Sequential Thinking MCP Server
Click on "Deploy 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 Server Break down the problem of planning a cross-country road trip into logical steps."
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: Enhanced Sequential Thinking 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
Inputs: thought (required), thoughtNumber (required), totalThoughts (required), branchId, +5 more. Returns: branches, thoughtNumber, totalThoughts, nextThoughtNeeded, +1 more
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion between tools. The tool's purpose is clear and distinct.
With a single tool, naming consistency is inherently perfect. The name 'sequentialthinking' is descriptive and follows a clear pattern.
A single tool is borderline for a server; while the tool is comprehensive, the server's scope would typically benefit from multiple focused tools.
The tool covers the full sequential thinking process with parameters for revision, branching, and verification, leaving no obvious gaps for its stated purpose.
Maintenance
Related MCP Connectors
AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.
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Memory, reflection and safety for AI agents. Hosted or local, no vector database.
Google-OAuth-gated LLM gateway: verify a Google ID token, then run a Gemini (Vertex AI) completion f
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