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MCP server for Brazilian Federal Senate open data (legislative, administrative, e-Cidadania).
Why recommended
Scored from trust, install, risk, and community signals.
Real outcomes: sample pending
Governed execution cells for AI agents: Docker/K8s sandboxes with policy, audit, and approvals.
Run MCP tool calls through Runestone Gatekeeper — policy enforcement, human approval, audit.
Unofficial read-only client for the mein-aldi.de (ALDI Süd) grocery-delivery catalog.
Plain-English browser tests for AI agents: real Chrome, deterministic verdicts. Free, no API keys.
x402 analytics feed + 20+ pay-per-call agent tools over x402 (data, LLM, risk), no accounts.
Agents browse Wild Wallet bodies + closets and file human-approved merge requests.
The prediction MCP — score your prompt before you generate, so you never waste a credit.
Test the voice agents you run: scored transcripts, pass/fail verdicts, latency and WER metrics.
Pre-tool policy enforcement + JSONL audit for AI agents. Apache 2.0.
Structural graph map of any codebase so LLMs navigate by structure, not guesswork.
Real human judgment as agent tools — ask a question, get a structured human answer back.
