Observed Signal · May 9, 2026 · Technical Release · Source: DEV Community · Impact: 3/5 · Sentiment: Positive
Open-source FIVE: Input-Filtering Engine for LLM Characters
The author released FIVE, an open-source MCP server and constraint-generation service designed to improve character consistency for LLM-powered personas by filtering and classifying user input before it reaches the model. FIVE produces a structured JSON
Open-source technical release offering a novel input-filtering constraint engine for LLM characters that integrates with MCP; useful to developers building persistent conversational personas and agents but not a major platform policy change.
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Key Takeaways & Evidence Grounding
- FIVE is an open-source MCP server and harness (repos: github.com/kiro0x/five-character-engine and github.com/kiro0x/five-mcp) published under an MIT license.
- FIVE generates JSON personality constraints from a 4-question form; the API charges $1 per call for constraint generation and returns JSON you can paste into system prompts.
- The project integrates with the Model Context Protocol (MCP) and is listed as io.github.kiro0x/five-mcp in the MCP Registry; it is also available on PyPI as five-mcp.
- FIVE's approach filters input (a three-stage harness: keyword scan, LLM classification fallback, strength-aware gate transform) rather than moderating output; the constraint JSON typically adds ~700 tokens to a system prompt.
- FIVE is compatible with multiple LLMs that accept JSON in system prompts (examples named: Claude, GPT-family, Llama, Mistral, Gemini) and is positioned for use in long-running characters such as game NPCs, customer personas, and roleplay companions.
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