Offers containerized deployment of the Sequential Thinking MCP server, allowing for isolated and portable execution environments.
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 help me plan a marketing campaign for our new product launch"
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.
An MCP server implementation that provides a tool for dynamic and reflective problem-solving through a structured thinking process.
Break down complex problems into manageable steps
Revise and refine thoughts as understanding deepens
Branch into alternative paths of reasoning
Adjust the total number of thoughts dynamically
Generate and verify solution hypotheses
Related MCP server: Think MCP
Facilitates a detailed, step-by-step thinking process for problem-solving and analysis.
Inputs:
thought (string): The current thinking step
nextThoughtNeeded (boolean): Whether another thought step is needed
thoughtNumber (integer): Current thought number
totalThoughts (integer): Estimated total thoughts needed
isRevision (boolean, optional): Whether this revises previous thinking
revisesThought (integer, optional): Which thought is being reconsidered
branchFromThought (integer, optional): Branching point thought number
branchId (string, optional): Branch identifier
needsMoreThoughts (boolean, optional): If more thoughts are needed
The Sequential Thinking tool is designed for:
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
Tasks that need to maintain context over multiple steps
Situations where irrelevant information needs to be filtered out
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"sequential-thinking": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-sequential-thinking"
]
}
}
}{
"mcpServers": {
"sequentialthinking": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"mcp/sequentialthinking"
]
}
}
}Docker:
docker build -t mcp/sequentialthinking -f src/sequentialthinking/Dockerfile .This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.
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:
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 next_thought_needed to false when truly done and a satisfactory answer is reached
| Name | Required | Description | Default |
|---|---|---|---|
| thought | Yes | Your current thinking step | |
| nextThoughtNeeded | Yes | Whether another thought step is needed | |
| thoughtNumber | Yes | Current thought number | |
| totalThoughts | Yes | Estimated total thoughts needed | |
| isRevision | No | Whether this revises previous thinking | |
| revisesThought | No | Which thought is being reconsidered | |
| branchFromThought | No | Branching point thought number | |
| branchId | No | Branch identifier | |
| needsMoreThoughts | No | If more thoughts are needed |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully carries the burden of behavioral disclosure. It details the iterative process including revision, branching, hypothesis generation, verification, and repetition. It explains that thoughts can build, question, or revise previous insights. This level of detail fully informs the agent of the tool's behavior.
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 well-structured with clear sections (When to use, Key features, Parameters explained, You should). It is front-loaded with the tool's purpose. However, it is somewhat verbose, particularly the 'You should' list which repeats earlier points (e.g., generating and verifying hypothesis). Trimming redundant points could improve conciseness without losing value.
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 complexity (9 parameters, no output schema, no annotations), the description covers most aspects: purpose, usage guidelines, parameter details, and behavioral process. The only gap is that it does not mention the return value format or output structure. Since there is no output schema, the description could have briefly described what the tool returns (e.g., final answer or thought log) to be fully complete.
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?
Although schema coverage is 100%, the description adds substantial meaning beyond the schema. For example, the 'thought' parameter is described as including regular analytical steps, revisions, questions, etc. The 'next_thought_needed' and 'total_thoughts' parameters are explained with usage context. This extra information greatly helps the agent understand how to use each parameter effectively.
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's purpose: 'dynamic and reflective problem-solving through thoughts.' It specifies that it helps analyze problems through a flexible thinking process. Since there are no sibling tools, differentiation is not needed. The verb 'helps analyze' and the resource 'problems' make it 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 provides a dedicated 'When to use this tool' section listing scenarios like breaking down complex problems, planning with revision, and analysis needing course correction. It offers clear context but does not explicitly state when to avoid using the tool or mention alternatives. With no sibling tools, the lack of exclusions is acceptable, but the guidance could be more comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
sequentialthinkingScored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is clearly defined as a dynamic problem-solving process, making it distinct by default.
A single tool inherently has consistent naming, as there are no other tools to compare it against. The name 'sequentialthinking' is descriptive and follows a clear, albeit unique, pattern for this server.
One tool is too few for a server that aims to facilitate dynamic and reflective problem-solving, as it forces all functionality into a single, complex interface. This limits flexibility and could overwhelm users or agents with its many parameters, making it feel thin in scope despite its depth.
The tool covers a broad range of problem-solving steps, but as a single tool, it lacks modularity. There are no obvious gaps in functionality, but the monolithic design might lead to dead ends or inefficiencies in handling diverse tasks that could benefit from separate, specialized tools.
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