Hejdar용 MCP 서버 — AI 에이전트를 위한 런타임 정책 시행 도구입니다.
이 서버는 hejdar_evaluate를 MCP 도구로 노출합니다. MCP 호환 에이전트(Claude, ChatGPT, Cursor, 커스텀 등)는 작업을 실행하기 전에 해당 작업이 조직 정책에 따라 허용되는지 확인하기 위해 이 도구를 호출할 수 있습니다.
이 MCP 서버는 Hejdar API(POST /v1/evaluate)를 감싸는 얇은 래퍼입니다. 자체적인 정책 로직은 포함되어 있지 않으며, 모든 결정은 Hejdar 조직에 구성된 정책에 따라 이루어집니다.
pip install hejdar-mcp또는 uvx를 사용하여 직접 실행하세요:
uvx hejdar-mcpapp.hejdar.com에 가입하고 Settings → API Keys에서 API 키를 생성하세요.
Claude Desktop 설정 파일(~/Library/Application Support/Claude/claude_desktop_config.json (macOS), %APPDATA%\Claude\claude_desktop_config.json (Windows))에 추가하세요:
{
"mcpServers": {
"hejdar": {
"command": "uvx",
"args": ["hejdar-mcp"],
"env": {
"HEJDAR_API_KEY": "hejdar_sk_your_key_here"
}
}
}
}Claude Code MCP 설정에 추가하세요:
{
"mcpServers": {
"hejdar": {
"command": "uvx",
"args": ["hejdar-mcp"],
"env": {
"HEJDAR_API_KEY": "hejdar_sk_your_key_here"
}
}
}
}export HEJDAR_API_KEY=hejdar_sk_your_key_here
hejdar-mcpRelated MCP server: Aegis MCP Server
설치: pip install hejdar-mcp 또는 uvx hejdar-mcp
API 키 발급 — hello@hejdar.com으로 문의하거나 hejdar.com을 방문하세요.
MCP 클라이언트 구성 (위의 구성 예시 참조)
hejdar_evaluate조직의 보안 정책에 따라 에이전트 작업을 평가합니다.
입력:
매개변수 | 유형 | 필수 | 설명 |
| string | 예 |
|
| string | 예 | 대상 리소스 (예: |
| string | 아니오 | 호출하는 에이전트 이름 (예: |
| object | 아니오 | 자유 형식 메타데이터 (부서, user_id, 사유 등) |
출력:
{
"decision": "DENY",
"policy_id": "pol_abc123",
"reason": "Deletion of customer data requires manager approval",
"risk_level": "HIGH"
}decision은 ALLOW, DENY, WOULD_DENY 중 하나입니다.
최상의 결과를 얻으려면 에이전트의 시스템 프롬프트에 다음을 추가하세요:
You have access to the hejdar_evaluate tool. Before performing any action
that reads, writes, deletes, transfers data, or executes commands on
external systems, you MUST call hejdar_evaluate first.
If hejdar_evaluate returns DENY or WOULD_DENY, do NOT proceed with the
action. Instead, inform the user that the action was blocked by policy
and include the reason provided.변수 | 필수 | 기본값 | 설명 |
| 예 | — | Hejdar API 키 |
| 아니오 |
| API 기본 URL (자체 호스팅용) |
API 키는 환경 변수에서만 읽어오며, 하드코딩되거나 도구 I/O에 노출되지 않습니다.
모든 입력값은 API로 전달되기 전에 검증 및 정제됩니다.
오류 응답은 내부 세부 정보, API 키 또는 스택 추적을 유출하지 않습니다.
모든 API 호출은 TLS를 강제합니다.
git clone https://github.com/ARKALDA/hejdar-mcp.git
cd hejdar-mcp
pip install -e ".[dev]"
pytestMIT
Evaluate an AI agent action against Hejdar security policies BEFORE executing it. Returns ALLOW, DENY, or WOULD_DENY. Call this before any sensitive action (read, write, delete, transfer, execute) to check if the action is permitted by organizational policy. If the decision is DENY, do NOT execute the action.
| Name | Required | Description | Default |
|---|---|---|---|
| action_type | Yes | The type of action the agent intends to perform | |
| resource | Yes | The target resource or system the action applies to, e.g. 'customer_database', 'employee_records', 'email_system' | |
| agent_name | No | Name identifying this agent, e.g. 'hr-assistant', 'finance-bot' | |
| context | No | Optional metadata about the action — department, user_id, reason, data_classification, etc. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it performs a pre-execution security evaluation, returns one of three policy decisions, and has a critical safety implication (preventing execution on DENY). It doesn't mention rate limits, authentication needs, or error handling, but covers the core operational behavior well.
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 perfectly structured and concise. The first sentence establishes the core purpose and output. The second sentence provides critical usage guidelines. The third sentence delivers an essential safety instruction. Every sentence earns its place with no wasted words, and the most important information (what it does and when to use it) is front-loaded.
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?
For a security evaluation tool with no annotations and no output schema, the description provides excellent context about its purpose, usage, and behavioral implications. It doesn't describe the return format details (what ALLOW/DENY/WOULD_DENY responses contain) or potential error cases, but covers the essential operational context sufficiently given the tool's critical safety role.
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?
The schema has 100% description coverage, so the baseline is 3. The tool description doesn't add any parameter-specific information beyond what's already documented in the schema (action_type, resource, agent_name, context). It mentions these parameters implicitly through examples ('read, write, delete, transfer, execute') but provides no additional semantic context.
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 with specific verbs ('evaluate an AI agent action against Hejdar security policies') and resources ('security policies'), and explicitly distinguishes its role as a pre-execution check. It identifies the exact function (policy evaluation) and output (ALLOW, DENY, WOULD_DENY), leaving no ambiguity about what this tool does.
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 explicit guidance on when to use this tool ('before any sensitive action') and what to do based on the outcome ('if the decision is DENY, do NOT execute the action'). It lists specific action types (read, write, delete, transfer, execute) that should trigger its use, offering clear operational instructions despite no sibling tools for comparison.
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.
v0.1.0hejdar_evaluateScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined as evaluating AI agent actions against security policies, making it distinct and unambiguous in isolation.
A single tool inherently has perfect naming consistency since there are no other tools to compare against. The name 'hejdar_evaluate' follows a clear pattern of server prefix and action, which would be consistent if more tools existed.
A single tool is too few for a server that claims to handle security policy evaluation across various actions (read, write, delete, transfer, execute). This minimal set forces agents to rely solely on this one tool without dedicated tools for different policy aspects or actions, making the scope feel incomplete and thin.
The server's domain appears to be security policy evaluation for AI actions, but with only one tool, there are significant gaps. It lacks tools for managing policies, querying specific rules, or handling different types of security checks, which limits agents to a single evaluation call without supporting operations for a comprehensive workflow.
Deterministic runtime safety for AI agents: scan PII, gate tool actions, verify LLM output.
Security gateway for AI agents: policy, approval, and audited execution, no secrets shared.
See, price, and control every tool call your AI agents make: policy checks, cost, and audit tools.
Zero-trust gateway for AI agents: score tool calls, verify agent cards, enforce policy, audit.
