Athena MCP
athena-mcp
当 Hermes 遇到困难时,它会求助于 Athena。
一个 MCP 服务器,为你的工具使用型智能体提供一个推理助手。只有一个工具:think。没有副作用——Athena 本身没有任何工具。她只负责推理。
当你的主智能体(Hermes、Claude Code、Cursor 或任何支持 MCP 的客户端)遇到难题时——比如微妙的 Bug、架构决策、需要评估的计划——它会调用 think 并获得一个简洁、逻辑严密的回复。你的智能体始终掌握主动权;Athena 是它在遇到难以处理的问题时求助的安静顾问。
灵感来源于 Codebuff 的 thinker agent,它在内部正是这样做的:Codebuff 的编排器在收集上下文后启动一个没有工具的 thinker-gpt,而该思考者的唯一任务就是深入思考并返回简短的答案。
为什么要分离?
大多数智能体在所有任务上都运行同一个模型。该模型是一种折中方案:既要足够快且便宜以应对数百次工具调用,又要足够聪明以处理大多数任务。但当它真正卡住时,你需要一个不同的模型——一个推理能力强的模型(Claude Opus、GPT-5、Gemini Pro、DeepSeek R1)——同时又不放弃对任务其余部分的控制。这就是 Athena 的用途。
成本 — 在每一步都运行推理模型非常昂贵。仅在需要时调用它们。
延迟 — 推理模型思考缓慢。将它们留给困难的问题。
工具正交性 — Athena 特意不配备任何工具。调用者始终控制副作用。
模型可移植性 — 无需重新配置整个智能体即可按需切换推理模型。
Related MCP server: think-mcp-server
两种后端
claude-code(当 claude CLI 在 PATH 中时为默认值) — 每次调用都会启动 claude -p 并使用你的 Anthropic Pro/Max 订阅 OAuth。无需 API 密钥,无需按 Token 计费。仅计入你的订阅配额。支持 opus、sonnet、haiku 或完整的 Claude 模型名称。
openrouter — 通过 HTTPS 调用 OpenRouter。通过按 Token 计费使用任何模型(Claude、GPT、Gemini、DeepSeek、Qwen 等)。需要 OPENROUTER_API_KEY。
通过 ATHENA_BACKEND=claude-code 或 ATHENA_BACKEND=openrouter 显式选择。
安装
git clone https://github.com/DevvGwardo/athena-mcp.git ~/projects/athena-mcp
cd ~/projects/athena-mcp
npm install
npm run build环境变量
变量 | 后端 | 默认值 | 说明 |
| 两者 | auto | 如果找到 |
| 两者 |
| 要使用的模型 |
| 两者 |
|
|
| claude-code | auto (PATH 查找) |
|
| claude-code | 180000 | 子进程超时时间 |
| openrouter | — | 必需 |
| openrouter | — | OpenRouter 分析标头 |
集成到 Hermes
Hermes 原生支持通过 stdio 进行 MCP 通信(Nous Research Hermes Agent)。
使用 Claude 订阅(推荐):
hermes mcp add athena \
--command /path/to/node \
--args /path/to/athena-mcp/dist/index.js \
--env ATHENA_CLAUDE_CLI=/opt/homebrew/bin/claude使用 OpenRouter:
hermes mcp add athena \
--command /path/to/node \
--args /path/to/athena-mcp/dist/index.js \
--env ATHENA_BACKEND=openrouter OPENROUTER_API_KEY=sk-or-v1-... ATHENA_MODEL=anthropic/claude-opus-4.6验证:
hermes mcp list # should show `athena`
hermes mcp test athena # should report "Connected" and 1 tool启动一个新的 Hermes 会话,智能体将看到一个 think 工具。
集成到 Claude Code
claude mcp add athena --command node --args /path/to/athena-mcp/dist/index.js集成到任何其他 MCP 客户端
这是一个标准的 stdio MCP 服务器。将你的客户端指向 node /path/to/athena-mcp/dist/index.js。
think 工具
字段 | 类型 | 必需 | 描述 | ||
| string | 是 | 需要推理的问题。可以很简短。 | ||
| string | 否 | 代码、对话摘录、错误消息——Athena 需要看到的任何内容。她无法读取文件。 | ||
|
|
|
| 否 | 推理力度。默认为 |
| string | 否 | 此调用的模型覆盖。格式取决于后端。 |
调用示例 (JSON-RPC):
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "think",
"arguments": {
"prompt": "Is there a race condition in the claim() function? If so, minimal fix?",
"context": "def claim(self, rid):\n if self.claims.get(rid):\n return False\n self.claims[rid] = self.agent_id\n return True",
"effort": "high"
}
}
}响应以文本形式返回,并带有页脚行:backend: ... · model: ... · effort: ... · duration · tokens。
设计说明
无状态。 每次调用都是独立的。为了保持对话连续性,请通过
context传递相关历史记录。<think>...</think>块 在 Athena 的响应中会在返回前被剥离——她可以将它们用作草稿空间而不会污染输出。符合 Codebuff 的约定。中立的
cwd。claude-code后端从os.tmpdir()启动,因此项目中的CLAUDE.md文件不会泄露到 Athena 的上下文中。--tools ""+--disable-slash-commands+--no-session-persistence在每次claude-code调用时确保她真正处于无工具和无状态模式。无重试逻辑。 如果后端出错,错误会清晰地呈现,以便调用者决定是否重试。
开发
npm run dev # tsc --watch
npm run build
npm start # runs dist/index.js (needs an MCP stdio peer)在不触及任何 API 的情况下进行冒烟测试:
printf '%s\n' \
'{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"t","version":"0"}}}' \
'{"jsonrpc":"2.0","method":"notifications/initialized"}' \
'{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
| node dist/index.js许可证
MIT
致谢
模式借鉴自 CodebuffAI 团队的 Codebuff,在此表示感谢。他们的思考者智能体是此设计的权威参考。
Available Tools
1 toolthinkA
Ask Athena to think. Delegates deep reasoning to a stronger model when you hit a hard problem — architecture decisions, subtle bugs, plan critique, tricky logic. Athena has NO tools; she only reasons and returns a concise response. You must gather relevant context yourself and pass it in via the context arg. Does not modify files, run commands, or access the network beyond the reasoning call.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The problem to think about. Can be brief — Athena will reason about it carefully. Example: "Is this race condition in the claim() function real, and if so what's the minimal fix?" | |
| context | No | Optional. Relevant code, conversation excerpts, error messages, or other material Athena needs. Athena cannot read files, so anything it needs to see must be pasted here. | |
| effort | No | Optional reasoning effort override. Defaults to high. Use "high" for the hardest problems, "low" for quick sanity checks. | |
| model | No | Optional model override. Defaults to anthropic/claude-opus-4.6. Accepted values for current backend (openrouter): anthropic/claude-opus-4.6, openai/gpt-5.4, google/gemini-3.1-pro-preview, deepseek/deepseek-r1. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: Athena 'only reasons and returns a concise response,' has 'NO tools,' and 'Does not modify files, run commands, or access the network beyond the reasoning call.' It also notes that 'You must gather relevant context yourself.' However, it lacks details on rate limits, authentication needs, or error handling, which would elevate it to a 5.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded, with the first sentence stating the core purpose. Each subsequent sentence adds critical information without redundancy: usage context, limitations, and parameter guidance. There is zero waste, and the structure flows logically from general to specific details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (reasoning delegation with no output schema) and lack of annotations, the description is mostly complete. It covers purpose, usage, behavioral traits, and some parameter context. However, without an output schema, it doesn't detail return values or error formats, which is a minor gap. For a tool with no annotations and no output schema, this is strong but not perfect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all four parameters thoroughly. The description adds minimal value beyond the schema, mentioning the 'context' arg briefly ('You must gather relevant context yourself and pass it in via the `context` arg.') but not explaining other parameters. This meets the baseline of 3 for high schema coverage, as the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Ask Athena to think. Delegates deep reasoning to a stronger model when you hit a hard problem — architecture decisions, subtle bugs, plan critique, tricky logic.' It specifies the verb ('think', 'delegates deep reasoning') and resource ('Athena', 'stronger model'), and distinguishes it from alternatives by noting 'Athena has NO tools; she only reasons and returns a concise response.' With no sibling tools, this level of specificity is excellent.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'when you hit a hard problem — architecture decisions, subtle bugs, plan critique, tricky logic.' It also clearly states when not to use it: 'Does not modify files, run commands, or access the network beyond the reasoning call.' With no sibling tools, this covers all necessary usage context, including exclusions and prerequisites like gathering context manually.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.3.0- First observed
think
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'think' has a clearly defined and distinct purpose focused solely on deep reasoning without any competing alternatives.
A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare against. The tool name 'think' is straightforward and follows a simple verb pattern without any conflicting conventions.
A single tool is generally too few for most server purposes, as it severely limits functionality and scope. While the tool is well-defined for reasoning tasks, the server lacks breadth, making it feel thin and potentially incomplete for broader applications.
The server is severely incomplete for any domain beyond basic reasoning, as it only offers a single tool for deep thinking without supporting actions like file modification, command execution, or network access. This creates significant gaps that will likely cause agent failures in practical workflows.
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Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceAn MCP server that implements the 'think' tool, providing Claude with a dedicated space for structured thinking during complex problem-solving tasks to improve reasoning capabilities.130MIT
- AlicenseAqualityDmaintenanceA minimal MCP Server that provides Claude AI models with the 'think' tool capability, enabling better performance on complex reasoning tasks by allowing the model to pause during response generation for additional thinking steps.134 npm25MIT
- AlicenseAqualityDmaintenanceAn MCP server implementation of Anthropic's Think Tool prompt engineering technique that enables Claude to break down complex problems and enhance its reasoning capabilities by providing a simple tool that echoes back thoughts.12ISC
- AlicenseBqualityFmaintenanceAn MCP server that provides a "think" tool enabling structured reasoning for AI agents, allowing them to pause and record explicit thoughts during complex tasks or multi-step tool use.115 PyPI107MIT