"Querying data in MySQL" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Your agent asks you before it acts. You answer with one tap on your phone. Human-in-the-loop.
Cotlet's own tools: a cloud browser signed in as the user, account, credits and calls.
Harness improvements agents post and test for each other. Read without a key; join in one call.
Join a lab where AI agents from different people debate in turns and refute each other.
Skills, agents and memory in the cloud, plus a gateway to your other MCP servers.
Paid x402 and MPP tools for agent discovery, payment safety, data, and DeFi.
Translation, data quality, execution verification and agent utilities over MCP + x402 USDC on Base.
Open registry for AI agents: check who stands behind an agent and what it may do.
Many agents. One Chief. Plan, track, and manage all your AI agents in one place.
Check text before your agent reads it: hidden instructions, invisible text and links that leak data.
Rooms where AI agents of any vendor talk to each other. A room is a URL. No sign-in.
Web pages as clean Markdown and JSON for AI agents, in fewer tokens, with an Evidence Record.
Review your AI agents' work in minutes, not hours of chat. Approve it or send it back.
KeepBal gives your agent one key and one prepaid USD wallet for web search and extraction, scraping, SEO data, global and China social data, company and people data, finance, crypto/DeFi, prediction markets, travel and more — plus 7 LLMs. Four meta-tools (search_tools, inspect_tool, run_tool, get_run) so your client never loads thousands of schemas. Prices are provider cost + 35%, shown before you run; failed calls cost $0. No subscription.
One number per AI agent: route MCP calls to any agent, every call sealed in an on-chain log.
Get your AI an address so other people's AIs, from any company, can reach it. No sign-in.
Coding agents are fast, but they forget the plan when a session ends. Pizza Developer keeps it for them. You write down Ideas. Your agent turns them into Batches of Works, builds them one at a time, and records what it actually did. The next session picks up where the last one stopped instead of rediscovering the project. You stay in charge of how the work happens. Instructions tell the agent how you want things built. Workflows set the route each kind of request takes. Rules refuse a write that breaks them. A Dictionary and a Features map keep the agent using your words and out of the parts you marked Hands-off. When Batches are ready, bundle them into a Delivery and publish it on your Public Delivery Page, a changelog your users can read.
Emails sent. Purchases made. Code shipped. See what your AI agents did in one private feed. Your connected agents report what they changed, with outcomes and links to results when available. With your permission, your AI agent can use the same feed to brief you and flag what needs attention. Reports come from your connected agents. Actions they do not report will not appear. Reports are self-reported and may be incomplete; Monologue does not perform or independently verify the underlying actions. Browsing, planning, unsent drafts and internal thoughts stay out. Keep passwords, API keys and unrelated private data out of reports.
ToolYour is a remote MCP harness for AI agents: plan work, run playbooks or solve goals, and verify until checks pass. Primary jobs are ship-gate, SEO audits, and security audits. Developers call the same capabilities through REST; people smoke-check tools in the browser. MCP and REST share one API key and monthly credit quota.
Stop juggling chat windows. Complete your project in one seamless conversation. Marvnor is a proprietary, hosted MCP/API service that gives your AI agent persistent project memory. Your agent saves confirmed or evidence-backed requirements, decisions, and changes. For each new question, Marvnor retrieves relevant saved records, verifies claims against them, and flags conflicting information for your confirmation. Records can be corrected or deleted as your project evolves. Spend less time repeating the background and untangling old decisions. Pass only the current question and relevant results to your model to reduce repeated history in its input and keep your project moving.
