I recently stumbled across a fantastic MCP server from Anthropic that forces a more methodical approach to problem-solving.
Instead of trying to solve everything in one shot (and often missing crucial details), the Anthropic sequential thinking MCP server forces a more methodical approach.
> /mcp
╭──────────────────────────────────────────────────────────────────────────────╮│ sequentialthinking (sequential-thinking) ││ ││ Tool name: sequentialthinking ││ Full name: mcp__sequential-thinking__sequentialthinking ││ ││ Description: ││ 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 ││ - thought_number: Current number in sequence ││ - total_thoughts: Current estimate of thoughts needed (adjustable) ││ - is_revision: Boolean indicating if this revises previous thinking ││ - revises_thought: Which thought number is being reconsidered ││ - branch_from_thought: Branching point thought number ││ - branch_id: Identifier for the current branch ││ - needs_more_thoughts: If reaching end but realizing more thoughts needed ││ ││ You should: ││ 1. Start with an initial estimate of needed thoughts, be ready to adjust ││ 2. Feel free to question or revise previous thoughts ││ 3. Don't hesitate to add more thoughts if needed, even at the "end" ││ 4. Express uncertainty when present ││ 5. Mark thoughts that revise previous thinking or branch into new paths ││ 6. Ignore information that is irrelevant to the current step ││ 7. Generate a solution hypothesis when appropriate ││ 8. Verify the hypothesis based on the Chain of Thought steps ││ 9. Repeat the process until satisfied with the solution ││ 10. Provide a single, ideally correct answer as the final output ││ 11. Only set next_thought_needed to false when truly done ││ ││ Parameters: ││ • thought (required): string - Your current thinking step ││ • nextThoughtNeeded (required): boolean - Whether another step is needed ││ • thoughtNumber (required): integer - Current thought number ││ • totalThoughts (required): integer - Estimated total thoughts needed ││ • isRevision: boolean - Whether this revises previous thinking ││ • revisesThought: integer - Which thought is being reconsidered ││ • branchFromThought: integer - Branching point thought number ││ • branchId: string - Branch identifier ││ • needsMoreThoughts: boolean - If more thoughts are needed │╰──────────────────────────────────────────────────────────────────────────────╯When you ask an AI to refactor a complex system, Sequential Thinking approaches it differently:
# Instead of: "Here's your refactored code!"# It does:# 1. Map out current architecture# 2. Identify pain points# 3. Propose alternatives# 4. Consider trade-offs# 5. Then implementSuper useful for architectural decisions, complex debugging, or any task where you’d normally grab a whiteboard. If you’re building anything complex with AI, this is worth checking out.