Chain of Draft Thinking
MCP 链草稿服务器
Chain of Draft Server 是一款强大的 AI 驱动工具,它通过系统化、迭代式地完善思路和设计,帮助开发者做出更明智的决策。它能够与主流 AI 代理无缝集成,并提供结构化的推理、API 设计、架构决策、代码审查和实施规划方法。
🌟 功能
核心能力
迭代推理:通过草案链协议进行系统改进
思维史:跟踪和管理推理迭代
分支支持:重点回顾特定的推理步骤
TypeScript 支持:通过 Zod 验证实现完整的 TypeScript
错误处理:全面的错误类型和处理
实时日志记录:内置调试和监控系统
Related MCP server: Chain of Draft Prompt Tool
🚀 入门
先决条件
Node.js >= 16.0.0
npm >= 8.0.0
安装
克隆存储库:
git clone https://github.com/bsmi021/mcp-chain-of-draft-server.git
cd mcp-chain-of-draft-server安装依赖项:
npm install配置
在initialize.ts中进行简单的服务器配置:
const serverConfig = {
name: "chain-of-draft",
version: "1.0.0",
}💡 使用示例
草案协议链
const thoughtData = {
reasoning_chain: ["Initial analysis of the problem"],
next_step_needed: true,
draft_number: 1,
total_drafts: 3,
is_critique: true,
critique_focus: "logical_consistency"
};🛠️ 开发
项目结构
src/
├── tools/ # Specialized Tools
│ ├── chainOfDraft/ # Core Protocol
│ └── index.ts / # Entry Point
├── utils/ # Utilities
└── index.ts # Entry Point启动开发服务器
npm run dev❓ 常见问题解答
Chain of Draft 协议如何运作?
该协议将指导您通过迭代草稿和重点批评来系统地改进您的思维。
我可以自定义批评维度吗?
是的!每个工具都支持根据您的特定需求定制评论重点。
我应该计划多少份草稿?
我们建议大多数任务需要 3-5 个草稿,但您可以根据复杂性进行调整。
🤝 贡献
欢迎大家投稿!请查看我们的投稿指南。
👥 社区与支持
GitHub 问题 - 报告错误或建议功能
拉取请求 - 提交你的贡献
文档 - 查看我们的详细文档
📝 许可证
MIT 许可证 - 有关详细信息,请参阅LICENSE文件。
🙏 致谢
感谢我们的贡献者和早期采用者
特别感谢 MCP 社区
受到系统推理方法的启发
由 @bsmi021 使用 🧠 制作
Available Tools
1 toolchain-of-draftA
# Chain of Draft (CoD): Systematic Reasoning Tool⚠️ REQUIRED PARAMETERS - ALL MUST BE PROVIDED:
reasoning_chain: string[] - At least one reasoning step
next_step_needed: boolean - Whether another iteration is needed
draft_number: number - Current draft number (≥ 1)
total_drafts: number - Total planned drafts (≥ draft_number)
Optional parameters only required based on context:
is_critique?: boolean - If true, critique_focus is required
critique_focus?: string - Required when is_critique=true
revision_instructions?: string - Recommended for revision steps
step_to_review?: number - Specific step index to review
is_final_draft?: boolean - Marks final iteration
Purpose:
Enhances problem-solving through structured, iterative critique and revision.
Chain of Draft is an advanced reasoning tool that enhances problem-solving through structured, iterative critique and revision. Unlike traditional reasoning approaches, CoD mimics the human drafting process to improve clarity, accuracy, and robustness of conclusions.
When to Use This Tool:
Complex Problem-Solving: Tasks requiring detailed, multi-step analysis with high accuracy demands
Critical Reasoning: Problems where logical flow and consistency are essential
Error-Prone Scenarios: Questions where initial reasoning might contain mistakes or oversight
Multi-Perspective Analysis: Cases benefiting from examining a problem from different angles
Self-Correction Needs: When validation and refinement of initial thoughts are crucial
Detailed Solutions: Tasks requiring comprehensive explanations with supporting evidence
Mathematical or Logical Puzzles: Problems with potential for calculation errors or logical gaps
Nuanced Analysis: Situations with subtle distinctions that might be missed in a single pass
Key Capabilities:
Iterative Improvement: Systematically refines reasoning through multiple drafts
Self-Critique: Critically examines previous reasoning to identify flaws and opportunities
Focused Revision: Targets specific aspects of reasoning in each iteration
Perspective Flexibility: Can adopt different analytical viewpoints during critique
Progressive Refinement: Builds toward optimal solutions through controlled iterations
Context Preservation: Maintains understanding across multiple drafts and revisions
Adaptable Depth: Adjusts the number of iterations based on problem complexity
Targeted Improvements: Addresses specific weaknesses in each revision cycle
Parameters Explained:
reasoning_chain: Array of strings representing your current reasoning steps. Each element should contain a clear, complete thought that contributes to the overall analysis.
next_step_needed: Boolean flag indicating whether additional critique or revision is required. Set to true until the final, refined reasoning chain is complete.
draft_number: Integer tracking the current iteration (starting from 1). Increments with each critique or revision.
total_drafts: Estimated number of drafts needed for completion. This can be adjusted as the solution evolves.
is_critique: Boolean indicating the current mode:
true = Evaluating previous reasoning
false = Implementing revisions
critique_focus: (Required when is_critique=true) Specific aspect being evaluated, such as:
"logical_consistency": Checking for contradictions or flaws in reasoning
"factual_accuracy": Verifying correctness of facts and calculations
"completeness": Ensuring all relevant aspects are considered
"clarity": Evaluating how understandable the reasoning is
"relevance": Assessing if reasoning directly addresses the problem
revision_instructions: (Required when is_critique=false) Detailed guidance for improving the reasoning based on the preceding critique.
step_to_review: (Optional) Zero-based index of the specific reasoning step being critiqued or revised. When omitted, applies to the entire chain.
is_final_draft: (Optional) Boolean indicating whether this is the final iteration of reasoning.
Best Practice Workflow:
Start with Initial Draft: Begin with your first-pass reasoning and set a reasonable total_drafts (typically 3-5).
Alternate Critique and Revision: Use is_critique=true to evaluate reasoning, then is_critique=false to implement improvements.
Focus Each Critique: Choose a specific critique_focus for each evaluation cycle rather than attempting to address everything at once.
Provide Detailed Revision Guidance: Include specific, actionable revision_instructions based on each critique.
Target Specific Steps When Needed: Use step_to_review to focus on particular reasoning steps that need improvement.
Adjust Total Drafts As Needed: Modify total_drafts based on problem complexity and progress.
Mark Completion Appropriately: Set next_step_needed=false only when the reasoning chain is complete and satisfactory.
Aim for Progressive Improvement: Each iteration should measurably improve the reasoning quality.
Example Application:
Initial Draft: First-pass reasoning about a complex problem
Critique #1: Focus on logical consistency and identify contradictions
Revision #1: Address logical flaws found in the critique
Critique #2: Focus on completeness and identify missing considerations
Revision #2: Incorporate overlooked aspects and strengthen reasoning
Final Critique: Holistic review of clarity and relevance
Final Revision: Refine presentation and ensure direct addressing of the problem
Chain of Draft is particularly effective when complex reasoning must be broken down into clear steps, analyzed from multiple perspectives, and refined through systematic critique. By mimicking the human drafting process, it produces more robust and accurate reasoning than single-pass approaches.
| Name | Required | Description | Default |
|---|---|---|---|
| critique_focus | No | The specific aspect or dimension being critiqued in the current evaluation (e.g., 'logical_consistency', 'factual_accuracy', 'completeness', 'clarity', 'relevance'). Required when is_critique is true. | |
| draft_number | Yes | Current draft number in the iteration sequence (must be >= 1). Increments with each new critique or revision. | |
| is_critique | No | Boolean flag indicating whether the current step is a critique phase (true) evaluating previous reasoning, or a revision phase (false) implementing improvements. | |
| is_final_draft | No | Boolean flag indicating whether this is the final draft in the reasoning process. Helps signal the completion of the iterative refinement. | |
| new_reasoning_steps | Yes | New reasoning steps to add to the chain | |
| next_step_needed | Yes | Boolean flag indicating whether another critique or revision cycle is needed in the reasoning chain. Set to false only when the final, satisfactory conclusion has been reached. | |
| reasoning_chain | Yes | Array of strings representing the current chain of reasoning steps. Each step should be a clear, complete thought that contributes to the overall analysis or solution. | |
| revision_instructions | No | Detailed, actionable guidance for how to revise the reasoning based on the preceding critique. Should directly address issues identified in the critique. Required when is_critique is false. | |
| step_to_review | No | Zero-based index of the specific reasoning step being targeted for critique or revision. When omitted, the critique or revision applies to the entire reasoning chain. | |
| total_drafts | Yes | Estimated total number of drafts needed to reach a complete solution (must be >= draft_number). Can be adjusted as the solution evolves. |
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 does an excellent job explaining the tool's iterative nature, critique/revision modes, parameter dependencies, and workflow patterns. However, it doesn't explicitly mention potential limitations like computational cost or time requirements for multiple iterations.
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 excessively long (over 800 words) with redundant sections. While well-structured with headings, it repeats information (e.g., parameter explanations appear in both the initial list and a dedicated section) and includes unnecessary elaboration that doesn't add proportional value for tool selection and invocation.
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?
For a complex 10-parameter tool with no annotations or output schema, the description provides substantial context about workflow, usage scenarios, and behavioral patterns. It adequately compensates for the lack of structured metadata, though the absence of output information (what the tool returns) is a minor gap given the tool's complexity.
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 parameters thoroughly. The description's 'Parameters Explained' section adds some contextual meaning (e.g., explaining what different critique_focus values represent), but mostly restates what's already in the schema descriptions. This meets the baseline for high schema coverage.
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 explicitly states the tool's purpose as 'enhances problem-solving through structured, iterative critique and revision' and provides a detailed explanation of how it works. It clearly distinguishes this as an 'advanced reasoning tool' that 'mimics the human drafting process' for improving reasoning quality, which is specific and actionable.
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 includes a dedicated 'When to Use This Tool' section with 8 specific scenarios (e.g., 'Complex Problem-Solving', 'Critical Reasoning', 'Error-Prone Scenarios'), plus a 'Best Practice Workflow' with 8 steps and an 'Example Application' section. This provides comprehensive guidance on when and how to use the tool effectively.
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
v1.0.0- First observed
chain-of-draft
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool has a single, clearly defined purpose: structured iterative reasoning with critique and revision cycles. This eliminates any ambiguity in tool selection entirely.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The tool name 'chain-of-draft' follows a clear, descriptive kebab-case pattern that matches the server name, establishing a coherent identity without any naming conflicts or variations.
A single tool is generally too few for most server purposes, as it limits functionality and forces all operations through one interface. While this tool is complex and multi-faceted, a server with only one tool often feels thin and may not provide adequate coverage for varied tasks, suggesting a mismatch between the tool's depth and the server's scope.
The tool comprehensively covers the iterative reasoning process with parameters for drafts, critiques, and revisions, but as a single-tool server, it inherently lacks breadth. There are no obvious gaps within its defined domain of structured reasoning, but the server's overall surface is limited to this one function, which may not suffice for broader problem-solving needs.
Maintenance
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