Athena MCP
athena-mcp
Cuando Hermes está atascado, le pregunta a Athena.
Un servidor MCP que le da a tu agente que utiliza herramientas un asistente de razonamiento. Una herramienta: think. Sin efectos secundarios: Athena no tiene herramientas propias. Ella solo razona.
Cuando tu agente principal (Hermes, Claude Code, Cursor, cualquier cliente que hable MCP) se encuentra con un problema difícil —un error sutil, una decisión de arquitectura, un plan que necesita crítica— llama a think y recibe una respuesta concisa y bien razonada. Tu agente sigue al mando; Athena es la consultora silenciosa a la que recurre cuando el problema es más espinoso de lo que su modelo predeterminado puede manejar bien.
Inspirado en el agente pensador de Codebuff, que hace exactamente esto internamente: el orquestador de Codebuff genera thinker-gpt sin herramientas después de recopilar contexto, y el único trabajo del pensador es pensar profundamente y devolver una respuesta breve.
¿Por qué la separación?
La mayoría de los agentes ejecutan un solo modelo para todo. Ese modelo es un compromiso: lo suficientemente rápido y barato para cientos de llamadas a herramientas, lo suficientemente inteligente para la mayoría de ellas. Pero cuando realmente está atascado, quieres un modelo diferente —uno con gran capacidad de razonamiento (Claude Opus, GPT-5, Gemini Pro, DeepSeek R1)— sin ceder el control del resto de la tarea. Para eso es Athena.
Coste — los modelos de razonamiento son caros de ejecutar en cada turno. Llámalos solo cuando sea necesario.
Latencia — los modelos de razonamiento piensan lentamente. Guárdalos para problemas difíciles.
Ortogonalidad de herramientas — Athena no tiene herramientas a propósito. El llamador mantiene el control de los efectos secundarios.
Portabilidad del modelo — cambia los modelos de razonamiento por llamada sin reconfigurar todo tu agente.
Related MCP server: think-mcp-server
Dos backends
claude-code (predeterminado cuando la CLI claude está en el PATH) — genera claude -p para cada llamada y utiliza tu OAuth de suscripción Pro/Max de Anthropic. Sin clave API, sin facturación por token. Solo cuenta contra la cuota de tu suscripción. Admite opus, sonnet, haiku o nombres completos de modelos de Claude.
openrouter — llamada HTTPS a OpenRouter. Cualquier modelo (Claude, GPT, Gemini, DeepSeek, Qwen, lo que sea) mediante facturación por token. Requiere OPENROUTER_API_KEY.
Elige explícitamente con ATHENA_BACKEND=claude-code o ATHENA_BACKEND=openrouter.
Instalación
git clone https://github.com/DevvGwardo/athena-mcp.git ~/projects/athena-mcp
cd ~/projects/athena-mcp
npm install
npm run buildEntorno
Var | Backend | Predeterminado | Notas |
| ambos | auto |
|
| ambos |
| Modelo a utilizar |
| ambos |
|
|
| claude-code | auto (búsqueda en PATH) | Ruta absoluta al binario |
| claude-code | 180000 | Tiempo de espera del subproceso |
| openrouter | — | Requerido |
| openrouter | — | Cabeceras de análisis de OpenRouter |
Conectar a Hermes
Hermes habla MCP sobre stdio de forma nativa (Nous Research Hermes Agent).
Con una suscripción a Claude (recomendado):
hermes mcp add athena \
--command /path/to/node \
--args /path/to/athena-mcp/dist/index.js \
--env ATHENA_CLAUDE_CLI=/opt/homebrew/bin/claudeCon 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-4.6Verificar:
hermes mcp list # should show `athena`
hermes mcp test athena # should report "Connected" and 1 toolInicia una nueva sesión de Hermes y el agente verá una herramienta think.
Conectar a Claude Code
claude mcp add athena --command node --args /path/to/athena-mcp/dist/index.jsConectar a cualquier otro cliente MCP
Es un servidor MCP stdio estándar. Apunta tu cliente a node /path/to/athena-mcp/dist/index.js.
La herramienta think
Campo | Tipo | Requerido | Descripción | ||
| string | sí | El problema sobre el que razonar. Puede ser breve. | ||
| string | no | Código, extracto de conversación, mensajes de error: cualquier cosa que Athena necesite ver. Ella no puede leer archivos. | ||
|
|
|
| no | Esfuerzo de razonamiento. Por defecto |
| string | no | Sustitución de modelo para esta llamada. El formato depende del backend. |
Ejemplo de llamada (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"
}
}
}La respuesta vuelve como texto con una línea de pie de página: backend: ... · model: ... · effort: ... · duration · tokens.
Notas de diseño
Sin estado. Cada llamada es independiente. Para la continuidad de la conversación, pasa el historial relevante a través de
context.Los bloques
<think>...</think>en la respuesta de Athena se eliminan antes de devolverla; ella puede usarlos como espacio de trabajo sin contaminar la salida. Coincide con la convención de Codebuff.cwdneutral. El backend claude-code se genera desdeos.tmpdir()para que los archivosCLAUDE.mddel proyecto no se filtren en el contexto de Athena.--tools ""+--disable-slash-commands+--no-session-persistenceen cada llamada de claude-code la mantienen verdaderamente sin herramientas y sin estado.Sin lógica de reintento. Si el backend falla, el error aparece claramente para que el llamador decida si reintentar.
Desarrollo
npm run dev # tsc --watch
npm run build
npm start # runs dist/index.js (needs an MCP stdio peer)Prueba de humo sin tocar ninguna API:
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.jsLicencia
MIT
Créditos
Patrón tomado prestado, con agradecimiento, de Codebuff por el equipo de CodebuffAI. Su agente pensador es la referencia canónica para este diseño.
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.
Maintenance
Related MCP Connectors
MCP server for building and testing AI agents with multi-model experimentation and insights.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
MCP server for progressive tool usage at any scale (see https://klavis.ai)
Official MCP server for Agentwork — delegate tasks to AI agents with human-in-the-loop
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.144 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.164 PyPI107MIT