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嘿，HN，我一直在实验如何让大型语言模型（LLMs）动态使用大量工具，而无需将所有工具预先加载到上下文中——因此我构建了 one-mcp 来探索这个想法。 one-mcp 是一个开源的 MCP 服务器，使用 FastAPI 构建，充当语义索引和 API 工具的动态加载器。与其在 LLM 会话中硬编码或嵌入每个工具规格（这会迅速增加上下文大小和成本），one-mcp 允许模型按需搜索、加载和执行工具——使用嵌入和自然语言查询。它的功能包括： - 工具的语义搜索：使用自然语言查询您的 API 目录（例如“如何获取用户信息”），并返回相关的工具规格。 - 上传/管理/删除工具：通过简单的 REST API 或直接从 MCP 本身进行 JSON 或文件格式的工具导入。 - MCP 集成：作为一个合规的 MCP 服务器工作——可以将其插入到支持 MCP 的客户端或框架中。示例： --- 上传工具 --- ```bash curl -X POST http://localhost:8003/api/tools/upload-json \ -H "Content-Type: application/json" \ -d '{"tools": [{"name": "getUserProfile", "description": "Retrieves user profile"}]}' ``` --- 语义搜索 --- ```bash curl -X POST http://localhost:8003/api/tools/search \ -H "Content-Type: application/json" \ -d '{"query": "how to get user info"}' ``` 您也可以直接使用 MCP 进行这两项操作 :) 为什么这很重要：目前大多数 LLM 设置（即使是那些具有 MCP 或工具 API 的设置）都在前期加载所有工具——这会膨胀提示/上下文大小，浪费令牌预算，并增加推理成本。通过 one-mcp，工具存储在外部向量存储中——LLMs 只“看到”与当前用户查询最相关的少数工具。这种架构可能使动态工具生态系统更具可扩展性。我期待您的反馈。 [https://github.com/freakynit/one-mcp](https://github.com/freakynit/one-mcp)

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Hey HN,I’ve been experimenting with how LLMs can dynamically use large sets of tools without needing to preload them all into context - and built one-mcp to explore that idea.one-mcp is an open source MCP server built with FastAPI that acts as a semantic index + dynamic loader for API tools.Instead of hardcoding or embedding every tool spec in an LLM session (which quickly blows up context size and costs), one-mcp lets the model search, load, and execute tools on demand — using embeddings and natural language queries.What it does:- Semantic Search for Tools: Query your API catalog with natural language ("how to get user info") and get relevant tool specs back.- Upload/Manage/Delete Tools: JSON or file-based tool ingestion with a simple REST API or directly from MCP itself.- MCP Integration: Works as a compliant MCP server — plug it into MCP-enabled clients or frameworks.Example:--- Upload tools ---curl -X POST [http://localhost:8003/api/tools/upload-json](http://localhost:8003/api/tools/upload-json) -H "Content-Type: application/json" -d '{"tools": [{"name": "getUserProfile", "description": "Retrieves user profile"}]}'--- Semantic search ---curl -X POST [http://localhost:8003/api/tools/search](http://localhost:8003/api/tools/search) -H "Content-Type: application/json" -d '{"query": "how to get user info"}'You can MCP itself as well for both these operations :)Why this matters:Most current LLM setups (even those with MCP or tool APIs) load all tools upfront - which inflates prompt/context sizes, wastes token budget, and increases inference costs.With one-mcp, tools live in an external vector store - LLMs only "see" the few tools most relevant to the current user query. This architecture could make dynamic tool ecosystems more scalable.I’d love your feedback..<a href="https://github.com/freakynit/one-mcp" rel="nofollow">https://github.com/freakynit/one-mcp</a>

展示HN：One-MCP：无上下文膨胀的无限工具MCP服务器