ccs-mcp-server

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.

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本工具的合规授权与批量能力可通过支付宝 SkillPay 获取：

• 打开货架：https://skillpay.alipay.com/public/correctover
• 或把这行发给你的 AI 助手：请通过 SkillPay 购买并安装 Skill「CCS收据批量审计」

China users: purchase via Alipay SkillPay — see above.

⚡ Quick Start — running in 30 seconds

npx -y ccs-mcp-server

Add it to any MCP client — paste this into your mcpServers config in Claude Desktop, Cursor or Cline:

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

Or install from the official MCP Registry: search "Correctover" inside your client, or open registry.modelcontextprotocol.io and find io.github.Correctover/ccs.

No API key, no account, no environment variables required — an Ed25519 signing key is generated automatically on first run (set CCS_KEY_DIR, or CCS_PRIVATE_KEY/CCS_PUBLIC_KEY, only if you want to pin a persistent key). Zero dependencies. Pure Node.js stdlib (Node ≥ 18). 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	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).
verify_receipt	Offline-verify a CCS Ed25519-signed receipt: checks the signature against the embedded signer public key, reports tampering, and optionally pins an expected signer key or fingerprint.

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

Receipts are Ed25519-signed and JSON-based: verify them offline with the built-in verify_receipt tool, or independently with any Ed25519 library. Cross-tool receipt verification (same crypto, chain linkage, field mapping) is designed to work without this package installed.

The 7 CCS Dimensions

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

Protocol Context

• IETF: draft-correctover-ccs — an individual Internet-Draft; not an RFC or IETF endorsement, and the Datatracker page is authoritative for revision and status
• Complements: VAP draft-samal-vap-00 (scope/budget/purpose), Microsoft ACS (policy decisions), AP2 (human authorization)
• Does not replace: authentication, payment networks, policy engines

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/

License

Elastic License 2.0 (ELv2). See LICENSE.