de en es ja ko ru zh

MCP Airlock

Name: MCP Airlock
Author: lara-muhanna

by lara-muhanna

Overview Schema Source Related Servers Discussions Admin

Python

Remote

MCP Airlock

License: MIT Python

用于 MCP 工具的零信任安全网关。 MCP Airlock 将每一次工具调用转化为具有防篡改来源证明的、短期且上下文绑定的能力决策。

为什么存在这个项目

智能体工具生态系统在一个痛苦的边界上失败了：从不可信的提示词文本到特权工具执行的跨越。

当前的模式通常是以下之一：

静态允许列表（智能体可以调用工具 X）
弱正则表达式过滤
没有完整性保证的事后日志

当提示词注入在会话期间改变意图时，它们就会失效，从而导致静默的权限提升或数据泄露。

MCP Airlock 通过为 MCP 提供一个缺失的原语解决了这个问题：

能力租约 (Capability Leases)：短期、已签名、上下文绑定的权限（会话 + 意图 + 工具范围 + 约束）
上下文感知策略：每次调用时的动态授权（风险评分 + 工具约束 + 租约检查）
防篡改来源证明：所有允许/拒绝决策的仅追加哈希链

Related MCP server: evav-gateway

核心创新

上下文绑定能力租约 (CBCL)

每个工具调用都根据已签名的租约进行授权：

绑定到 session_id
绑定到 intent_hash
限定于特定工具
有时间限制
可选约束（例如：允许的域名、最大风险值）

如果提示词注入试图改变意图或跳出工具范围，执行将被拒绝。

架构

flowchart LR
    A[Agent / MCP Client] -->|tools/call| B[MCP Airlock Server]
    B --> C[Risk Engine]
    B --> D[Capability Verifier]
    B --> E[Policy Engine]
    E -->|allow| F[Tool Adapter Layer]
    E -->|deny| G[Policy Deny Response]
    F --> H[External APIs / Internal Services]
    B --> I[Provenance Ledger Hash Chain]

信任边界

flowchart TB
    subgraph Untrusted
      U1[Prompt Content]
      U2[Agent Reasoning Trace]
    end

    subgraph Trusted Control Plane
      T1[MCP Airlock]
      T2[Policy + Lease Validation]
      T3[Signed Provenance Ledger]
    end

    subgraph External Targets
      X1[Public APIs]
      X2[Internal APIs]
    end

    U1 --> T1
    U2 --> T1
    T1 --> T2
    T2 --> X1
    T2 --> X2
    T1 --> T3

关键特性

与 initialize、tools/list、tools/call 兼容的 MCP stdio 服务器
能力颁发工具：airlock_issue_capability
智能体/API 使用分析工具：airlock_usage_stats
API 暴露度测量工具：airlock_exposure_report
具有各工具风险阈值的策略执行中间件
提示词注入签名评分
抗 SSRF 的 HTTP 工具适配器 (http_get_json)
真实 API 集成示例 (weather_hourly)
防篡改来源日志 + 验证命令
智能体 API 安全的沙箱加固指南
用于服务/演示/颁发/验证/统计/暴露度的 CLI

2 分钟快速入门

git clone https://github.com/lara-muhanna/mcp-airlock
cd mcp-airlock
python -m pip install -e .
python -m mcp_airlock --config examples/airlock.config.json demo

你将看到：

对人类友好的摘要（握手、能力、允许/拒绝、审计完整性）
被拒绝的恶意调用及其通俗易懂的原因
已签名的来源证明

单命令本地演示（无需安装）

python -m mcp_airlock --config examples/airlock.config.json demo --city Austin --state Texas

演示期间的完整 JSON 负载：

python -m mcp_airlock --config examples/airlock.config.json demo --raw

作为 MCP 服务器运行

python -m mcp_airlock --config examples/airlock.config.json serve

MCP 客户端设置示例：

CLI

# Issue a capability directly
python -m mcp_airlock --config examples/airlock.config.json issue \
  --session-id sess-123 \
  --subject agent:planner \
  --tools weather_hourly,http_get_json \
  --intent "Plan safe outdoor activities" \
  --ttl-seconds 900 \
  --constraints '{"allowed_domains":["api.open-meteo.com"],"max_risk":0.6}'

# Verify audit integrity
python -m mcp_airlock --config examples/airlock.config.json verify-log

# API usage stats by agent
python -m mcp_airlock --config examples/airlock.config.json stats --lookback-hours 24

# API exposure measurement
python -m mcp_airlock --config examples/airlock.config.json exposure --lookback-hours 24

智能体集成示例

运行：

python examples/agent_integration.py

此脚本：

通过 stdio 启动 Airlock
协商 MCP 初始化/列表
颁发租约
运行正常的工具调用
运行被拦截的注入调用

配置模板

examples/airlock.config.json

{
  "secret_key": "dev-secret-change-this-before-production",
  "provenance_log": "./airlock-provenance.log",
  "max_ttl_seconds": 1800,
  "default_risk_threshold": 0.55,
  "tools": {
    "weather_hourly": {
      "require_capability": true,
      "risk_threshold": 0.7
    },
    "http_get_json": {
      "require_capability": true,
      "risk_threshold": 0.45,
      "allowed_domains": ["api.open-meteo.com", "geocoding-api.open-meteo.com"]
    }
  }
}

安全模型摘要

智能体通过 airlock_issue_capability 请求租约。
租约经过 HMAC 签名，包含 session、intent_hash、tool_scope、expiry。
每个 tools/call 请求都包含 _capability 和 _context。
Airlock 执行：
- 租约有效性 + 签名
- 会话和意图的连续性
- 风险阈值
- 特定工具的约束（例如：域名允许列表）
决策 + 证据被哈希链接到来源日志中。

项目结构

mcp-airlock/
  mcp_airlock/
    cli.py
    server.py
    policy.py
    capability.py
    risk.py
    provenance.py
    config.py
    tool_ids.py
    tools/
      http_json.py
      weather.py
  examples/
    airlock.config.json
    agent_integration.py
  docs/
    CLIENT_SETUP.md
    SANDBOXING_AGENTIC_APIS.md

路线图

上游 MCP 代理模式（透明地包装现有的 MCP 服务器）
OPA/Rego 策略后端
OpenTelemetry 追踪 + SIEM 接收器
托管能力代理 + 密钥轮换
用于事件响应的已签名重放包

社区

贡献指南：CONTRIBUTING.md
安全策略：SECURITY.md
行为准则：CODE_OF_CONDUCT.md
发布检查清单：docs/RELEASE_CHECKLIST.md

许可证

MIT

Available Tools

4 tools

airlock_audit_tailC

Read recent signed provenance events.

ParametersJSON Schema

Name	Required	Description	Default
`limit`	No

TDQS

C2.6/5.0

Behavior2/5

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. While it notes events are 'recent' and 'signed', it fails to specify the time window for 'recent', result ordering, return format, pagination behavior, or the implications of 'signed' (verification requirements?). This leaves critical behavioral gaps for an audit tool.

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 extremely concise at five words, immediately front-loading the verb and object. While efficient, this brevity is arguably inappropriate given the complete absence of annotations and schema descriptions, leaving the description too terse to stand alone as documentation.

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

Completeness2/5

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

For a security/audit tool handling cryptographically signed provenance data, the description is inadequate. With no output schema, no parameter descriptions, and no annotations, the description should explain what data structure is returned and what 'airlock' provenance tracks, but it provides none of this context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage for the 'limit' parameter. The description completely fails to compensate by explaining the parameter's purpose, valid ranges, or default behavior (20). The agent has no textual guidance on how to use the only available parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Read') and the specific resource ('signed provenance events'), distinguishing it from the sibling 'airlock_issue_capability' (which issues/writes) and the unrelated 'weather_hourly' and 'http_get_json' tools. However, it could better clarify what constitutes a 'provenance event' in this specific 'airlock' domain.

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, nor does it mention prerequisites (e.g., specific permissions needed to read signed audit trails) or when not to use it. The agent receives no signals about appropriate usage contexts.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

airlock_issue_capabilityC

Issue a short-lived capability lease bound to session+intent.

ParametersJSON Schema

Name	Required	Description	Default
`session_id`	Yes
`subject`	Yes
`tools`	Yes
`ttl_seconds`	No
`intent`	Yes
`constraints`	No

TDQS

C2.4/5.0

Behavior2/5

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

Lacking annotations, the description only discloses the 'short-lived' nature (relating to ttl_seconds) but omits critical behavioral details: what authorization the lease grants, how the constraints object limits usage, side effects of issuance, or security implications.

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?

Single dense sentence with no filler words; information is front-loaded. However, extreme brevity becomes a liability given the complete absence of schema documentation and annotations.

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

Completeness2/5

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

Inadequate for a security-sensitive tool with 6 parameters (including a nested constraints object). Fails to explain the capability model, what the lease authorizes, or the purpose of required fields like 'subject', creating operational risk.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description partially compensates by implying session_id, intent, and ttl_seconds via 'bound to session+intent' and 'short-lived', but leaves 'subject', 'tools', and the nested 'constraints' object completely unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

States the core action (Issue) and resource (capability lease) with binding context (session+intent), but uses domain jargon without explanation and fails to differentiate from sibling 'airlock_audit_tail' or explain what the lease enables.

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?

Provides no guidance on when to issue capabilities versus using other tools, no security prerequisites, and no warnings about the sensitivity of granting tool access via leases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

http_get_jsonA

Fetch JSON from a public HTTPS API endpoint.

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	HTTPS URL to fetch.
`query`	No	Optional query parameters.

TDQS

A3.8/5.0

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 adds valuable behavioral context by specifying 'public' (implying no auth headers required) and 'JSON' (setting expectation for response parsing), but omits operational details like timeout behavior, redirect handling, or error responses for non-JSON content.

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

Conciseness5/5

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

The description is a single, dense sentence with zero waste. It front-loads the action and precisely qualifies the target resource type and protocol without filler text.

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 tool's simplicity (2 parameters, no output schema), the description adequately covers the essential contract. The 'public' qualifier is crucial for setting correct expectations, though mentioning error handling for non-JSON responses would strengthen completeness.

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%, with the schema fully documenting both 'url' and 'query' parameters. The description adds no additional parameter semantics beyond what the schema provides, meeting the baseline expectation for well-documented schemas.

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 provides a specific verb ('Fetch'), resource type ('JSON'), and scope ('public HTTPS API endpoint'), clearly positioning this as a generic external HTTP client distinct from domain-specific siblings like weather_hourly and airlock_audit_tail.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

While it lacks explicit 'when-to-use' statements, the description implies usage through the 'public HTTPS API' scope, suggesting external/unauthenticated endpoints versus the internal/domain-specific siblings. However, it does not explicitly direct users to alternatives like weather_hourly for weather data.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

weather_hourlyA

Resolve US city/state and fetch hourly weather from Open-Meteo.

ParametersJSON Schema

Name	Required	Description	Default
`city`	Yes
`state`	Yes
`hours`	No

TDQS

A3.7/5.0

Behavior3/5

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

With no annotations provided, the description carries the full disclosure burden. It successfully indicates the geocoding behavior ('Resolve') and data source ('Open-Meteo'), but omits critical behavioral details like error handling for invalid locations, output format, units (imperial/metric), or rate limits.

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

Conciseness5/5

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

The description is a single, dense sentence with zero redundancy. Every word contributes essential information (action, scope, data source), making it appropriately sized and front-loaded.

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?

Given the simple 3-parameter schema with primitive types and no output schema, the description adequately covers the core function. However, gaps remain: the 'hours' parameter is undocumented, and the absence of annotations or output schema leaves the return structure and units unspecified.

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 0%, requiring the description to compensate. It implicitly documents the 'city' and 'state' parameters by specifying they are 'US city/state', adding geographic context not present in the raw parameter names. However, it fails to mention the 'hours' parameter or its constraints (1-168, default 24).

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 uses specific verbs ('Resolve', 'fetch') and identifies the exact resource ('hourly weather'). It clearly distinguishes the tool from siblings (airlock_audit_tail, http_get_json) by specifying the weather domain and Open-Meteo data source.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies geographic constraints by specifying 'US city/state', hinting at usage boundaries. However, it lacks explicit guidance on when to use versus alternatives (e.g., for non-US locations) or prerequisites.

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.

4 tool updatesv0.1.0Apr 4, 2026
- First observedairlock_audit_tail
- First observedairlock_issue_capability
- First observedhttp_get_json
- First observedweather_hourly

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.9/5.0

Scored 2026-04-04 05:31 across 4 tools

Disambiguation4/5

Each tool targets a distinct function (provenance auditing, capability issuance, generic HTTP fetching, and weather retrieval) with minimal overlap. An agent can easily distinguish when to use each based on the task requirements.

Naming Consistency3/5

Mixed naming patterns: two tools use 'airlock_' prefix with verbs (issue, audit_tail), while others use 'http_' prefix or no prefix (weather_hourly). The weather tool breaks the verb-first convention used by others, using a noun-adjective pattern instead.

Tool Count3/5

Four tools is acceptably compact but the set suffers from scope confusion, mixing core Airlock security functions with unrelated utility tools (weather, HTTP). This feels like two different servers merged together rather than a cohesive toolset.

Completeness2/5

The Airlock domain (capability management) is severely underdeveloped, offering only issuance and audit tail reading without revocation, validation, or capability listing. The utility tools (weather, HTTP) are also minimal, offering only hourly forecasts and GET requests without parameter customization.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Related Servers

Related MCP Connectors

gatewayOAuth
ai.sealgate
MCP gateway with runtime security policy, tool-call-level control, and audit of agent actions.
Security Agent Orchestration
11
Singularity - MCP Discovery & Security Gateway AOAuth
io.github.gaurav-kumar-sinha-060705
Find, vet, and run MCP tools through a secure audited gateway with prompt-injection risk scoring
Security Developer Tools
11
IdentArk Gateway
io.identark
Zero-secret MCP gateway for AI agents: risk-scored, audited calls with human-in-the-loop approval.
Security Agent Orchestration
29
mcp-gatewayOAuth
io.github.titus-civic
Identity, authorization, audit trails, and revocable permissions for AI agents accessing MCP tools.
Security Autonomous Agents Agent Orchestration
2

Related MCP Servers

Security Guard MCP
Security Observability
marcojourney
F
license
Not graded
quality
D
maintenance
Enables secure interaction between LLMs and MCP tools by applying zero-trust security controls, including sensitive data masking, file system protection, and policy enforcement.
Updated 3 months ago (2026-06-18 06:28 UTC)
-
evav-gatewayofficial
Security Agent Orchestration
evavai
A
license
Not graded
quality
B
maintenance
Governed MCP gateway that lets AI agents call tools with policy enforcement, prompt-injection screening, a kill-switch, and tamper-evident signed audit logs.
Updated 2 months ago (2026-07-31 19:09 UTC)
Apache 2.0
mcp-permission-server
Security Observability
jigonyoo
A
license
Not graded
quality
B
maintenance
Enforces fine-grained, context-aware access control on MCP tool calls, with a tamper-evident, replayable audit log that records denials and verifies every decision.
Updated a month ago (2026-08-30 06:45 UTC)
MIT
MCP Tool Manager
Agent Orchestration Security
Nagendda
A
license
Not graded
quality
C
maintenance
Enables AI agents to securely discover, invoke, and manage tools through a hardened MCP endpoint with protections like injection detection, circuit breakers, retry backoff, response caching, context-window limiting, and state snapshots.
Updated a month ago (2026-08-26 11:50 UTC)
1 npm
MIT

MCP Airlock

为什么存在这个项目

核心创新

上下文绑定能力租约 (CBCL)

架构

信任边界

关键特性

2 分钟快速入门

单命令本地演示（无需安装）

作为 MCP 服务器运行

CLI

智能体集成示例

配置模板

安全模型摘要

项目结构

路线图

社区

许可证

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

evav-gatewayofficial