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harshpreet931

Long Reasoning MCP Server

sequentialthinking

Break down complex problems into sequential steps for deep analysis, allowing revision and branching as understanding evolves to reach comprehensive solutions.

Instructions

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:

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

  2. Feel free to question or revise previous thoughts extensively

  3. Don't hesitate to add more thoughts if needed, even at the "end" - research has no artificial limits

  4. Express uncertainty when present and explore it deeply

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

  6. Ignore information that is irrelevant to the current step

  7. Generate MULTIPLE solution hypotheses when appropriate, not just one

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

  9. Repeat the hypothesis-verification cycle multiple times for robustness

  10. Explore edge cases, counterexamples, and alternative interpretations

  11. For deep research: aim for 100+ thoughts, exploring breadth AND depth

  12. Use branching extensively to explore alternative paths in parallel

  13. Perform multiple revision passes to refine understanding

  14. Only set next_thought_needed to false when truly done with COMPREHENSIVE analysis

  15. Provide a single, well-researched, thoroughly verified answer as the final output

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
thoughtYesYour current thinking step
nextThoughtNeededYesWhether another thought step is needed
thoughtNumberYesCurrent thought number (numeric value, e.g., 1, 2, 3)
totalThoughtsYesEstimated total thoughts needed (numeric value, e.g., 5, 10)
isRevisionNoWhether this revises previous thinking
revisesThoughtNoWhich thought is being reconsidered
branchFromThoughtNoBranching point thought number
branchIdNoBranch identifier
needsMoreThoughtsNoIf more thoughts are needed
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 thoroughly describes how the tool operates: it allows dynamic adjustment of thoughts, revision of previous insights, branching, hypothesis generation and verification, and iterative cycles. It specifies that there are 'no artificial limits' and details the process flow, including when to mark thoughts as revisions or branches, making the behavioral expectations clear and comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is excessively long and repetitive, with multiple sections (e.g., 'IMPORTANT,' 'When to use this tool,' 'Key features,' 'Parameters explained,' 'You should') that overlap in content. Sentences like 'Go as deep as needed - there is no maximum limit' and 'Explore edge cases, counterexamples, and alternative interpretations' could be condensed. It lacks front-loading of critical information, reducing efficiency.

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 tool's complexity (9 parameters, no annotations, no output schema), the description is highly complete. It explains the tool's purpose, usage guidelines, behavioral traits, parameter meanings, and process flow in detail. It compensates for the lack of structured fields by providing thorough context, ensuring an agent can understand how to invoke and use the tool effectively without gaps.

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

Parameters4/5

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

Schema description coverage is 100%, so the schema already documents all parameters. The description adds significant semantic context beyond the schema by explaining what each parameter represents in practice (e.g., 'thought' can include 'Revisions of previous thoughts' or 'Hypothesis generation'), providing examples and clarifying how parameters interact in the thinking process. This elevates it above the baseline of 3.

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 is for 'DEEP, EXTENSIVE, and dynamic problem-solving through extended thinking' and 'MAXIMUM DEPTH research and analysis.' It specifies the verb (problem-solving/research/analysis) and resource (thinking process), though without sibling tools to differentiate from, it can't achieve full distinction. The purpose is specific but not compared to alternatives.

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 guidance on when to use this tool, listing 10 specific scenarios (e.g., 'Breaking down complex problems into steps,' 'Deep research requiring extensive exploration'). It includes quantitative recommendations ('50+ thoughts minimum for complex problems, 100+ for deep research') and qualitative advice ('Take time to explore multiple angles'), offering comprehensive usage context without misleading information.

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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