fix: prevent MCP tool name collision by prefixing with server name

[2722550596]

问题

当添加多个 MCP 服务器时，如果不同服务器暴露了同名工具（如 read_memory），前端选中一个会意外地把另一个也选中。原因是后端注册和前端 API 都只用了原生的工具名，没有加服务器标识。

修改

astrbot/core/agent/mcp_client.py — MCPTool 注册名加上服务器前缀：

name=f"{mcp_server_name}:{mcp_tool.name}"

astrbot/dashboard/services/tools_service.py — MCP 服务器列表 API 也返回前缀名：

server_info["tools"] = [f"{name}:{tool.name}" for tool in mcp_client.tools]

实际调用 MCP server 时仍使用原始工具名（self.mcp_tool.name），不影响通信。

Summary by Sourcery

Bug Fixes:

• Ensure tools from different MCP servers with the same native name are treated as distinct tools in both backend registration and the dashboard API.

[sourcery-ai]

Hey - I've left some high level feedback:

• Consider centralizing the logic for constructing the prefixed tool name (e.g., a helper like get_prefixed_tool_name(server_name, tool_name)) so the prefix format stays consistent if you need to modify it later.
• Using : as a separator between server and tool names may be brittle if either component can contain :; you might want to either validate/disallow : in names or choose a safer delimiter/encoding scheme.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Consider centralizing the logic for constructing the prefixed tool name (e.g., a helper like `get_prefixed_tool_name(server_name, tool_name)`) so the prefix format stays consistent if you need to modify it later.
- Using `:` as a separator between server and tool names may be brittle if either component can contain `:`; you might want to either validate/disallow `:` in names or choose a safer delimiter/encoding scheme.

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[gemini-code-assist]

Code Review

This pull request prefixes MCP tool names with their server name using a colon separator in both the client registration and the dashboard tools service. The reviewer identified a critical issue where using a colon in tool names violates the strict naming constraints of major LLM APIs, which can lead to API errors. They suggested refactoring this logic to use a shared helper function that cleans and formats the tool names safely to ensure compatibility and avoid code duplication.

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[gemini-code-assist]

问题分析

在大多数主流的大语言模型（LLM）API（如 OpenAI、Anthropic、Gemini、Groq 等）中，工具/函数名称（name）有非常严格的命名规范限制：

1. 字符限制：通常只允许字母、数字、下划线和连字符，即满足正则表达式 ^[a-zA-Z0-9_-]+$。冒号 : 是不被允许的非法字符。
2. 长度限制：通常最大长度为 64 个字符。

如果直接使用 f"{mcp_server_name}:{mcp_tool.name}" 作为工具名称，包含冒号 : 会直接导致 LLM API 返回 400 Bad Request 错误。

解决方案

建议使用双下划线 __ 作为命名空间分隔符，并对服务器名称和工具名称进行清洗（将所有非 [a-zA-Z0-9_-] 的字符替换为下划线 _），同时限制总长度不超过 64 字符。

为了避免在 mcp_client.py 和 tools_service.py 中重复编写相同的清洗逻辑，应当将此清洗逻辑重构为一个公共的辅助函数（例如 clean_tool_name），以避免代码重复。

Suggested change

	name=f"{mcp_server_name}:{mcp_tool.name}",
	name=clean_tool_name(mcp_server_name, mcp_tool.name),

References

1. When implementing similar functionality for different cases, refactor the logic into a shared helper function to avoid code duplication.

[gemini-code-assist]

为了与 astrbot/core/agent/mcp_client.py 中的工具注册名称保持一致，并避免冒号 : 导致 LLM API 报错，这里也需要采用相同的清洗逻辑。为了避免代码重复，建议调用公共的辅助函数 clean_tool_name，而不是在此处重复编写相同的正则表达式清洗逻辑。

Suggested change

	server_info["tools"] = [f"{name}:{tool.name}" for tool in mcp_client.tools]
	server_info["tools"] = [
	clean_tool_name(name, tool.name)
	for tool in mcp_client.tools
	]

References

1. When implementing similar functionality for different cases, refactor the logic into a shared helper function to avoid code duplication.