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.
29
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Retrieval-Augmented Thinking MCP Server
by stat-guy
検索拡張思考MCPサーバー
構造化された検索拡張思考プロセスによってAIモデルの機能を強化するMCP（モデル・コンテキスト・プロトコル）サーバー実装。このサーバーは、動的な思考連鎖、並列探索パス、再帰的な改良サイクルを可能にし、推論と問題解決能力を向上させます。
特徴
適応型思考チェーン：分岐と修正機能により一貫した推論フローを維持します
反復的な仮説生成：仮説検定のための検証サイクルを実装する
コンテキストの一貫性: 非線形推論パス全体でコンテキストを維持する
ダイナミックスコープ調整：柔軟な探索と改良をサポート
品質評価：思考プロセスのリアルタイム評価
ブランチ管理: 並列探索パスを処理する
リビジョントラッキング: 再帰的な改良サイクルを管理します
Related MCP server: Sequential Thinking MVP Server
インストール
npm install @modelcontextprotocol/server-retrieval-augmented-thinking使用法
コマンドライン
mcp-server-retrieval-augmented-thinkingプログラムによる使用
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);ツール構成
サーバーは、次のパラメータを持つツールを提供します。
thought（文字列）：現在の推論ステップthoughtNumber（数字）：推論の連鎖における位置totalThoughts（数値）: 推定範囲nextThoughtNeeded(boolean): チェーン継続信号isRevision（ブール値、オプション）：改良ステップをマークするrevisesThought(数値、オプション): 参照対象の思考branchFromThought(数値、オプション): 分岐の起点branchId(文字列、オプション): ブランチ識別子needsMoreThoughts（ブール値、オプション）: スコープ拡張シグナル
高度な機能
思考連鎖分析
サーバーは思考連鎖の品質に関するさまざまな指標を追跡します。
チェーンの有効性
改訂の影響
ブランチ成功率
全体的な品質
個人の思考指標（複雑さ、深さ、質、影響）
パターン認識
以下の思考パターンを分析します:
推論構造
コンテキストの保存
仮説検証
ソリューションの一貫性
発達
# Build
npm run build
# Watch mode
npm run watch貢献
貢献を歓迎します！貢献ガイドラインをお読みになり、プルリクエストを送信してください。
ライセンス
マサチューセッツ工科大学
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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