claim":"https://wellknown.network/agents/iflow-mcp-cr21-rag-mcp/claim","claimApi":"https://wellknown.network/api/v1/claims","claimDescriptor":"https://wellknown.network/agents/iflow-mcp-cr21-rag-mcp/claim.json","badge":"https://wellknown.network/agents/iflow-mcp-cr21-rag-mcp/badge.svg","openapi":"https://wellknown.network/openapi.json","history":"https://wellknown.network/api/v1/agents/iflow-mcp-cr21-rag-mcp/history","tools":"https://wellknown.network/api/v1/agents/iflow-mcp-cr21-rag-mcp/tools"},"ard":{"identifier":"urn:air::server:iflow-mcp-cr21-rag-mcp","type":"application/mcp-server-card+json"},"kind":"mcp_server","declared":{"name":"iflow-mcp_cr21-rag-mcp","summary":"MCP RAG Agent","description":"# Rise of Agentic AI and Birth of Model Context Protocol (MCP)\n\n## The Evolution of AI: From Text Generation to Agentic Action\n\n### 1. Early Limitations of LLMs\nInitial Large Language Models (LLMs) were groundbreaking in their ability to generate human-like text. However, they lacked precise instruction-following capabilities, often providing irrelevant or generalized responses. For example:\n\n- **User:** \"Who is the president of the USA?\"  \n  **LLM:** \"The most influential person in the world.\"\n\n### 2. Enhanced Instruction Following with SFT and RLHF\nTo overcome these limitations, two critical methodologies emerged:\n\n- **Supervised Fine-Tuning (SFT)**: Models were explicitly trained on human-generated responses aligned with specific instructions.\n- **Reinforcement Learning from Human Feedback (RLHF)**: Models were further refined by human-ranked preferences, significantly enhancing relevance and coherence in generated responses.\n\n### 3. Integrating Function Calling\nAs users began to require precise, factual, and real-time information, LLMs integrated external tool invocation capabilities (function calling). For instance:\n\n- **User:** \"What is the square root of 14.5232?\"  \n  **LLM (Function Calling):** \"The square root of 14.5232 is approximately 3.81.\"\n\n### 4. Emergence of Agentic AI\nDemand for practical, real-world interactions led to the rise of Agentic AI—models capable of autonomously interacting with external applications and services to perform actions such as:\n\n- Scheduling calendar events\n- Sending messages\n- Conducting online transactions\n\nHowever, integrating with multiple diverse applications faced significant challenges, including:\n\n- Diverse technology stacks\n- Inconsistent API structures\n- Complex authentication requirements\n- Scalability issues with manual integrations\n\n### 5. 📜 Birth of Model Context Protocol (MCP)\n\nAs AI assistants became more capable, they remained trapped by **information silos** — isolated from real-world data in business tool…","publisher":null,"homepage":null,"repository":null,"version":"0.1.1","license":null,"protocols":["mcp"],"tags":["mcp"],"pricing":null,"endpoints":[{"url":"pypi:iflow-mcp_cr21-rag-mcp","type":"package_pypi","auth":null,"probeable":false}],"skills":null,"tools":null,"extra":null,"attribution":{"kind":"pypi","name":"pypi","summary":"pypi","version":"pypi","description":"pypi"}},"derived":{"capabilities":[{"slug":"data.vector-search","name":"Vector Search","confidence":1,"provenance":"derived"},{"slug":"productivity.calendar","name":"Calendar & Scheduling","confidence":0.848,"provenance":"derived"}],"categories":["data","productivity"],"language":"en"},"observed":{"status":"unknown","statusReason":"Distributed as a package to run locally; no network endpoint to check.","lastOkAt":null,"lastProbedAt":null,"statusComputedAt":null,"reliability30d":null,"latestObservations":[],"tools":null,"package":{"name":"iflow-mcp_cr21-rag-mcp","registry":"pypi","observedAt":"2026-11-09T08:24:23.437Z","publishedAt":"2026-02-12T00:27:41.937126Z","latestVersion":"0.1.1"},"toolSurface":null,"endpointFacts":[]},"verification":{"claimed":false,"claimedAt":null,"proofs":[]},"provenance":{"sources":[{"source":"pypi","key":"iflow-mcp_cr21-rag-mcp","url":"https://pypi.org/project/iflow-mcp_cr21-rag-mcp/","firstSeenAt":"2026-09-09T18:21:28.725Z","fetchedAt":"2026-11-09T08:22:27.048Z","normalizedAt":"2026-11-09T08:22:27.048Z"}]},"firstSeenAt":"2026-09-09T18:21:28.725Z","updatedAt":"2026-11-09T08:24:23.437Z"}