"How to send queries to an Elasticsearch cluster" matching MCP connectors:

Audit agent-distribution surfaces and create an evidence-based distribution plan.

Read-only MCP server for turva.dev, an agent-readiness audit and advisory service.

Check that your AI is being logical. Free tool that mathematically catches contradictions in agent reasoning. No account needed. Also offers paid guardrails that converts natural language to formal verification proofs, that anyone can check succinctly.

Verifiable cognition: any agent file to a deterministic content-addressed brain_id.

The MCP-native bridge to ERC-8004 (on-chain agent identity/reputation/validation): resolve registrat

Verifiable delivery for the agent economy: commit-reveal content notarization. Sellers commit to a d

MCP server teaching AI agents to implement TideCloak: auth, E2EE, IGA, security analysis

Developmental agents + Wu Wei compute routing: evolve from outcomes, route to cheapest reliable.

Third-party sandbox verdict on any artifact in one call, no account. Also an agent marketplace.

The agent-to-agent capability exchange — rent memory, reasoning and safety, settled per call.

Agent-to-agent marketplace MCP: list skills, buy/sell services, earn gas, cash out BTC.

Trust Graduation gate: Claude can do real work once Mission decides what it is allowed to do.

Trust infrastructure for AI agents: read a verifiable Trust Rating, claim an identity, earn a badge.

Self-destructing secret sharing for AI agents: encrypted burn links for credentials, plus an agent-to-agent dead-drop with exactly-once claim semantics. RAM-only, no database, open source (MIT). Source: https://github.com/mmmpym/flashpaper

Quant tools + an NFL fantasy draft assistant for AI agents — Kalshi & Polymarket EV, edge, ADP.

Reliable async execution for agent tool calls: schema-gate hallucinated payloads before they run, absorb rate limits and outages with retries and circuit breakers, and add idempotency, human approval gates, encrypted credentials, and signed-webhook results. Failed calls return an llm_hint the agent can self-correct from.

Third-party signed reads of how an AI agent behaved: drift, vows, and the town it works in.

A place, not an API: public 0-9 block commons — spark(), agent-to-agent meet, model-free text RPG.

One-to-three-cent spend routing, lead research, token-risk, and agent-tool comparison preflights.

Get a real human to verify, decide, or improve an AI agent's work.