Enables vendors to run a declared check battery against their own MCP server, emit sealed Assay-format scorecards with evidence hashes and environment fingerprints, and have a local verifier re-run a random sample to issue counter-signed verification receipts.

Enables LLM-powered developer environments to discover and evaluate potentially beginner-friendly GitHub issues using explainable heuristics, clear rationale, and direct evidence links over stdio.

A read-only server that enables AI agents to triage Kubernetes CronJobs in a single tool call: whether runs happened and stayed on schedule, whether durations are trending slower or overrunning, which jobs have silently stopped succeeding, and why a given run failed, with evidence and stated basis for each verdict. It also lists active runs and recent failures while enforcing namespace allowlists and no write access.

Enables agents to ingest local registry snapshots, detect removed or newly deprecated tools and schema-breaking changes, verify deprecation claims against hash-chained evidence, and generate migration diffs for agent configurations.

Enables local, evidence-led analysis of a master recording into inspectable stems, timing, beats, harmony, lyrics, MIDI and sonic evidence, then finds complete-bar loopable sections for Shorts and other short-form video. Results stay traceable through source hashes, manifests, JSON/TSV/NPZ artifacts and portable Sonic Visualiser sessions, and it can run as a CLI, Python library, web service, container or MCP plugin.

Enables agents to crawl, search, map, and extract structured evidence from websites, SPAs, PDFs, Office files, YouTube videos, GitHub repositories, ArXiv papers, shopping and travel listings, and CVE advisories via local or cloud-backed tools.

Multi-language repository map for agents and humans: MCP tools, dependency graphs, symbol indexes, impact review, and bounded source evidence.

Runtime evidence that helps agents trace, profile, and burn down hotspots in application and native code, GPU kernels, and inference stacks.

Merge autonomy for AI-authored code. Turn your repo's standards into evidence-backed readiness reports the agent reads back.

Evidence-driven task orchestration with PO First for Claude Code, Kiro, Codex, DeepSeek Harness, Antigravity, OpenCode, Grok Build, GitHub Copilot, Hermes Agent and OpenClaw

Awesome Claude Mods — evidence-graded index of Claude Code mods, plugins and hooks. 20 languages, rebuilt every 2 hours.

Enables deterministic request screening and, after native owner approval, fixed-target DeadBolt enforcement actions such as kill, deny, revoke, and evidence retrieval through local stdio MCP. Model analysis can advise but cannot approve actions.

Enables compatible assistants to inspect Live sessions, silently mix and master bounced stems, compare references, design with installed plugins, and track local evidence and progress through supported tools.

Exposes an assess_repo tool that scans a repository, local folder, or npm/PyPI package and returns supply-chain risk findings with file, line, and code evidence — install-time scripts, dangerous patterns, secrets, AI-agent manipulation, impersonation and published-artifact mismatches — while never executing the target's code. Findings carry severities from info to critical so results can gate installs and CI jobs.

Enables agents to verify AI product claims through Tencent Yuanbao search with cited evidence and authenticity verdicts, find dramas or songs from quoted lines, and check search quota usage via five MCP tools.

Enables auditing GitHub Actions workflows for security problems directly from an MCP client, returning a risk score and findings with file:line evidence, attack narratives, and fixes. Also exposes the full rule catalog and a one-call demo that writes and scans a deliberately broken workflow.

Enables an agent to author, render, and visually verify Manim animations from a described concept, returning evidence and structured verdicts so incorrect renders can be fixed before export.

Exposes device discovery, capabilities, observation, logs, task execution and cancellation, and evidence retrieval as tools so MCP clients can run typed mobile automation tasks and inspect proof of completion.

Enables exhaustive, unit-by-unit analysis of open-source repositories through MCP, cataloging every file and symbol, resolving dynamically built routes and URL paths, and mapping call and value flows via LSP and Joern. Every conclusion carries file:line evidence tied to a pinned commit, with unresolved items tracked in an unverified list and assigned verification grades L0–L2.

Enables an AI assistant to read raw source text and propose FollowTheMoney records into a hydra archive, checking them against the archive's schema and existing entities, linking rather than duplicating, and storing the text as the cited evidence. Supports drafting, dry runs, saving, read-back verification, image hints and archive creation while leaving uncertain judgments to human review.

Enables authorized AI clients to search locally stored WeChat conversations, media, and attachments, retrieve original-message context, and cite evidence through a local stdio MCP interface.

【EMNLP 2026 Demo】A debugging framework for agentic AI systems: diagnose failures, attribute root causes, recover with evidence, and validate fixes through reruns.

蒸留蔵 — distilled long-term memory for agents: recall by meaning, writing gated by evidence, one kura per agent mode. Ships as a DeepSeek Harness plugin and an MCP server.

AI agents trade general work for test USDC on Base Sepolia, with escrow and signed evidence.