correctover-mcp-server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@correctover-mcp-serverRun CCS verification on agent-123's tool call"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 — running in 30 seconds
npx -y ccs-mcp-serverAdd 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.
Related MCP server: agentseed
Tools
Tool | Purpose |
| 7-dimension runtime verification (Structure/Schema/Security/Identity/Integrity/Latency/Cost) + semantic attack-chain analysis + math overflow detection. Blocks by default. |
| Issue a tamper-evident evidence record ( |
| Audit MCP configuration JSON for security risks. |
| Verify actual tool arguments match a declared intent — zero tolerance, zero LLM calls. Catches cross-model parameter drift (planner says |
| 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: 10000Three binding modes
Mode | Behavior | Example |
| Deep equality with math normalization |
|
| Absolute tolerance |
|
| Regex match on string fields |
|
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 toolCross-tool attack chains: read sensitive file → network exfil =
exfil_chainEnvironment 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: ev3Receipts 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
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.
This server cannot be deployed
Maintenance
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