A
licenseB
qualityNot graded
maintenanceProvides structured sequential thinking capabilities for AI assistants to break down complex problems into manageable steps, revise thoughts, and explore alternative reasoning paths.
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Retrieval-Augmented Thinking MCP Server
by stat-guy
检索增强思维 MCP 服务器
MCP（模型上下文协议）服务器实现，通过结构化、检索增强的思维过程增强 AI 模型能力。该服务器支持动态思维链、并行探索路径和递归细化循环，从而提升推理和问题解决能力。
特征
自适应思维链：通过分支和修订功能保持连贯的推理流程
迭代假设生成：实施假设检验的验证周期
上下文连贯性：在非线性推理路径中保留上下文
动态范围调整：支持灵活的探索和细化
质量评估：实时评估思维过程
分支管理：处理并行探索路径
修订跟踪：管理递归细化周期
Related MCP server: Sequential Thinking MVP Server
安装
npm install @modelcontextprotocol/server-retrieval-augmented-thinking用法
命令行
mcp-server-retrieval-augmented-thinking程序化使用
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);工具配置
服务器提供了一个具有以下参数的工具：
thought（字符串）：当前推理步骤thoughtNumber（数字）：推理链中的位置totalThoughts（数量）：估计范围nextThoughtNeeded（布尔值）：链延续信号isRevision（布尔值，可选）：标记细化步骤revisesThought（数字，可选）：参考目标思想branchFromThought（数字，可选）：分支原点branchId（字符串，可选）：分支标识符needsMoreThoughts（布尔值，可选）：范围扩展信号
高级功能
思维链分析
服务器跟踪思想链质量的各种指标：
链效性
修订影响
分支成功率
总体质量
个人思维指标（复杂性、深度、质量、影响力）
模式识别
分析思维模式：
推理结构
上下文保存
假设验证
解决方案的一致性
发展
# Build
npm run build
# Watch mode
npm run watch贡献
欢迎贡献！请阅读我们的贡献指南并提交 PR。
执照
麻省理工学院
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| thought | Yes | Your current thinking step | |
| branchId | No | Branch identifier | |
| isRevision | No | Whether this revises previous thinking | |
| thoughtNumber | Yes | Current thought number | |
| totalThoughts | Yes | Estimated total thoughts needed | |
| revisesThought | No | Which thought is being reconsidered | |
| branchFromThought | No | Branching point thought number | |
| needsMoreThoughts | No | If more thoughts are needed | |
| nextThoughtNeeded | Yes | Whether another thought step is needed |
TDQS
B3.1/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes capabilities like branching and revisions but does not disclose side effects, statefulness, or what the tool returns when invoked. The behavior is described conceptually rather than practically.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Structured with sections and bullet points, making it skimmable. However, it is somewhat verbose with overlapping sections (Core Capabilities vs Reasoning Patterns). Could be tightened without losing meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is thorough about reasoning patterns but omits what the tool actually returns or how the agent should interpret the result. With no output schema and no annotations, this is a significant gap for correct invocation and result handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers all 9 parameters with basic descriptions. The description adds value by categorizing what each parameter supports (e.g., 'thought' supports hypothesis formulation, branch exploration, etc.) and providing domain context for fields like branch_id and is_revision.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it is a context-aware reasoning system that orchestrates structured thought processes. The verb 'orchestrates' and resource 'thought processes' are explicit, but the tool's operational purpose for an agent is somewhat abstract; no siblings to differentiate from.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides an Execution Protocol with steps, implying a multi-step reasoning workflow. However, it does not explicitly state when to use this tool versus other tools, as there are no siblings, nor does it give conditions for non-use.
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
v0.1.0- First observed
rat
TDQS
B3.1/5.0
Scored across 1 tool
Disambiguation5/5
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.
Naming Consistency2/5
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.
Tool Count3/5
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.
Completeness2/5
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.
Maintenance
ActivityInactive
ResponsivenessNo issues
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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