Chainward MCP server

ChainWard

An automated, adversarially-verified on-chain investigation engine for AI agents on Base.

Decodes · Observatory · Website

ChainWard investigates what AI agents on Base actually do on-chain — and proves it. Its core is an automated pipeline that researches a target, writes a forensic report, and runs that report through a verifier gauntlet that re-checks every numeric claim against the chain before anything publishes. There is no human in the loop between the trigger and the published article. The data is read from a self-hosted Base node, so the ground truth has no third-party RPC dependency.

The interesting engineering here isn't "an LLM writes crypto articles." It's the machinery that stops the LLM from being confidently wrong in public.

The auto-decode pipeline

One command — pnpm decode:auto <@handle|0xaddress> — resolves the target, then spawns Claude Code headless against an orchestrator prompt that runs a five-phase, multi-subagent pipeline. Each phase is a fresh fan-out of subagents; the orchestrator only dispatches and gates.

decode:auto @target
        │
        ▼
  ┌─────────────────────────────────────────────────────────┐
  │ 1. RESEARCH    3 parallel agents                          │
  │                identity/chain · token economics · utility │
  ├─────────────────────────────────────────────────────────┤
  │ 2. WRITE       1 agent → decode.md + 5-tweet thread       │
  ├─────────────────────────────────────────────────────────┤
  │ 3. GAUNTLET    3 parallel verifiers                       │
  │                citation · failure-mode · voice            │
  ├─────────────────────────────────────────────────────────┤
  │ 4. DECISION    citation + failure-mode block; voice is    │
  │                advisory. Any FAIL → 1 retry, then halt.   │
  ├─────────────────────────────────────────────────────────┤
  │ 5. PUBLISH     render OG card → commit → deploy →          │
  │                poll live URL → post launch tweet          │
  └─────────────────────────────────────────────────────────┘
        │
        ▼
   published decode  ·  or a halt report (nothing ships)

Why the gauntlet matters

The hard problem with LLM-generated analysis is confident fabrication — a plausible number with no basis. ChainWard's gauntlet is built on three rules, enforced by the orchestrator, that make unsupervised publishing safe:

• Verifiers re-fetch; they don't trust. The citation verifier extracts every numeric and factual claim from the draft and re-fetches it independently from the source — transaction receipts off the self-hosted node, the ACP API, Blockscout — then compares within hard tolerances (USD ≤ $0.01, counts and tx hashes exact). An uncited claim is an automatic FAIL, even if the value happens to be right.
• Verifier output is read-only. The orchestrator cannot litigate a FAIL or spawn a "re-check the verifier" agent. If the verifier says FAIL, it's FAIL.
• One retry, then stop. The writer gets exactly one corrective pass against the failed claims. If anything still fails, the run halts and publishes nothing. There is no iterating to a green light — convergence is rationalization.

The result is a pipeline that would rather ship nothing than ship a wrong number. Every published decode has had each of its claims re-derived from chain data by an agent that was trying to fail it.

Proof

Investigations the pipeline has published — each claim chain-verified and falsifiable (all decodes →):

Shortened. The full README is on GitHub.