DCL Trust Oracle — AI/LLM Output Audit (x402 MCP) MCP server
online · 100% uptimecom.fronesislabs/dcl-trust-oracle·v2.3.1·1 starsAI/LLM agent output audit MCP: policy eval, tamper-evident chain, AI safety, x402 USDC on Base.
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DCL Trust Oracle — AI/LLM Output Audit (x402 MCP) tools (18, 7 write)
write = sends, deletes, buys or postsdcl_audit_decodewrite actionFreePOST-ACTION Basic Audit ($0.10). Retrieves a record from the tamper-evident chain by tx_hash.
dcl_audit_decode_deepwrite actionFreePOST-ACTION Deep Forensic Audit ($0.50). Extended output with drift_context and full chain integrity verification.
dcl_commitFreeFINAL-STEP Leibniz Layer Crypto Commit ($0.01). Writes a trading/agent decision to the append-only Leibniz Layer audit chain and returns a Merkle-proof-style receipt: `tx_hash` (proof of this specific commit), `chain_hash` (the previous commit's hash, linking this one into the chain), and `chain_depth` (this commit's position in the chain). Unlike the evaluate_* tools, this call has no pass/fail verdict of its own — it always succeeds and simply seals the decision. Passing `prior_checks` is optional but recommended: it records which earlier pipeline steps (firewall/wallet/trade/MEV) this specific commit is downstream of, in one auditable record. Always run this LAST, after every other crypto-suite check has passed.
dcl_evaluate_batchFreePRE-ACTION Bulk Processing ($0.10). Evaluates a list of items in one call; each item is a dict shaped {"response": str, "policy"?: str}, where policy defaults to "default" if omitted and may be any built-in policy name (default, strict, anti_jailbreak, safety, content_quality). Each item gets its own independent COMMIT/NO_COMMIT verdict via the same logic as the matching single-item evaluate_* tool; results are returned in input order under `results`, plus a shared `batch_id`. Capped at 200 items per call — oversized batches are rejected. Use this instead of multiple single-item evaluate_* calls when checking several responses — optionally against different policies — in one priced call rather than paying per item separately.
dcl_evaluate_fastFreeFAST Pre-Action Audit ($0.01). Runs the response through the server's "default" policy: a substring check against 3 forbidden phrases ("ignore previous instructions", "jailbreak", "bypass safety") with a 0.7 minimum-confidence threshold. Each forbidden match found costs 0.4 confidence; if confidence falls below 0.7, or any match is found, the verdict is NO_COMMIT and `reason` lists which phrase triggered it. Otherwise COMMIT. Use this as the default low-cost first-pass gate before a risky agent action; switch to dcl_evaluate_strict for a broader, higher-bar check, or to dcl_evaluate_jailbreak / dcl_evaluate_safety / dcl_evaluate_quality for a narrower, single-topic check instead of the general-purpose default policy.
dcl_evaluate_jailbreakFreePRE-ACTION Instruction Adherence Check ($0.02). Runs the "anti_jailbreak" policy: a substring check against 6 forbidden phrases ("ignore previous instructions", "jailbreak", "bypass safety", "pretend you are", "act as if", "DAN") with a 0.8 minimum-confidence threshold — each match costs 0.4 confidence. Returns COMMIT if no phrase matches and confidence stays at or above 0.8, otherwise NO_COMMIT with `reason` listing the matched phrase(s). Use this as a targeted, cheaper check when the concern is specifically prompt-injection / persona-hijack risk; use dcl_evaluate_strict instead when you also want safety- and default-policy phrases covered in the same call.
dcl_evaluate_jailbreak_cryptoFreePRE-ACTION Crypto Jailbreak & Injection Detection ($0.02). Crypto-specialized instruction-override/jailbreak/injection screen: standard role-switch and instruction-override patterns, plus crypto-specific drain-wallet injection (e.g. "transfer all funds to...", fake "test transaction" requesting full balance) and unlimited-approval injection (e.g. type(uint256).max, "approve unlimited allowance", skip-slippage-confirmation framing). Any match returns NO_COMMIT with `reason` and `findings` naming the matched category/categories; run this FIRST in the DCL crypto pipeline, before wallet/trade/MEV checks, since it screens the input itself rather than a decision built on top of it.
dcl_evaluate_mevwrite actionFreePOST-ACTION MEV & Market-Abuse Compliance Screen ($0.03). Text-level screen (not a mempool/transaction analyzer) for front-running/sandwich-attack language, wash trading/layering/spoofing, KYC/AML red flags (mixers, structuring, obscuring fund origin), and pump-and-dump/rug-pull language. Any critical-severity finding, or two or more major-severity findings, returns NO_COMMIT; a single major-severity finding is also returned as NO_COMMIT but with a distinctly higher `confidence` (~0.55 vs ~0.05-0.2 for harder violations) so downstream callers can tell a soft single flag apart from a hard multi-finding block. Each finding includes an illustrative `regulatory_reference` tag (MiFID II, FCA, or an EU AI Act article).
dcl_evaluate_output_sanitizerFreeFINAL-GATE Output Sanitizer ($0.02). Post-processing checkpoint that strips secrets/credentials, PII, crypto material (seed phrases, private keys, wallet addresses), internal network details (private IPs, MAC addresses, .internal/.local/.corp hostnames), and unsafe shell/SQL/path-traversal fragments from a raw model response — plus a narrow, high-precision safety net for direct self-harm-instruction-seeking and targeted-harassment phrasing (not a general toxicity classifier). Returns a single `sanitized_output` with every match replaced by `[REDACTED]`; use that instead of the original whenever verdict is NO_COMMIT. Run this as the LAST gate before a response reaches its destination — after `dcl_evaluate_jailbreak_crypto`/other input-side checks have already run, and immediately before `dcl_commit` seals the final decision. Internally re-uses the same detection tables as `dcl_evaluate_secrets`/`dcl_evaluate_pii` for the secrets/PII categories, so results stay consistent with those tools.
dcl_evaluate_piiwrite actionFreePOST-ACTION PII Detection Scan ($0.02). Regex-based scan across 8 personal-data categories, with a Luhn checksum on card numbers to reduce false positives. Any finding results in NO_COMMIT.
dcl_evaluate_qualityFreePRE-ACTION Content Quality & Drift Check ($0.03). Runs the "content_quality" policy: flags 12 absolutist or unverifiable-claim phrases (e.g. "guaranteed returns", "100% accurate", "studies show", "without a doubt") with a 0.85 minimum-confidence threshold — the highest bar of any single-policy tool. Returns NO_COMMIT if any phrase matches or confidence falls below 0.85, with `reason` listing the matched phrase(s). Use this to catch overconfident or unsubstantiated claims in generated content — a different concern from jailbreak or safety phrasing — e.g. before publishing agent-written copy or reports.
dcl_evaluate_safetyFreePRE-ACTION Baseline Safety Check ($0.01). Runs the "safety" policy: flags 2 forbidden disclaimers ("I cannot be held responsible", "no guarantees") and additionally REQUIRES the substring "AI" to appear somewhere in the response — missing it costs 0.2 confidence even with no forbidden phrase present. Minimum confidence is 0.75. Returns NO_COMMIT if confidence drops below 0.75, with `reason` naming the forbidden phrase found or the missing required pattern. Use this when you specifically need to confirm an AI-disclosure marker is present and the two disclaimer phrases are absent — not as a general-purpose safety net; for broader coverage use dcl_evaluate_fast or dcl_evaluate_strict instead.
dcl_evaluate_secretswrite actionFreePOST-ACTION Secret & Credential Leak Scan ($0.02). Regex-based scan across 8 categories (API keys, cloud credentials, tokens/JWTs, private keys, DB URLs, connection strings, env assignments, webhook secrets, internal endpoints with auth). Any finding results in NO_COMMIT.
dcl_evaluate_signalwrite actionFreePOST-ACTION Market Signal Fabrication Screen ($0.03). Pattern-based heuristic on the output text alone (no source price feed) — flags guaranteed-price-prediction language ("will definitely hit $X"), absolute-certainty claims ("100% certain", "cannot go down"), a fabricated-price flag when a specific dollar figure co-occurs with a guaranteed-outcome claim, and an invented-token flag when a "$TICKER" cashtag doesn't match a small set of well-known symbols (false positives are possible for legitimate lesser-known tickers — this is a heuristic pre-check, not ground truth). For a full claim-by-claim check against an actual price-feed snapshot, use the local grounding workflow instead of this live tool. Verdict/confidence collapsing follows the same rule as dcl_evaluate_mev: any critical finding or 2+ major findings is a hard NO_COMMIT; exactly one major finding is a softer NO_COMMIT at ~0.55 confidence.
dcl_evaluate_strictFreeSTRICT Pre-Action Audit ($0.05). Runs the response against a broader, higher-bar "strict" policy: the union of all forbidden phrases from the default, anti-jailbreak, and safety policies (8 phrases total), with a 0.85 minimum-confidence threshold instead of the default policy's 0.7. Each matched phrase costs 0.4 confidence; if confidence falls below 0.85, or any phrase matches, the verdict is NO_COMMIT with `reason` listing every match found. Use this instead of dcl_evaluate_fast when the cost of a false COMMIT is high — e.g. before an irreversible or high-stakes agent action — since it catches jailbreak- and safety-adjacent phrasing that the plain default policy would miss.
dcl_evaluate_tradeFreePRE-ACTION Trade Decision Verifier ($0.02). Screens trade-decision language for guaranteed-return claims, zero-risk/"can't lose" framing, and unqualified "buy/sell X now" directives — any match is NO_COMMIT. If no unsafe language is found, COMMIT additionally requires the word "risk" to appear anywhere in the text as a minimum disclosure marker; its absence alone triggers NO_COMMIT with `reason` noting the missing disclosure. Produces an immutable `trade_receipt` (tx_hash/chain_hash/chain_depth) distinct from the top-level audit hash, for downstream systems that specifically need a trade-shaped receipt object.
dcl_evaluate_walletwrite actionFreePOST-ACTION Wallet Secret Guardian ($0.02). Scans for BIP-39 seed phrases (12 or 24 consecutive wordlist words), raw hex or WIF-format private keys, Ethereum/Bitcoin wallet addresses, and API keys/bearer tokens appearing near wallet/custody/signing terminology. Any finding results in NO_COMMIT — wallet secrets have no safe threshold, unlike other DCL evaluators. Returns a `sanitized_output` with all matches redacted (null if nothing was found) and a masked `redacted_sample` per finding — the real value is never returned or stored server-side.
dcl_pipeline_startFreeSESSION Management ($0.05). Generates a new `pipeline_id` and returns session metadata (scope, expiry, initial drift_mode) for organizing a series of related checks under one identifier. Note: this call does not currently link the returned pipeline_id to later evaluate_* calls — there is no server-side session state that ties subsequent audits back to it; it is an identifier/timestamp issuer, not an active tracking session. Use this to obtain a shared reference ID for your own client-side grouping of a multi-step audit sequence; do not rely on it to automatically aggregate drift across calls.
Public scan report
scanner v0.1.10 · 2026-11-03 · same rubric, same numbers if you re-run it
- –Code scanremote-only server, no package to scann/a
- Live reliabilityremote reachable in 2081ms17/20
- Tool poisoning18 tool descriptions checked15/15
- Auth qualityopen endpoint exposes 7 write-action tools with no auth3/15
- Maintenancelast push 18 days ago15/15
- Maintainer identityendpoint served from the registry-verified namespace domain8/10
Findings (1)
- highWrite-action tools reachable without authentication
auth.open-write
How DCL Trust Oracle — AI/LLM Output Audit (x402 MCP) compares
Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.
- Safety score
- 77/100
- higher than 50% of 4,460 AI and agents servers
- Tools
- 18
- the typical AI and agents server has 6
- Tools that change things
- 39%
- typical: 0%. Fewer write tools means less an agent can break
- Uptime
- 100%
- of our recent checks; the typical remote AI and agents server answers 100%
- Response time
- 295 ms
- faster than 48% of 2,331 AI and agents servers
- GitHub stars
- 1
- more than 50% of 2,711 AI and agents servers
What its tools let an agent do: 11 read · 7 change
Install DCL Trust Oracle — AI/LLM Output Audit (x402 MCP) in Claude Code, Cursor or VS Code
claude mcp add --transport http dcl-trust-oracle https://mcp.fronesislabs.com/mcp
What the publisher says
From the DCL Trust Oracle — AI/LLM Output Audit (x402 MCP) repository's README, as published. We do not edit it. Read it on GitHub
DCL Trust Oracle
Don't trust the agent. Trust the proof.
Autonomous AI agents now take actions with real consequences — financial, legal, reputational. Most of them are black boxes: no record of what was decided, why, or whether that decision was tampered with afterward.
DCL Trust Oracle closes that gap. Every agent output is evaluated against policy in real time and sealed into a tamper-evident hash chain — a deterministic, cryptographically verifiable record of what happened and when. Edit any past entry and the entire chain invalidates. No one — not even Fronesis Labs — has to be trusted for the record to hold up.
What It Does
DCL Trust Oracle provides deterministic policy evaluation for LLM outputs with a tamper-evident audit chain. The system stores only cryptographic hashes and decision metadata — never raw content — enabling verifiable, post-action forensic analysis across distributed AI agents.
Available two ways:
integration for AI agents. Live at https://mcp.fronesislabs.com/mcp (streamable-http). Also listed on Smithery (remote URL → same production endpoint; see smithery.yaml) and the official MCP Registry.
- REST API (webhook_server.py) — direct HTTP integration.
- MCP Server (mcp_server.py) — native Model Context Protocol
Both servers share the same evaluation logic and tamper-evident chain (dcl_core.py), and are priced identically.
Quick Start
REST API
pip install -r requirements.txt
python webhook_server.pyServer runs on http://localhost:8080
MCP Server
Production (hosted):
https://mcp.fronesislabs.com/mcpStreamable HTTP transport — point any MCP client here directly, no setup required.
Local development:
pip install -r requirements.txt
python mcp_server.pyServer runs on http://localhost:8081 (streamable-http transport)
Tools & Endpoints
Pre-Action Evaluation
Catch a bad output before it reaches a user, a wallet, or downstream system.
Session Management
Post-Action Forensics
Shortened. The full README is on GitHub.
Nothing above is checked by us. What we check is on the safety report.
DCL Trust Oracle — AI/LLM Output Audit (x402 MCP): common questions
- Is DCL Trust Oracle — AI/LLM Output Audit (x402 MCP) MCP server safe?
- Mostly: it is graded B (77/100). Read the DCL Trust Oracle — AI/LLM Output Audit (x402 MCP) safety report
- How do I install DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)?
- It runs remotely at mcp.fronesislabs.com. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.
- Does DCL Trust Oracle — AI/LLM Output Audit (x402 MCP) need an API key?
- Not as far as the registry entry and our scan can tell: no credentials are declared or required.
- Is DCL Trust Oracle — AI/LLM Output Audit (x402 MCP) maintained?
- The last commit was 18 days ago (2026-10-17). The latest release is v2.3.1.
- Is DCL Trust Oracle — AI/LLM Output Audit (x402 MCP) up?
- 100% of our last 28 checks got an answer. We check remote servers about four times a day.
- What can I use instead of DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)?
- Servers from other publishers that do the same job: Agoragentic Agent OS MCP server, three.ws Provenance MCP server and IWE (iwe.md) MCP server.
- How does DCL Trust Oracle — AI/LLM Output Audit (x402 MCP) compare with other AI and agents MCP servers?
- safety score 77/100 (higher than 50% of 4,460 AI and agents servers); tools 18 (the typical AI and agents server has 6); tools that change things 39% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.
Alternatives to DCL Trust Oracle — AI/LLM Output Audit (x402 MCP)
Same job from other publishers: the closest match first, then the best rated.
Agoragentic Agent OS MCPTriptych OS (Agent OS) MCP for governed routing, receipts, and USDC settlement on Base.not reviewedEstablishedB- three.ws ProvenanceAppend-only, signed, on-chain-verifiable agent action log — record and audit what agents did.not reviewedEstablishedB
- IWE (iwe.md)Markdown knowledge base as agent memory. Runs against the notes directory it is started in.not reviewedEstablishedA
- Roast My Design SystemAudit your Design System and serve your Agent the rules that keep AI-written UI on-system.not reviewedEstablishedA
- llmtrimMCP server and proxy that compresses LLM prompts, tool output, and replies to cut token cost.not reviewedEstablishedA