Skip to main content
Glama

five-mcp

FIVE Character Engine 用のMCPサーバー — 一貫したキャラクターボイスを実現するためのJSON形式の性格・スタイル制約を生成するLLM制約エンジンです。

クイックスタート

インストール

pip install five-mcp

設定

APIキーを環境変数として設定します:

export FIVE_API_KEY=five_sk_your_key_here

キーは fiveengine.dev で取得してください。

Claude Desktopでの使用

claude_desktop_config.json に以下を追加します:

{
  "mcpServers": {
    "five-character-engine": {
      "command": "five-mcp",
      "env": {
        "FIVE_API_KEY": "five_sk_your_key_here"
      }
    }
  }
}

その他のMCPクライアントでの使用

MCP互換クライアントであれば、stdioトランスポート経由で接続可能です:

five-mcp

Related MCP server: CSL-Core

ツール: generate

FIVEエンジンを使用してキャラクター制約を生成します。

パラメータ

パラメータ

型

必須

説明

character_name

string

はい

キャラクター名

q1 – q4

A / B / C / D

はい

性格軸の選択

s1 – s4

1–5

いいえ

スタイル調整スライダー (デフォルト: 3)

free_text

string

いいえ

自由記述

レスポンス

{
  "status": "ok",
  "remaining": 42,
  "constraint": { "..." }
}

料金

generate の呼び出し1回につき $1 (1クレジット) が消費されます。クレジットの管理は fiveengine.dev で行ってください。

リンク

ライセンス

MIT

Available Tools

1 tool
generateA

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).

ParametersJSON Schema
NameRequiredDescriptionDefault
character_nameYes
q1Yes
q2Yes
q3Yes
q4Yes
s1No
s2No
s3No
s4No
free_textNo

TDQS

A3.7/5.0
Behavior3/5

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.

Conciseness4/5

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.

Completeness4/5

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.

Parameters4/5

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.

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: '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.

Usage Guidelines2/5

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.1.2
    • First observedgenerate

TDQS

A3.8/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion between tools. The single 'generate' tool has a clearly distinct purpose.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness4/5

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.

Maintenance

ActivityMaintained
ResponsivenessSyncing

Related MCP Connectors

Related MCP Servers

  • A
    license
    B
    quality
    D
    maintenance
    Provides a universal AI personality layer that uses a scientifically-backed Big Five engine to apply consistent character traits and brand voices across MCP-compatible platforms. It allows users to inject custom personality profiles or presets into AI interactions to ensure behavioral consistency.
    7
    15 npm
    76
    MIT
  • A
    license
    A
    quality
    A
    maintenance
    Deterministic AI safety policy engine with Z3 formal verification. Write, verify, simulate, and enforce machine-verifiable safety constraints for AI agents. Completely outside the LLM.
    11
    18
    Apache 2.0