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Sequential-Thinking

by xiaobenyang-com
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TypeScript

顺序思维服务器 Sequential Thinking

一个实现顺序思维协议的MCP服务器，提供结构化的问题解决方法，将复杂问题分解为可管理的步骤，并支持迭代优化和替代推理路径。 A MCP server that implements sequential thinking protocols, provides structured problem-solving methods, decomposes complex problems into manageable steps, and supports iterative optimization and alternative reasoning paths.## 工具列表 Tool List

本MCP服务封装下列工具，可让模型通过标准化接口调用以下功能。 本MCP服务封装下列工具，可让模型通过标准化接口调用以下功能。

工具 Tool

描述 Description

sequentialthinking

A detailed tool for dynamic and reflective problem-solving through thoughts. This tool helps analyze problems through a flexible thinking process that can adapt and evolve. Each thought can build on, question, or revise previous insights as understanding deepens. When to use this tool: - Breaking down complex problems into steps - Planning and design with room for revision - Analysis that might need course correction - Problems where the full scope might not be clear initially - Problems that require a multi-step solution - Tasks that need to maintain context over multiple steps - Situations where irrelevant information needs to be filtered out Key features: - You can adjust total_thoughts up or down as you progress - You can question or revise previous thoughts - You can add more thoughts even after reaching what seemed like the end - You can express uncertainty and explore alternative approaches - Not every thought needs to build linearly - you can branch or backtrack - Generates a solution hypothesis - Verifies the hypothesis based on the Chain of Thought steps - Repeats the process until satisfied - Provides a correct answer Parameters explained: - thought: Your current thinking step, which can include: * Regular analytical steps * Revisions of previous thoughts * Questions about previous decisions * Realizations about needing more analysis * Changes in approach * Hypothesis generation * Hypothesis verification - next_thought_needed: True if you need more thinking, even if at what seemed like the end - thought_number: Current number in sequence (can go beyond initial total if needed) - total_thoughts: Current estimate of thoughts needed (can be adjusted up/down) - is_revision: A boolean indicating if this thought revises previous thinking - revises_thought: If is_revision is true, which thought number is being reconsidered - branch_from_thought: If branching, which thought number is the branching point - branch_id: Identifier for the current branch (if any) - needs_more_thoughts: If reaching end but realizing more thoughts needed You should: 1. Start with an initial estimate of needed thoughts, but be ready to adjust 2. Feel free to question or revise previous thoughts 3. Don't hesitate to add more thoughts if needed, even at the "end" 4. Express uncertainty when present 5. Mark thoughts that revise previous thinking or branch into new paths 6. Ignore information that is irrelevant to the current step 7. Generate a solution hypothesis when appropriate 8. Verify the hypothesis based on the Chain of Thought steps 9. Repeat the process until satisfied with the solution 10. Provide a single, ideally correct answer as the final output 11. Only set next_thought_needed to false when truly done and a satisfactory answer is reached

检查服务 ## Inspector

工具在线测试： https://mcp.xiaobenyang.com/inspector/1777316659904515

Online Tool test https://mcp.xiaobenyang.com/inspector/1777316659904515

Related MCP server: Sequential Thinking Multi-Agent System

服务配置 MCP Server Config

如何获取 XBY-APIKEY ？ How to get XBY-APIKEY ?

访问小笨羊科技网站 https://xiaobenyang.com，注册用户即可获得APIKEY Visit XiaoBenYang website https://xiaobenyang.com, register and get the APIKEY.

SSE

{
  "mcpServers": {
    "顺序思维服务器": {
      "headers": {
        "XBY-APIKEY": "<YOUR_XBY_APIKEY>"
      },
      "type": "sse",
      "url": "https://mcp.xiaobenyang.com/1777316659904515/sse"
    }
  }
}

STREAMABLE HTTP

{
  "mcpServers": {
    "顺序思维服务器": {
      "headers": {
        "XBY-APIKEY": "<YOUR_XBY_APIKEY>"
      },
      "type": "streamable_http",
      "url": "https://mcp.xiaobenyang.com/1777316659904515/mcp"
    }
  }
}

STDIO

{
    "mcpServers": {
        "顺序思维服务器": {
          "command": "npx",
          "args": [
            "-y",
            "xiaobenyang-mcp"
          ],
          "env": {
            "XBY_APIKEY": "<YOUR_XBY_APIKEY>",
            "mcpId": "1777316659904515",
          },
          "transport": "stdio"
        }
      }
}

Available Tools

1 tool
sequentialthinkingsequentialthinkingA

A detailed tool for dynamic and reflective problem-solving through thoughts. This tool helps analyze problems through a flexible thinking process that can adapt and evolve. Each thought can build on, question, or revise previous insights as understanding deepens.

When to use this tool:

  • Breaking down complex problems into steps

  • Planning and design with room for revision

  • Analysis that might need course correction

  • Problems where the full scope might not be clear initially

  • Problems that require a multi-step solution

  • Tasks that need to maintain context over multiple steps

  • Situations where irrelevant information needs to be filtered out

Key features:

  • You can adjust total_thoughts up or down as you progress

  • You can question or revise previous thoughts

  • You can add more thoughts even after reaching what seemed like the end

  • You can express uncertainty and explore alternative approaches

  • Not every thought needs to build linearly - you can branch or backtrack

  • Generates a solution hypothesis

  • Verifies the hypothesis based on the Chain of Thought steps

  • Repeats the process until satisfied

  • Provides a correct answer

Parameters explained:

  • thought: Your current thinking step, which can include:

  • Regular analytical steps

  • Revisions of previous thoughts

  • Questions about previous decisions

  • Realizations about needing more analysis

  • Changes in approach

  • Hypothesis generation

  • Hypothesis verification

  • next_thought_needed: True if you need more thinking, even if at what seemed like the end

  • thought_number: Current number in sequence (can go beyond initial total if needed)

  • total_thoughts: Current estimate of thoughts needed (can be adjusted up/down)

  • is_revision: A boolean indicating if this thought revises previous thinking

  • revises_thought: If is_revision is true, which thought number is being reconsidered

  • branch_from_thought: If branching, which thought number is the branching point

  • branch_id: Identifier for the current branch (if any)

  • needs_more_thoughts: If reaching end but realizing more thoughts needed

You should:

  1. Start with an initial estimate of needed thoughts, but be ready to adjust

  2. Feel free to question or revise previous thoughts

  3. Don't hesitate to add more thoughts if needed, even at the "end"

  4. Express uncertainty when present

  5. Mark thoughts that revise previous thinking or branch into new paths

  6. Ignore information that is irrelevant to the current step

  7. Generate a solution hypothesis when appropriate

  8. Verify the hypothesis based on the Chain of Thought steps

  9. Repeat the process until satisfied with the solution

  10. Provide a single, ideally correct answer as the final output

  11. Only set next_thought_needed to false when truly done and a satisfactory answer is reached

ParametersJSON Schema
NameRequiredDescriptionDefault
thoughtYes
nextThoughtNeededYes
thoughtNumberYes
totalThoughtsYes
isRevisionNo
revisesThoughtNo
branchFromThoughtNo
branchIdNo
needsMoreThoughtsNo

TDQS

A4.6/5.0
Behavior5/5

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 extensively details the tool's behavior, including how thoughts can adapt (e.g., 'adjust total_thoughts up or down,' 'question or revise previous thoughts'), the iterative process (e.g., 'repeat until satisfied'), and output expectations (e.g., 'provides a correct answer'). This goes well beyond basic functionality.

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?

The description is appropriately structured with sections like 'When to use this tool,' 'Key features,' and 'Parameters explained,' but it is overly verbose with repetitive points (e.g., multiple mentions of revising thoughts). While informative, it could be more streamlined without losing essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (9 parameters, 0% schema coverage, no annotations, no output schema), the description is highly complete. It covers purpose, usage, behavior, parameter semantics, and process steps in detail, providing all necessary context for an AI agent to use the tool effectively despite the lack of structured data.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate fully. It provides a detailed 'Parameters explained' section that adds meaning for all 9 parameters, explaining their roles (e.g., 'thought: Your current thinking step,' 'is_revision: A boolean indicating if this thought revises previous thinking'). This effectively documents the parameters beyond the bare schema.

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?

The description clearly states the tool's purpose as 'dynamic and reflective problem-solving through thoughts' and 'analyze problems through a flexible thinking process,' which specifies the verb (problem-solving/analysis) and resource (thoughts/thinking process). However, with no sibling tools mentioned, there's no need for differentiation, so it doesn't reach the highest score of 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit 'When to use this tool' with seven specific scenarios (e.g., 'Breaking down complex problems into steps,' 'Planning and design with room for revision'), covering when to use it comprehensively. Since there are no sibling tools, alternatives aren't discussed, but the guidelines are thorough for the tool's context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.3/5.0
Disambiguation5/5

With only one tool named 'sequentialthinking', there is no possibility of confusion or overlap with other tools. The tool has a single, clearly defined purpose for dynamic problem-solving through sequential thoughts, making disambiguation perfect.

Naming Consistency5/5

The tool name 'sequentialthinking' follows a consistent, descriptive pattern as a single compound word. Since there is only one tool, naming consistency is inherently perfect with no deviations or mixed conventions to evaluate.

Tool Count2/5

The server has only one tool, which feels too thin for a general-purpose problem-solving domain implied by the tool's description. While the tool is feature-rich, a single tool limits scope and may not cover all potential use cases effectively, making the count inappropriate.

Completeness2/5

The tool surface is severely incomplete for the domain of dynamic problem-solving. It lacks complementary tools for tasks like data input/output, validation, or integration with external systems, creating significant gaps that could hinder agent workflows despite the tool's detailed functionality.

Maintenance

ActivityInactive
ResponsivenessNo issues

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