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manasp21

Event Horizon MCP Server

by manasp21

start_evolution

Initialize an evolutionary solution system to generate and optimize solutions across multiple generations using genetic algorithm principles with consistency check evaluations.

Instructions

Initialize an evolutionary solution system for a given problem with consistency checks.

This tool sets up the framework for evolving solutions across multiple generations. The LLM will:

  1. Generate initial population of solutions

  2. Evaluate solutions against consistency checks

  3. Use crossover recommendations to create better solutions

  4. Repeat until convergence or max generations

Use this tool to begin the evolutionary process with a clear problem statement and evaluation criteria.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
problemStatementYesThe problem or challenge to solve
consistencyChecksYesArray of consistency checks (strings or objects with description and weight)
populationSizeNoNumber of solutions per generation (default: 3)
maxGenerationsNoMaximum number of generations (default: 5)
convergenceThresholdNoScore threshold for convergence (default: 0.95)
Behavior3/5

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

With no annotations provided, the description carries the full burden. It explains the multi-step process (generate, evaluate, crossover, repeat) which is valuable behavioral context. However, it doesn't disclose important traits like whether this is a long-running operation, what happens if interrupted, or what permissions might be required.

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

Conciseness4/5

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

The description is appropriately sized with 5 sentences. It's front-loaded with the core purpose, then details the process, and ends with usage guidance. The bullet-point style for the process steps is efficient, though the final sentence could be more concise.

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

Completeness3/5

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

For a complex initialization tool with no annotations and no output schema, the description provides good process context but lacks details about what the tool returns, error conditions, or system requirements. It covers the 'what' and 'why' adequately but misses some operational details an agent would need.

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 description coverage is 100%, so the baseline is 3. The description mentions 'problem statement and evaluation criteria' which maps to the required parameters, but doesn't add significant meaning beyond what the schema already provides about each parameter's purpose.

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 states the tool's purpose: 'Initialize an evolutionary solution system for a given problem with consistency checks.' It specifies the verb ('Initialize'), resource ('evolutionary solution system'), and distinguishes from siblings by focusing on setup rather than ongoing operations like add_solution or evolve_generation.

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 clear context for when to use this tool: 'Use this tool to begin the evolutionary process with a clear problem statement and evaluation criteria.' It implies this is the starting point, but doesn't explicitly state when NOT to use it or name specific alternatives among the siblings.

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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