Long Reasoning 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., "@Long Reasoning MCP Serverexplain quantum entanglement 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.
Long Reasoning MCP Server
A production-ready Model Context Protocol (MCP) server implementing Chain of Thought methodology for super long, complex reasoning tasks. Extended Sequential Thinking.
Links:
Installation
npm install -g long-reasoning-mcpOr use directly with npx (no installation required):
npx long-reasoning-mcpRelated MCP server: Enhanced Sequential Thinking MCP Server
Configuration
Configure Your AI Client with MCP
Using npx (recommended):
{
"mcpServers": {
"long-reasoning": {
"command": "npx",
"args": [
"-y",
"long-reasoning-mcp"
]
}
}
}Using local installation:
{
"mcpServers": {
"long-reasoning": {
"command": "node",
"args": [
"/path/to/longThinking/dist/index.js"
]
}
}
}Restart your AI client and the longreasoning tool will be available!
Available Tools
1 toolsequentialthinkingA
A detailed tool for DEEP, EXTENSIVE, and dynamic problem-solving through extended thinking. This tool is designed for MAXIMUM DEPTH research and analysis with no artificial limits. Each thought can build on, question, or revise previous insights as understanding deepens.
IMPORTANT: For deep research tasks, you should:
Use 50+ thoughts minimum for complex problems, 100+ for deep research
Take time to explore multiple angles and perspectives
Question assumptions repeatedly throughout the process
Revise and refine understanding as you progress
Branch into alternative approaches when valuable
Generate multiple hypotheses and verify each thoroughly
Go as deep as needed - there is no maximum limit
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
Deep research requiring extensive exploration
Multi-hypothesis generation and verification
Comprehensive analysis across multiple dimensions
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 UPWARD frequently
Feel free to question or revise previous thoughts extensively
Don't hesitate to add more thoughts if needed, even at the "end" - research has no artificial limits
Express uncertainty when present and explore it deeply
Mark thoughts that revise previous thinking or branch into new paths
Ignore information that is irrelevant to the current step
Generate MULTIPLE solution hypotheses when appropriate, not just one
Verify each hypothesis thoroughly based on the Chain of Thought steps
Repeat the hypothesis-verification cycle multiple times for robustness
Explore edge cases, counterexamples, and alternative interpretations
For deep research: aim for 100+ thoughts, exploring breadth AND depth
Use branching extensively to explore alternative paths in parallel
Perform multiple revision passes to refine understanding
Only set next_thought_needed to false when truly done with COMPREHENSIVE analysis
Provide a single, well-researched, thoroughly verified answer as the final output
| Name | Required | Description | Default |
|---|---|---|---|
| thought | Yes | Your current thinking step | |
| nextThoughtNeeded | Yes | Whether another thought step is needed | |
| thoughtNumber | Yes | Current thought number (numeric value, e.g., 1, 2, 3) | |
| totalThoughts | Yes | Estimated total thoughts needed (numeric value, e.g., 5, 10) | |
| 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 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behaviors like adjusting thoughts, revising, branching, and hypothesis verification. No annotations present, so description carries full burden and does well, though could mention side effects or limits.
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 very long with some redundancy (e.g., repeated emphasis on depth). Structured with sections, but could be more concise while retaining clarity.
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 9 parameters, no output schema, and no annotations, the description is thoroughly complete, covering all aspects needed for correct invocation.
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%, and description adds extensive context for each parameter, explaining usage patterns, revision, branching, etc., beyond the schema definitions.
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 defines the tool as for deep, extensive, and dynamic problem-solving, specifying it for complex problems, research, and multi-step analysis. It is distinct and unambiguous.
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?
Provides a dedicated 'When to use this tool' section listing many appropriate scenarios, but does not explicitly mention when not to use or alternatives.
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
v1.0.0- First observed
sequentialthinking
TDQS
Only one tool exists, so there is no ambiguity. The tool 'sequentialthinking' has a unique and clear purpose focused on deep reasoning.
With a single tool, naming consistency is perfect. The name 'sequentialthinking' follows a clear camelCase convention and accurately describes its function.
One tool is borderline thin for a server named 'Long Reasoning MCP Server', as it lacks supporting tools for tasks like data retrieval or output formatting. However, the single tool is highly specialized and well-documented.
The tool extensively covers the reasoning lifecycle with features like branching, revision, and hypothesis verification. Minor gaps exist in areas like non-sequential reasoning or integration with external data, but overall it is comprehensive for its domain.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Agent-to-agent reasoning-as-a-service: chain-of-thought, analysis, and decision support.
- LiminalityOAuthai.physea
Breaks a hard question or decision into checkable sub-questions, grounds each to a real tool.
1 Deterministic reasoning stack for AI agents: simulate, decide & compute, plus cross-domain tools.
AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.
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- 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
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