Retrieval-Augmented Thinking MCP Server
Servidor MCP de pensamiento aumentado por recuperación
Una implementación de servidor MCP (Protocolo de Contexto de Modelo) que mejora las capacidades de los modelos de IA con procesos de pensamiento estructurados y de recuperación aumentada. Este servidor permite cadenas de pensamiento dinámicas, rutas de exploración paralelas y ciclos de refinamiento recursivos para mejorar el razonamiento y la resolución de problemas.
Características
Cadenas de pensamiento adaptativas : mantiene flujos de razonamiento coherentes con capacidades de ramificación y revisión.
Generación iterativa de hipótesis : implementa ciclos de validación para la prueba de hipótesis
Coherencia del contexto : preserva el contexto a lo largo de caminos de razonamiento no lineales
Ajuste dinámico del alcance : admite exploración y refinamiento flexibles
Evaluación de calidad : evaluación en tiempo real de los procesos de pensamiento
Gestión de sucursales : gestiona rutas de exploración paralelas
Seguimiento de revisiones : gestiona ciclos de refinamiento recursivos
Related MCP server: Sequential Thinking MVP Server
Instalación
npm install @modelcontextprotocol/server-retrieval-augmented-thinkingUso
Línea de comandos
mcp-server-retrieval-augmented-thinkingUso programático
import { Server } from '@modelcontextprotocol/sdk/server';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio';
// Initialize and run the server
const server = new Server({
name: 'retrieval-augmented-thinking',
version: '0.1.0'
});
// Connect transport
const transport = new StdioServerTransport();
await server.connect(transport);Configuración de herramientas
El servidor proporciona una herramienta con los siguientes parámetros:
thought(cadena): paso de razonamiento actualthoughtNumber(número): Posición en la cadena de razonamientototalThoughts(número): Alcance estimadonextThoughtNeeded(booleano): señal de continuación de cadenaisRevision(booleano, opcional): marca los pasos de refinamientorevisesThought(número, opcional): Referencias al pensamiento objetivobranchFromThought(número, opcional): punto de origen de la ramabranchId(cadena, opcional): identificador de ramaneedsMoreThoughts(booleano, opcional): Señal de expansión del alcance
Funciones avanzadas
Análisis de la cadena de pensamiento
El servidor rastrea varias métricas para la calidad de la cadena de pensamiento:
Eficacia de la cadena
Impacto de la revisión
Tasa de éxito de la sucursal
Calidad general
Métricas de pensamiento individual (complejidad, profundidad, calidad, impacto)
Reconocimiento de patrones
Analiza patrones de pensamiento para:
Estructuras de razonamiento
Preservación del contexto
Validación de hipótesis
Coherencia de la solución
Desarrollo
# Build
npm run build
# Watch mode
npm run watchContribuyendo
¡Agradecemos sus contribuciones! Lea nuestras normas de contribución y envíe solicitudes de incorporación de cambios.
Licencia
Instituto Tecnológico de Massachusetts (MIT)
Available Tools
1 toolratB
A context-aware reasoning system that orchestrates structured thought processes through dynamic trajectories.
Core Capabilities:
Maintains adaptive thought chains with branching and revision capabilities
Implements iterative hypothesis generation and validation cycles
Preserves context coherence across non-linear reasoning paths
Supports dynamic scope adjustment and trajectory refinement
Reasoning Patterns:
Sequential analysis with backtracking capability
Parallel exploration through managed branch contexts
Recursive refinement via structured revision cycles
Hypothesis validation through multi-step verification
Parameters: thought: Structured reasoning step that supports: • Primary analysis chains • Hypothesis formulation/validation • Branch exploration paths • Revision proposals • Context preservation markers • Verification checkpoints
next_thought_needed: Signal for continuation of reasoning chain thought_number: Position in current reasoning trajectory total_thoughts: Dynamic scope indicator (adjustable) is_revision: Marks recursive refinement steps revises_thought: References target of refinement branch_from_thought: Indicates parallel exploration paths branch_id: Context identifier for parallel chains needs_more_thoughts: Signals scope expansion requirement
Execution Protocol:
Initialize with scope estimation
Generate structured reasoning steps
Validate hypotheses through verification cycles
Maintain context coherence across branches
Implement revisions through recursive refinement
Signal completion on validation success
The system maintains solution integrity through continuous validation cycles while supporting dynamic scope adjustment and non-linear exploration paths.
Inputs: thought (required), thoughtNumber (required), totalThoughts (required), nextThoughtNeeded (required), +5 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 1 tool
Only one tool exists, so there is no possibility of confusion or misselection. The tool's purpose, while broad, is clearly the sole entry point.
The single tool name 'rat' is vague and does not follow a clear verb_noun pattern or any recognizable convention. With only one tool, there's no consistent pattern to infer, and the name appears arbitrary.
Exposing just one tool is on the lower end of acceptable, borderline 'thin.' Although the tool is highly capable, a server focused on 'Retrieval-Augmented Thinking' might benefit from separate tools for retrieval and reasoning sub-tasks.
The server name implies both retrieval and thinking, but the tool only covers the reasoning aspect, missing retrieval or external context fetching. This leaves a significant gap in the expected functionality, and the single tool is overloaded with parameters rather than modularly covering the domain.
Related MCP Connectors
Agent-to-agent reasoning-as-a-service: chain-of-thought, analysis, and decision support.
Deterministic reasoning stack for AI agents: simulate, decide & compute, plus cross-domain tools.
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
Decision memory for AI agents: record, revisit, and resolve consequential choices.
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