correctover-mcp-server

correctover-mcp-server

Enables protocol-level runtime verification of AI agent tool calls across six dimensions with a fail-closed guarantee, blocking any action that fails verification before execution.

Category
访问服务器

README

CCS Runtime Evidence MCP Server

A Model Context Protocol (MCP) server that brings CCS runtime verification to any MCP-compatible client — Claude Desktop, Cursor, Windsurf, and more.

It verifies AI agent tool calls at runtime, blocks unsafe ones by default, issues tamper-evident evidence records for every decision, and verifies that actual tool arguments match the agent's declared intent — catching cross-model parameter drift.

Runtime evidence layer, not a static scanner. Every decision is enforced at call time and produces independently verifiable cryptographic evidence.

Quick Start

npx -y ccs-mcp-server

MCP Client Configuration

{
  "mcpServers": {
    "ccs-runtime-evidence": {
      "command": "npx",
      "args": ["-y", "ccs-mcp-server"]
    }
  }
}

Zero dependencies. 44KB. Pure Node.js stdlib. No install scripts.

Tools

Tool Purpose
verify_tool_call 7-dimension runtime verification (Structure/Schema/Security/Identity/Integrity/Latency/Cost) + semantic attack-chain analysis + math overflow detection. Blocks by default.
issue_evidence Issue a tamper-evident evidence record (content_hash + evidence_hash, chainable). Produced for allowed AND denied calls.
audit_mcp_config Audit MCP configuration JSON for security risks.
verify_intent_binding (v1.1.0) Verify actual tool arguments match a declared intent — zero tolerance, zero LLM calls. Catches cross-model parameter drift (planner says amount: 100, executor writes amount: 10000 → DENIED).

Intent Binding — Cross-Model Drift Detection

Agent planners (Claude, GPT) declare one thing; executors (Qwen, DeepSeek) sometimes write another. No existing protocol verifies that actual tool call arguments match the agent's declared intent:

  • AP2 signs human authorization — not LLM intent
  • AgentPay does baseline tolerance (1%) — not zero-tolerance equivalence
  • ACS runs policy decisions — not argument-level equivalence
  • VAP (draft-samal-vap-00) explicitly excludes argument semantics from its wire schema

CCS Intent Binding fills this layer. The agent framework declares a structured intent before execution; CCS verifies actual arguments against it in sub-millisecond, zero-LLM time.

Example: amount drift

// Intent declared by planner
{
  "intent_id": "int-001",
  "intent_type": "payment",
  "fields": {
    "amount": { "value": 100, "binding_mode": "exact" },
    "recipient": { "value": "Alice", "binding_mode": "exact" }
  },
  "issued_at": 1755000000000,
  "ttl_ms": 30000
}

// Actual arguments from executor
{ "amount": 10000, "recipient": "Alice" }

// Result: DENIED — intent_arg_mismatch
// field: amount, expected: 100, actual: 10000

Three binding modes

Mode Behavior Example
exact Deep equality with math normalization 100, 100.0, 1e2 all match; 10000 does not
numeric_tolerance Absolute tolerance 100 ± 0.01 matches 100.005
pattern Regex match on string fields ^[A-Z]{3}$ matches "USD"

No intent declared? Falls through to standard 7-dimension verification. Zero breaking changes.

What It Detects

  • Command injection: shell metacharacters, curl|sh, rm -rf, eval()
  • Path traversal: ../, /etc/passwd, /proc/self/
  • SSRF: 169.254.169.254 (cloud metadata), localhost, private ranges — across any tool
  • Cross-tool attack chains: read sensitive file → network exfil = exfil_chain
  • Environment variable exfiltration: API keys, secrets, credentials
  • Obfuscation: hex encoding, base64, privilege escalation signals
  • Math safety: integer overflow (>2^53-1), NaN/Infinity
  • MCP config risks: plain HTTP, weak secrets, --insecure, TLS disabled

Evidence Chain

Every decision produces evidence with dual hashes (content_hash + evidence_hash) and chain linkage (parent_evidence_hash). Any third party can independently verify that evidence has not been tampered with — without trusting the operator.

evidence 1: allowed  (fs.read_file)       parent: null
evidence 2: denied   (shell.exec curl|sh) parent: ev1
evidence 3: denied   (http.fetch SSRF)    parent: ev2
evidence 4: denied   (fs + curl exfil)    parent: ev3

The 7 CCS Dimensions

  1. Structure — valid tool name, argument format, nesting depth, payload size
  2. Schema — type, required fields, enums, ranges, string lengths
  3. Security — injection, traversal, SSRF, env exfiltration, obfuscation + semantic attack chains
  4. Identity — caller agent ID verification
  5. Integrity — request hash validation
  6. Latency — execution time budget
  7. Cost — cost budget

Interoperability

CCS receipts are Ed25519-signed, JSON-based, and verifiable offline with zero dependencies. If your project produces or verifies signed attestations, receipts, or evidence records, see INTEGRATION.md — a one-page guide covering:

  • Full receipt field schema (22 fields)
  • JCS canonicalization spec
  • Three levels of interop (same crypto → cross-referenced chains → field-aligned)
  • 30-second verification snippets (Node.js + Python)
  • Clear scope boundaries (what CCS does / does not cover)

Join technical discussions in GitHub Discussions — receipt interoperability, field mapping proposals, and protocol feedback welcome.

Protocol Context

Ecosystem

Project What it does
ccs-demo Complete INSPECT→SIGN→VERIFY walkthrough — 6 scenarios, zero dependencies, node demo.js
ccs-verifier-action GitHub Action — verify CCS receipts in CI/CD pipelines
ccs-mcp-server Core MCP server (this repo) — runtime verification + evidence signing
ccs-proxy Commercial — inline enforcement proxy with L2 protocol attestation + L3 execution binding (Ed25519 signed receipts)

Links

  • npm: https://www.npmjs.com/package/ccs-mcp-server
  • GitHub: https://github.com/DSHCorrectover/ccs-mcp-server
  • IETF Draft: https://datatracker.ietf.org/doc/draft-correctover-ccs/
  • PyPI (full verifier): https://pypi.org/project/ccs-verifier/
  • Demo: https://github.com/DSHCorrectover/ccs-demo
  • GitHub Action: https://github.com/DSHCorrectover/ccs-verifier-action
  • Discussions: https://github.com/DSHCorrectover/ccs-mcp-server/discussions
  • CCS Proxy (commercial): https://dshcorrectover.github.io/ccs-proxy/
  • Integration Guide: INTEGRATION.md

License

Elastic License 2.0 (ELv2) — see the full terms. Reference implementation for CCS standard evaluation.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选