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olaservo

Shannon Thinking MCP Server

shannonthinking

Solve complex problems systematically by breaking them down into iterative steps: define, model, validate, and implement, revising as understanding evolves.

Instructions

A problem-solving tool inspired by Claude Shannon's systematic and iterative approach to complex problems.

This tool helps break down problems using Shannon's methodology of problem definition, mathematical modeling, validation, and practical implementation.

When to use this tool:

  • Complex system analysis

  • Information processing problems

  • Engineering design challenges

  • Problems requiring theoretical frameworks

  • Optimization problems

  • Systems requiring practical implementation

  • Problems that need iterative refinement

  • Cases where experimental validation complements theory

Key features:

  • Systematic progression through problem definition → constraints → modeling → validation → implementation

  • Support for revising earlier steps as understanding evolves

  • Ability to mark steps for re-examination with new information

  • Experimental validation alongside formal proofs

  • Explicit tracking of assumptions and dependencies

  • Confidence levels for each step

  • Rich feedback and validation results

Parameters explained:

  • thoughtType: Type of thinking step (PROBLEM_DEFINITION, CONSTRAINTS, MODEL, PROOF, IMPLEMENTATION)

  • uncertainty: Confidence level in the current thought (0-1)

  • dependencies: Which previous thoughts this builds upon

  • assumptions: Explicit listing of assumptions made

  • isRevision: Whether this revises an earlier thought

  • revisesThought: Which thought is being revised

  • recheckStep: For marking steps that need re-examination

  • proofElements: For formal validation steps

  • experimentalElements: For empirical validation

  • implementationNotes: For practical application steps

The tool supports an iterative approach:

  1. Define the problem's fundamental elements (revisable as understanding grows)

  2. Identify system constraints and limitations (can be rechecked with new information)

  3. Develop mathematical/theoretical models

  4. Validate through proofs and/or experimental testing

  5. Design and test practical implementations

Each thought can build on, revise, or re-examine previous steps, creating a flexible yet rigorous problem-solving framework.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
thoughtYesYour current thinking step
isRevisionNoWhether this thought revises an earlier one
assumptionsYesExplicit list of assumptions
recheckStepNoFor marking steps that need re-examination
thoughtTypeYesType of thinking step
uncertaintyYesConfidence level (0-1)
dependenciesYesThought numbers this builds upon
proofElementsNoElements required for formal proof steps
thoughtNumberYesCurrent thought number
totalThoughtsYesEstimated total thoughts needed
revisesThoughtNoThe thought number being revised
nextThoughtNeededYesWhether another thought step is needed
implementationNotesNoNotes for practical implementation steps
experimentalElementsNoElements for experimental validation
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full responsibility for disclosing behavior. It thoroughly explains the iterative nature, support for revisions, re-examination, and tracking of assumptions and confidence levels. It leaves little ambiguity about how the tool operates.

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

Conciseness3/5

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

The description is well-structured with clear sections but is somewhat verbose, especially the 'Key features' and iterative process parts which are partially redundant with the 'Parameters explained' and usage guidelines. It could be more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (14 parameters, nested objects) and no output schema, the description is reasonably complete. It explains the methodology, parameter purposes, and iterative workflow. However, it does not specify what the tool returns or how errors are handled.

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

Parameters3/5

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

Schema coverage is 100%, so the baseline is 3. The description's 'Parameters explained' section reiterates schema descriptions, adding some context (e.g., 'Which previous thoughts this builds upon') but does not provide significant new meaning beyond what the schema already states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly defines the tool as a problem-solving tool inspired by Claude Shannon's systematic approach. It explicitly states the verb (break down problems) and resource (Shannon's methodology), and lists specific use cases, making its purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides an extensive list of when to use the tool, covering complex systems, engineering, optimization, etc. It also outlines the iterative process and key features. However, it does not explicitly state when not to use the tool or suggest alternatives, but given no siblings, this is acceptable.

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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