Sequential Thinking MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Sequential Thinking MCP ServerWalk me through solving a logic puzzle step by step."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Sequential Thinking MCP Server
This repository contains a Model Context Protocol (MCP) server that provides a sequentialthinking tool. The tool facilitates dynamic and reflective problem-solving through a chain of thoughts, allowing AI agents to break down complex problems, revise past thoughts, and explore branches of logic before arriving at a conclusion.
This project is configured to run in two modes:
Stdio: Standard MCP transport for local IDE and CLI integrations.
Server-Sent Events (SSE): Deployed as a secure, remote HTTP gateway on Google Cloud Run.
Local Development
Install dependencies and build the TypeScript code:
npm install
npm run buildTo run the Stdio server locally:
node dist/index.jsRelated MCP server: Visum Thinker MCP Server
Cloud Run Deployment (SSE Gateway)
This project can be easily cloned and deployed to any Google Cloud account or project. This allows anyone to host their own secure, remote Sequential Thinking MCP server.
Prerequisites
Install the Google Cloud CLI (
gcloud).Authenticate with your Google account:
gcloud auth loginSet your active Google Cloud Project (ensure billing is enabled):
gcloud config set project YOUR_PROJECT_IDEnable the required APIs for your project:
gcloud services enable run.googleapis.com cloudbuild.googleapis.com
Deploying
Once authenticated, you can deploy the service using the provided Makefile:
make deployWhat this does:
Packages the application and builds a Docker container using the provided
Dockerfile.Generates a secure, random
MCP_API_KEY(if one is not already provided).Deploys the container to Google Cloud Run (default region:
us-central1).Outputs the Service URL and your secure API key.
Client Configuration
Once deployed, you can configure your MCP client (Cursor, Hermes, Antigravity, Claude Desktop, etc.) to connect to the SSE gateway.
Provide your client with:
URL:
<YOUR_CLOUD_RUN_URL>/sseHeaders:
Authorization: Bearer <YOUR_MCP_API_KEY>
Available Tools
1 toolsequentialthinkingSequential ThinkingARead-onlyIdempotent
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
nextThoughtNeeded: True if you need more thinking, even if at what seemed like the end
thoughtNumber: Current number in sequence (can go beyond initial total if needed)
totalThoughts: Current estimate of thoughts needed (can be adjusted up/down)
isRevision: A boolean indicating if this thought revises previous thinking
revisesThought: If is_revision is true, which thought number is being reconsidered
branchFromThought: If branching, which thought number is the branching point
branchId: Identifier for the current branch (if any)
needsMoreThoughts: If reaching end but realizing more thoughts needed
You should:
Start with an initial estimate of needed thoughts, but be ready to adjust
Feel free to question or revise previous thoughts
Don't hesitate to add more thoughts if needed, even at the "end"
Express uncertainty when present
Mark thoughts that revise previous thinking or branch into new paths
Ignore information that is irrelevant to the current step
Generate a solution hypothesis when appropriate
Verify the hypothesis based on the Chain of Thought steps
Repeat the process until satisfied with the solution
Provide a single, ideally correct answer as the final output
Only set nextThoughtNeeded to false when truly done and a satisfactory answer is reached
| 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 (numeric value, e.g., 1, 2, 3) | |
| totalThoughts | Yes | Estimated total thoughts needed (numeric value, e.g., 5, 10) | |
| revisesThought | No | Which thought is being reconsidered | |
| branchFromThought | No | Branching point thought number | |
| needsMoreThoughts | No | If more thoughts are needed | |
| nextThoughtNeeded | No | Whether another thought step is needed |
Output Schema
| Name | Required | Description |
|---|---|---|
| branches | Yes | |
| thoughtNumber | Yes | |
| totalThoughts | Yes | |
| nextThoughtNeeded | Yes | |
| thoughtHistoryLength | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds context about the tool's adaptive and reflective nature, such as questioning previous thoughts and generating hypotheses. It does not contradict annotations and enriches understanding.
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 long but well-structured with sections, bullet points, and clear headings. It is front-loaded with purpose and use cases. However, it could be slightly more concise without losing essential information.
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?
Given the tool's complexity, the description covers all necessary aspects: purpose, when to use, key features, parameter explanations, and step-by-step guidance. The presence of an output schema further supports completeness.
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 coverage is 100%, but the description adds significant meaning beyond the schema by explaining each parameter in context (e.g., what 'thought' can include like revisions or hypothesis generation). It provides usage examples and clarifies the role of each parameter.
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 clearly states the tool is for dynamic and reflective problem-solving through thoughts. It lists specific use cases like breaking down complex problems and planning with room for revision, distinguishing it as a thinking tool even though no siblings exist.
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 provides a detailed 'When to use this tool' section and 'You should' steps, offering clear context and instructions for when to apply the tool. It does not explicitly state when not to use it, but the guidance is comprehensive.
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. Dates show when Glama detected each change.
1 tool update
v0.6.2- First observed
sequentialthinking
TDQS
Only one tool exists, so there is no ambiguity. The tool's purpose is clearly defined and distinct.
Single tool, so naming consistency is perfect. The name 'sequentialthinking' is descriptive and follows a single convention.
A single tool for a server focused on complex sequential thinking feels thin. Even though the tool is comprehensive, the count is at the lower boundary of acceptability.
The tool covers many aspects of sequential thinking: problem analysis, revision, branching, hypothesis generation, and verification. Minor gaps might exist in explicit state management, but overall it is quite complete for its domain.
Maintenance
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Related MCP Connectors
Deterministic reasoning stack for AI agents: simulate, decide & compute, plus cross-domain tools.
Decision memory for AI agents: record, revisit, and resolve consequential choices.
Agent-to-agent reasoning-as-a-service: chain-of-thought, analysis, and decision support.
Persistent memory and knowledge graphs for AI agents. Hybrid search, context checkpoints, and more.
Related MCP Servers
- AlicenseBqualityDmaintenanceEnhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.124MIT
- AlicenseBqualityNot gradedmaintenanceProvides structured sequential thinking capabilities for AI assistants to break down complex problems into manageable steps, revise thoughts, and explore alternative reasoning paths.29-
- AlicenseAqualityBmaintenanceEnables structured step-by-step reasoning with branching, revisions, and self-critique to help break down complex problems into manageable steps with confidence tracking and thought history search.7197MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to work through complex problems step-by-step with dynamic thought processes, allowing for revision of previous steps, exploration of alternative approaches, and flexible planning as understanding deepens.7MIT
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