MCP server for the FIVE Persona Engine — an LLM persona constraint engine that generates structured JSON constraints to eliminate persona drift. Instead of describing personality in words (which LLMs interpret differently each turn), FIVE defines behavioral parameters the LLM executes as a recipe. See how it works →
Measured: with the constraint JSON + free harness, the demo character survived a 120-turn pressure test with zero persona breaks (plain prompt: 8 breaks; JSON alone: 1). Numbers, transcripts and scripts →
Free — no API key, no account.
pip install five-mcpAdd to your claude_desktop_config.json:
{
"mcpServers": {
"five-character-engine": {
"command": "five-mcp"
}
}
}That's it — no environment variables needed.
Any MCP-compatible client can connect via stdio transport:
five-mcpRelated MCP server: CSL-Core
generateGenerates persona constraints via the FIVE engine.
Parameter | Type | Required | Description |
| string | Yes | Name of the character |
| A / B / C / D | Yes | Personality axis choices |
| 1–5 | No | Style sliders (default: 3) |
| string | No | Free-form description |
{
"status": "ok",
"constraint": { "..." }
}The API is free. To keep it available to everyone, a light per-IP limit applies: 10 requests/min, 200 requests/day. The free host may cold-start, so the first call can take up to a minute.
API & Docs: fiveengine.dev
GitHub: github.com/kiro0x/five-mcp
Design philosophy & examples: five-character-engine README
Measured evaluation (120-turn drift test): five-character-engine eval/
Engine repo: github.com/kiro0x/five-character-engine
MIT
mcp-name: io.github.kiro0x/five-mcp
Generate persona constraints using the FIVE engine.
This tool calls the FIVE Persona Engine API to produce JSON constraints that prevent persona drift and keep an LLM character's voice consistent.
Each call costs $1 and consumes one credit from your account.
Args: character_name: Name of the character to generate constraints for. q1: Personality axis 1 – choose A, B, C, or D. q2: Personality axis 2 – choose A, B, C, or D. q3: Personality axis 3 – choose A, B, C, or D. q4: Personality axis 4 – choose A, B, C, or D. s1: Style slider 1 (1-5, default 3). Optional fine-tuning. s2: Style slider 2 (1-5, default 3). Optional fine-tuning. s3: Style slider 3 (1-5, default 3). Optional fine-tuning. s4: Style slider 4 (1-5, default 3). Optional fine-tuning. free_text: Optional free-form description to further guide generation.
Returns: A dict with keys: status, remaining (credits left), constraint (the generated JSON constraint object).
| Name | Required | Description | Default |
|---|---|---|---|
| character_name | Yes | ||
| q1 | Yes | ||
| q2 | Yes | ||
| q3 | Yes | ||
| q4 | Yes | ||
| s1 | No | ||
| s2 | No | ||
| s3 | No | ||
| s4 | No | ||
| free_text | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses cost per call and credit consumption, which is helpful. However, it lacks details on idempotency, side effects, or rate limits. The return format is described, but behavioral transparency is not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured, starting with a one-line summary, then engine explanation, cost, and a clear parameter list. While the style slider descriptions are repetitive, the overall structure is logical and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of 10 parameters, no output schema, and no annotations, the description covers the essential aspects: purpose, inputs, output format, and cost. It is sufficiently complete for an agent to invoke the tool correctly, though additional behavioral details would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description adds significant value by explaining each parameter: character_name, q1-q4 (enum selections), s1-s4 (integer ranges with defaults), and free_text. It clarifies the purpose of optional fields and provides defaults, compensating for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate persona constraints using the FIVE engine.' It explains the specific API and output format, leaving no ambiguity about what the tool does. With no sibling tools, distinction is not applicable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives or prerequisites. It simply describes the function without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.2generateWith only one tool, there is no possibility of confusion between tools. The single 'generate' tool has a clearly distinct purpose.
The tool name 'generate' is a single verb, which is clear and follows a common convention. While there is no noun to form a verb_noun pattern, the name is consistent as the only tool.
The server has only one tool, which feels thin for a general-purpose utility. However, for a very focused single-API function, it is borderline acceptable.
The tool covers the core functionality of generating persona constraints with many parameters. Minor gaps exist (e.g., no credit management or constraint listing), but the tool is complete for its stated purpose.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Jailbreak-proof AI guardrails. Automated Reasoning SMT solver, not an LLM. ZK proofs included.
Personality tuning files for AI agents: 43 MIT-licensed tunings + 5 inline personality tests.
A personal RAG database you build from chat, so AI creates work that sounds like you.
Stop re-explaining yourself to AI. Save knowledge once, use it in every conversation.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/kiro0x/five-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
