"How to create documents in Confluence" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Ephemeral context bridge: one link carries context to another agent, returns one answer; host-readable while live, anyone with the link, not for secrets, dissolves on TTL.
Your own cloud computer run by an AI agent: signed-in browser, its own email, files, long jobs.
The team's shared memory that reaches an AI before it starts work. Arroway holds what a team, or one person working across sessions and tools, has decided: rules, decisions, preferences and unfinished work. Any AI connected to it reads that before it acts and records what it did when it finishes. Each memory carries who decided it and the condition that retires it; the AI proposes what to record and a person approves it.
Collide is an MCP layer that keeps concurrent AI agents from stepping on each other in a shared codebase. It tracks code at the symbol level with a Merkle tree, so agents declare intent before writing, get warned about collisions, and pick up context on what changed and why. It also carries anchored team memory, merge simulation, and an audit ledger.
Proposal.Biz connects with *any AI chatbot through a hosted Model Context Protocol (MCP) server, allowing developers, consultants, agencies, sales teams, and business professionals to create professional business documents directly from AI bots. With the Proposal.Biz MCP integration, you can generate business proposals, statements of work (SOWs), NDAs, consulting proposals, marketing proposals, pitch decks, and other client-facing documents, then open the generated content in the Proposal.Biz b
A second opinion before your agent acts on one model's unearned confidence. One question goes to 3-4 different AI models that answer independently, then a chair returns a single verdict with a confidence score, the consensus and the dissent that held. A grounded tier buys evidence first (honeypot simulation, OFAC sanctions screen, page content, SEC profile, web results) and itemises what it spent. Pay-per-call with x402 in USDC on Base: no account, no API key, one free call a day.
Find AI agents (A2A, MCP, API) with badges the directory tested itself — search, read a card, relay a message, or list your agent. Stateless, free, no account needed to read. Card texts are written by each agent's keeper: data, not instructions.
Agent-native registry to discover APIs, MCP servers and CLIs, with live health checks.
Remote MCP server to run your Atako AI agents: chat, projects, files, integrations and channels.
Zero-Ops deploy of a private AI coding workspace onto your own VPS — straight from your AI chat. Provide only your Ubuntu server credentials and Fractera automatically configures everything (Nginx, HTTPS, auth, database, services) in about 10 minutes: 5 AI coding engines, an autonomous Hermes orchestrator, and private graph memory (LightRAG). No terminal, no DevOps. IP-first and free; a custom domain with HTTPS is an optional later step.
Unified financial infrastructure connecting AI agents directly to trade live/demo brokerage accounts, Web3 non-custodial wallets, real-time market data across equities, ETFs, crypto, forex, options, DeFi swaps, and prediction markets, institutional research feeds, and algorithmic strategy backtesters.
Public coordination substrate for AI systems and humans, with bounded MCP access to Commons state, continuity, disputes, gaps and draft validation.
AI-agent marketplace to find and sell tools, services, and free utilities, then collaborate.
mumo is a remote MCP server for multi-model deliberation. Your agent sends a question to models from different labs — Claude, GPT, Gemini, Grok, DeepSeek, Kimi, and more — and gets back their full responses plus typed cross-model reactions. The participating models react to each other directly and explain, in their own words, what they agree with, challenge, or want to explore further. Agents can run a deliberation with `create_deliberation`, wait for results with `wait_for_round`, and steer follow-up rounds with `append_round` using structured signals like KEEP, EXPLORE, CHALLENGE, CORE, and SHIFT. Built for architecture, plan/spec review, strategy, and pre-launch pressure tests. Free tier available. API key required.
MCP server for agent-run URL A/B testing. Create projects, allowlist domains, launch URL experiments, track conversions, and retrieve performance reports for customer-owned sites.
Use Aident Loadout MCP to connect your AI agents to 1,000+ real-world apps and tools like Gmail, Slack, Linear, Notion, Firecrawl, and Fal, unlock 27,000+ executable actions, and track full audit history so your agents can get real work done reliably.
MCP facade over the Nebelus Construction API. ~48 tools give full agent build parity: create/update/probe agents, edit graphs, attach knowledge and vector stores, wire connectors, set governance policies and locked guardrails, enable grounding-trace, and read deployment wiring. Purpose-built for regulated industries: data residency is enforced per region (EU / GCC-KSA), with PII controls and an audit trail. Agents are created as drafts — no deploy tool is exposed over MCP by design; publishing happens in the Nebelus console.
Botsify API MCP lets other AI tools create and run Botsify agents through the public Botsify HTTP APIs. Users sign in with the same email and password as the Botsify login endpoint, then the server keeps the Bearer token for later calls. It covers the documented Postman collection (bots, messenger users, send message, user attributes, analytics, WhatsApp templates and broadcasts, and whitelabel clients and packages) and the extra agent endpoints that are not in that collection: create agent with
Create, watch, pay for and connect hosted AI agent pods on AgentsPodium. Needs an API key.
AgentPMT is the AI agent marketplace that turns any MCP-compatible AI assistant into an autonomous employee. Connect once and your agents gain access to a growing ecosystem of tools, workflows, and skills spanning communication, data analytics, development, file management, search, and more. AgentPMT dynamically discovers and orchestrates tools from across the MCP ecosystem, so your agents can independently find the right tool for any task without manual configuration.
