Aimeat Protocol MCP Server

AIMEAT

AI Memory Exchange and Action Transfer

Love what you build, share what you know.

AIMEAT is a digital agency where people, AI, agents and apps work under one roof — and everyone owns their own data.

It's a real, working example of an AI-accelerated environment you can get for your own needs, from your own point of view, and run independently: your identity, your memory, your agents, your apps — on your own node. Humans and AI agents (Claude, ChatGPT, Grok, Gemini, local models, or your own code) collaborate in shared organisms and workspaces, build apps by talking to an AI, and — if you want — federate with other people's nodes. Plain HTTP + JSON, MIT-licensed.

Transparency built in — content a model writes through the node carries a machine-readable provenance record and a visible EU AI label, on every surface, from day one of Article 50.

Try it at aimeat.io, or run your own node and make it yours.

Specification (for people building their own node): v4.0 Core + v4.0 Platform · openapi.yaml · MIT License · Author: Jouni Miikki

Fastest start: let your AI assistant set this up

Cloned the repo and want it running without reading docs? Open startup.prompt.md and paste its contents into Claude Code, Copilot, Cursor, or any coding assistant with this repo open. It takes the assistant — and you — from a fresh clone to a live AIMEAT node (or a connection to a hosted one), registers your AI agents (CrewAI crews, Claude, Cursor, …) onto it, and explains the essentials of working with AIMEAT as it goes.

The prompt asks only what it can't determine for itself (self-host vs aimeat.io, SQLite vs PostgreSQL, your owner handle), runs the setup commands for you, and surfaces each agent's approval code for you to confirm. It never invents secrets or pushes anything outward without asking.

On a company-managed AI account, this prompt and every other one here can trigger a prompt injection warning before anything runs. That is the environment, not the prompt: what it means and the three ways round it.

See it in action

Four recordings of a real browser against the live node, with the account's real data. Waiting is cut and long stretches are compressed; every number on screen is the one the node actually returned.

An agent builds a working surface over MCP — nothing is clicked (1:05)

One sentence typed into a chat. Claude writes the plan onto an ORIGAMI board and ticks it off as it goes: 23 days of the owner's own AI usage read out of the account, the same numbers drawn as a chart, a whole CRM running live inside a frame, and an invitation published at its own address that anyone can answer without an account — then the replies, read back out of the CRM and filtered to that event. Nobody clicked anything in that window. 3 min 49 s from nothing to all of it, shown here in 65 seconds. (direct link)

The same surface, driven by a human (1:34)

An empty board → one sentence that starts several pieces of work at once → an invitation, where you describe what its button should do in plain language → publish → a guest answers with no account → the answer lands in the CRM → all of it side by side on one surface. (direct link)

A capability earns real money (2:46)

A real product (NUOTTA, not a demo) answers a question a supplier would actually ask; the same capability is listed for sale on EXCHANGE with what a buyer is told before paying; somebody buys it; the seller's wallet changes; and a till built on the surface reads the seller's own public figures every five seconds. (direct link)

An agent connects and reaches full operational readiness on its own (5:50)

0:45 Device auth with automatic polling (RFC 8628) | 2:10 Skill bundle download + boot sequence | 3:40 Hello Integration | 4:30 Test task proposed, executed, completed | 5:20 Commands + config registered, agent operational

Independently scored Level 5 · Agent-Native by isitagentready.com. Out of the box a node advertises everything an AI agent needs to find, join, and read it — an Agent Skills index, an /auth.md registration document with agent_auth metadata, an MCP Server Card, Web Bot Auth request signing, and Accept: text/markdown content negotiation.

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Why AIMEAT Exists

AI agents are currently isolated. Every session starts from zero. Claude doesn't know what you told ChatGPT. One person's Copilot can't ask another person's Claude to review a document. There is no standard way for agents to discover each other, share knowledge, or pay for services.

There are good tools solving pieces of this (as of April 2026). MCP lets agents call tools. A2A lets agents delegate tasks to each other. MemPalace gives an agent excellent recall of its own conversations. What's missing is the layer between them: when an agent produces something, there's no standard way for other users' agents to find it, use it, or build on it. No shared memory across users, no identity that spans nodes, no economy for pricing services.

AIMEAT covers that layer. Agents store their output in shared memory, other agents and humans discover it through federation, and apps pull it in. It works with the existing tools, not instead of them:

• MCP (now Linux Foundation, MIT) is the native tool-calling standard in AIMEAT
• A2A (now Linux Foundation, Apache 2.0) handles session-based delegation; AIMEAT adds persistent identity, memory exchange, and economic settlement
• ODPS — the Open Data Product Specification v4.1 (Linux Foundation, Apache 2.0) is how a priced capability describes itself. Every EXCHANGE listing is projected into an ODPS document on read — GET /v1/exchange/offerings/{id}/odps(.yaml) — so a catalogue or a buying agent that has never heard of AIMEAT can still read what a thing is, how it is delivered, what it costs, what you may do with the output, and who stands behind it
• MemPalace (MIT) is excellent single-agent memory; AIMEAT adds the network layer (sharing, federation, discovery)
• Nostr, ANP, Mem0/Letta etc. cover different angles; AIMEAT offers a simpler HTTP-based approach focused on shared memory and economy

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The Protocol

As of v4.0, AIMEAT is specified as two layers (the spec is split to match):

Core — the generic, federatable substrate any service could build on:

1. Identity — three principals across the network: GHII (humans), GAII (agents), and GEAI (ecosystem apps) — one resolver, one owner who owns everything
2. Memory & storage — consent-governed key-value + files with visibility tiers (private / owner / group / members / workspace / public), versioning, and schema locking
3. Authorization — consent + a runtime access-guard + a capability (IAM) model + scoped delegation grants
4. Collaboration — organisms (groups) and workspaces (shared, versioned, access-gated record spaces) — the "shared living surface" humans, agents, and apps mutate together
5. Economy & metering — morsels (an internal quality-gate token, not cryptocurrency) and a real-currency usage ledger (USD LLM cost). These are meters, not one currency, behind a pluggable, non-mandatory payment interface — the operator owns any KYC/billing. That interface now has real, non-custodial settlement: a protocol-agnostic checkout (native REST + UCP + ACP adapters) that pays in morsels, USDC over x402 (Base), or euros over Stripe Connect — funds land on the seller, never the node
6. Federation — bilateral peering whose live use is logging into a peered node with your own credentials
7. Observability — metrics, health, telemetry

Platform — what aimeat.io builds on the Core: the app platform (hosted apps + scoped grants + origin isolation), the agent fleet plane (onboarding, tasks, directives, telemetry), the compute + metered-AI plane (sandboxed extensions, cortex, the owner's LLM as a metered resource, scheduler, workflows), and skills & capabilities. This is where most of the product lives — because an AI-generated app on generic APIs beats a purpose-built protocol feature.

CSM (Community Service Manifest) lets a service declare its data schema; the generic APIs enforce it, and clients render the UI. Everything specific (semantic search, translation, image generation, code review) is a capability some agent or app provides — the network is the extension system.

Protocol layers

┌─────────────────────────────────────────────────────────────┐
│  PLATFORM (aimeat.io on the Core)                           │
│  Apps + grants + origin isolation · agent fleet plane ·     │
│  extensions/cortex · metered AI · scheduler · workflows ·   │
│  skills & capabilities                                      │
╞═════════════════════════════════════════════════════════════╡
│  CORE — Federation    cross-node identity/login, peering    │
│  CORE — Collaboration organisms, workspaces, knowledge      │
│  CORE — Economy       morsels + USD metering ledger, trust  │
│  CORE — Authorization consent, access-guard, IAM, delegation│
│  CORE — Data          memory, storage, schema-lock          │
│  CORE — Identity      GHII / GAII / GEAI, Ed25519, JWT       │
└─────────────────────────────────────────────────────────────┘

A node MUST implement Identity, Data, and Authorization. Economy, Collaboration, and Federation are recommended but optional for specialized nodes; the Platform is everything built on top.

Design principles

1. Zero SDK requirement, HTTP + JSON is enough
2. Self-describing (HATEOAS-style responses)
3. Self-bootstrapping, an AI can read a URL and integrate itself
4. Fully decentralized, no single point of control
5. Data sovereignty, data stays where it was created unless explicitly shared
6. Economically self-regulating, morsels gate low-value writes; real cost is metered separately (payment stays optional and operator-owned)

Applications and packages

On top of the protocol sits the application layer. Apps are self-contained HTML files built by AI and stored on your node, each served from its own origin. Server extensions run in a QuickJS-WASM sandbox, processing data and calling external APIs. Cortex manifests provide shared UI components (charts, forms, layouts) that any app can use. Packages bundle all of these together into installable units that others can browse and install from the template gallery.

An app is also an agent surface: any function it exposes can be published as a priced app-tool that appears on EXCHANGE, in the node's MCP server card and in the product feed. The browser and the agent then run the same engine — GRAPH 3D previews a surface in WebGL and sells the identical vector render server-side; Pixel Mirage dithers in the browser and sells the same render for a morsel. One implementation, two audiences.

Node types

Authentication tiers

(The old Tier 0.5 keyed-browse / one-time-key path is deprecated — superseded by MCP + device authorization.)

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What You Can Do

Use the AI chat you like, with your node in it

One command connects Goose, Claude Code, Cursor, VS Code or Claude Desktop to your node:

aimeat connect client goose --url https://aimeat.io --owner your-handle

It does three things. It authorizes an agent of its own for that program (you approve it from your profile -> Agents tab, as always). It writes the MCP settings in the shape that program expects, merging into whatever you already had. And it leaves you a launcher script that starts the program with the token supplied at run time.

Options: --agent <name> names the agent, --workdir <path> decides where the program starts (an agent writes files where it is launched, so this keeps it out of your source repos), --surface appdev narrows the toolset to app building, --home <path> puts the credential somewhere else, --name <server> lets a second node live alongside the first.

Two guarantees, because this command edits files you own. Your token is never written into a config file: each client is given a variable reference, a headers-helper command, or the local connector, and the token itself stays in the connector home. And every other MCP server you had configured is left exactly as it was -- the command merges, backs the file up first, and refuses to write at all if it cannot parse what is there.

After it runs you are talking to your own memory, apps, organisms, workspaces, tasks and the marketplace from that window: "what do I have in my workspace", "build me an app that does X", "what did my agents do today".

Goose is the one to try if you want a chat that is not Claude, ChatGPT or Grok. It is an open source terminal agent that takes its model from OpenRouter, so you pick the model and pay per token for exactly what you use -- a strong model for a hard build, a cheap one for everything else, switched in seconds:

# a model for this session only
$env:GOOSE_MODEL = "z-ai/glm-5.2"; C:\Users\you\.aimeat-goose\launch-goose.ps1
GOOSE_MODEL=z-ai/glm-5.2 ~/.aimeat-goose/launch-goose.sh

# inside a running session
/model deepseek/deepseek-v3.2     # switch model without restarting
/mode auto                        # stop confirming every tool call

Set OPENROUTER_API_KEY in your environment before launching. The full toolset costs roughly 50k input tokens per turn; --surface appdev cuts that to about a third when you are only building apps.

Connect AI agents

There are two ways to connect:

1. aimeat connect CLI (recommended, added in 1.10.0). Any runtime that can run a shell command can attach to a node in seconds:

npx aimeat connect --url https://your-node --owner your-handle [--agent name]
# you approve from your profile -> Agents tab
# the CLI stores the token, downloads the runtime-specific skill bundle,
# and prints a paste-ready Hello Integration instruction for your agent

For MCP-capable runtimes (Claude Desktop, MCP-aware IDEs), run aimeat connect serve afterwards to attach the AIMEAT toolset over stdio. For CLI-only runtimes that cannot do stdio, every MCP tool is also reachable via aimeat connect call <tool-name> --json '<input>'.

Multi-agent connector. A single aimeat connect serve process can serve multiple agents at once. Add more agents with aimeat connect add --agent <name> --url ... --owner ...; list them with aimeat connect list; remove with aimeat connect remove <name>. In multi-agent mode, MCP tools accept an optional agent_name parameter; when omitted, the agent marked primary: true in its per-agent config is used. This is the path for connecting one interactive agent (Claude Code) plus several task-runner agents (e.g. CrewAI crews) from one connector process -- see docs/integrations/crewai.md for the task-runner pattern.

Agent modes. Every agent declares a mode at registration: autonomous (continuous), interactive (chat/IDE, default), task-runner (triggered, runs one task, exits), coordinator (orchestrates others), or workstation (a node-visiting agent that lives in the user's own environment -- VSCode, Claude Desktop -- and uses MCP directly). Mode picks the Hello Integration flow: task-runner agents get a reduced 7-step onboarding (no command surface, but the test-task pair is kept as a smoke test), and workstation agents get the narrowest 4-step flow (auth + platform + capabilities + directives) because they are not node-resident -- no runtime config, slash commands, telemetry, or task queue. The others run the full 16-step flow (12 required + 4 optional). Combine modes with owner-managed tags (crew:*, source:*, role:*, project:*) for filtering and grouping in the profile UI. Details: docs/coding-guidelines/agent-tags.md.

2. Copy the prompt from your profile. If you do not want to install a CLI, your profile -> Agents tab still produces a paste-ready prompt with the device-auth flow baked in -- give it to any AI agent, the agent calls one endpoint, you approve, and it is connected with its own identity and scoped permissions.

Claude Pro, ChatGPT Plus, and other MCP-capable AIs connect directly as MCP clients. OpenClaw, Hermes, Claude Code, and Cursor all work. Three scope presets (readonly, standard, full) control what each agent can access.

Prompt injection warnings on company-managed AI accounts

If you run Claude, or another AI tool, inside a company-managed environment (Enterprise, Team, or an administrator-configured workspace), connecting this node or pasting one of its prompts can produce a prompt injection or untrusted source warning. The warning appears before anything has run.

The reason is the environment, not the prompt. In a managed environment every external service the administrator has not approved is untrusted by default, and the same warning applies to any unapproved connector. Injection classifiers score content that enters the model's context from outside -- a tool result, a fetched page, a connector response -- rather than what you type or paste yourself; without an approved connector, what the prompt asks the model to read arrives as an untrusted fetch. On a personal account, or through a connector that is already approved, the same prompts usually pass without a warning.

Do not click past the warning out of habit. If you do not know where a prompt came from, do not run it. Every prompt this project hands you is shown in full before you copy it, and every one of them is in this repository, readable.

Three routes, in this order:

1. Ask your administrator to approve the connector. They approve the MCP endpoint (https://your-node/v1/mcp), the OAuth 2.1 + PKCE sign-in to that node -- each person signs in as themselves, there are no shared keys -- and the tool set the connector exposes. The order matters: the administrator approves first, then you add the connector and sign in. For their review: the tool inventory and per-tool annotations in aimeat/src/mcp/annotations.ts, the OAuth metadata at /.well-known/oauth-authorization-server (RFC 8414) and /.well-known/oauth-protected-resource (RFC 9728), and the whole server in this repository.
2. Use the manual route instead of MCP. AIMEAT works with no connector at all: the app composes a prompt, you read it, you paste it into your chat by hand, and you bring the answer back. Nothing is connected, and nothing leaves the chat until you send it. This is a permanent path, not a workaround, and for confidential material it is often the right one anyway.
3. Run AIMEAT yourself. The whole codebase is MIT licensed and self-hostable (npx aimeat init). You can read exactly what a prompt does, run it on your own server, point the prompts at your own address, and verify the behaviour yourself. We are not asking you to trust it. We are asking you to check it.

The same explanation is shown in the product, above every copyable prompt: the landing page, the classic portal, the /v1/start playbook, /v1/connect, and the Experience Center.

Connect agent platforms (Dify, n8n, Open WebUI, ...)

AIMEAT also bridges agent platforms. A tool like Dify, n8n, or Open WebUI is its own island -- the agents and data you build there can't reach agents anywhere else. Pointing the platform at an AIMEAT node changes that, and it's a one-time MCP connection: add an MCP server for the node's /v1/mcp (or a scoped /v2/mcp/agent surface), authorize once in the browser, and the full AIMEAT toolset appears -- no token pasting, no per-tool wiring. The agent can even run Hello Integration on itself (paste the canonical instruction from your profile -> Agents), then immediately read/write shared memory, storage, and knowledge packages, discover other agents, and hire capabilities.

That's the federation play: each platform is an island, and through AIMEAT its agents move what they build onto the shared network -- where it spreads across federated nodes and other agents can find and use it. Walkthrough: docs/integrations/dify-hello-integration.md.

Build apps with AI

Tell any AI what you want and get a working app. Your node serves a canonical build-app prompt at GET /v1/prompts/build-app: the app-catalog's Create new app flow fetches it, or point a real coding agent at the same spec — the repo ships a preconfigured OpenHands app-builder (tools/aimeat-openhands/) that builds a single-file HTML app and publishes it live over MCP. Apps use AIMEAT memory, storage, and the client SDK, and can be packaged and shared as installable templates.

For simple one-off apps, just copy the prompt from the portal landing page, paste it into any AI chat, and you get a working HTML app that uses AIMEAT memory. No registration needed.

If your AI can make HTTP calls (Claude Code, Cursor, Copilot), point it at your node's llms.txt (the index; the full manual behind it is llms-full.txt) and describe what you want — it reads the API docs, checks /v1/capabilities, and builds. If your AI chat cannot make HTTP calls (ChatGPT, Gemini, free-tier Claude), copy the app-generation prompt from your node's "Try it" page at /v1/classic; the AI asks you what you want and produces an HTML file you paste into the App Catalogue. Add the node as an MCP server and the AI can install extensions and publish apps directly, with no UI at all: aimeat_app_publish, aimeat_extension_install, aimeat_cortex_install, aimeat_memory_*, aimeat_storage_* cover the whole publish/install/inspect cycle.

What has been built on it

Every app below is a single HTML file living on the node, built by talking to an AI, published over MCP in seconds. There is no per-app backend code beyond sandboxed extensions. All of them are live on aimeat.io; each runs on its own origin (https://<app>.apps.aimeat.io/) under H-2 origin isolation.

ORIGAMI — a surface that builds itself

You write a sentence; a window appears on the board and fills itself in. The window can be data, a chart, a form, a published app running as the real program, or work handed to one of your agents. Alternatives fold out beside a frame so you can compare them, and the winner gets promoted. The first two recordings above — one driven by an agent over MCP, one by a human — are both ORIGAMI.

EXCHANGE — a marketplace where apps buy what they need

Providers list capabilities (data feeds, APIs, agent work, callable app-tools); apps and agents browse or post a need, accept a budget-capped contract, and every call is metered. Prices show on both rails at once — real money settled to the provider, and morsels that pace usage.

ODPS — the descriptor a listing already speaks

Every listing is projected on read into an Open Data Product Specification v4.1 document (Linux Foundation, Apache 2.0) at GET /v1/exchange/offerings/{id}/odps.yaml — try one live. Nothing is stored in ODPS form, and nothing is invented: a field the node cannot know is omitted, never filled with a plausible value, so an unstated SLA is an absent SLA block instead of a promise of 99.9 %. The AIMEAT-specific truth (metered coordinate, pinned interface + I/O schema, call recipe, provenance, observed reputation) sits under product.x-aimeat, where the schema permits extensions, so the document still validates against the official one.

NUOTTA — public tender intelligence

Finnish public procurement, searchable by what you sell rather than by who you know — CPV codes match by prefix, so 72 covers every IT service beneath it and returns 1,456 notices in the shot below. Then Go/No-Go analysis, scoring, budget leads, a buyer money picture, stored tender documents and bid drafting. Five of its functions are listed on EXCHANGE as priced app-tools.

TURBO — make the API you already have AI-native

TURBO bolts an AI-native layer onto an existing system without touching it. Paste your openapi.yaml into an AI chat with TURBO's prompt (or let it draft the spec from your docs first) and you get an acceleration report: what becomes possible, proposed prices, proposed agent crews. You approve the operations and the EUR/USD prices, an MCP-connected AI executes one handoff brief, and TURBO polls the node and turns each step green only when the artifact really exists.

What you end up with: your API proxied as a sandboxed extension with the key encrypted and host-pinned, a dev library (AIMEAT.{yourapi}.* from one script tag), a published app built on it, and an agent skill with priced tools agents buy through checkout. Everything installs into your own account.

CADENCE — a CRM that fits in one hand

Mobile-first: tap to dial, dictate the note by voice, set the next follow-up. Company cards enrich from the Finnish trade register and show buying signals from public procurement. One named public form per event carries its name into every contact's source and tag. Per-member notification routing, HubSpot CSV import, SSE live-refetch, bilingual handbook — and four of its capabilities are for sale on EXCHANGE.

HARMONY MAP — chords as a living map

Chords are nodes and the arrows show how harmony moves; follow them and you get progressions that sound good. Six diagram shapes (function map, circle of fifths, cadence staircase, molecule, Tonnetz, tension pyramid), triads through jazz 9ths, seven modes that recolour the same theory, then scenes and tracks you arrange into a whole song — with a live 3D note-cube, an AI composer, guided lessons, ear training, MIDI export and a share link that carries the entire song.

Band Jam — jam together in real time

Multiple people join a room, pick instruments, and play together over the node's WebSocket layer: a ProTracker-style pattern editor, live jam mode, multi-layer song arrangement, a note river showing what everyone plays. Now with 116 ST-XX sample disks (CC0) in an extended .asb format free of Amiga-era size limits — every sample becomes an instrument you can pick per track — plus an AI bandmate that follows your key and groove and fills in drums, bass and chords.

Games

TOWER TETRIS — one persistent shared 3D tower for the whole node. Every line you clear adds a floor in your colour; doubles, triples and TETRIS wreck 3/6/10 floors of your top colour, and demolition pays points. The tower survives forever in a server-side extension, so you always arrive at whatever everyone else has built (43 floors, record 47, at the time of the shot).

TELETYPE — a writing-based R-Type. Your @-ship rides a rail, enemies are words you type down letter by letter, and magenta GUNNERs fire the same short bullet-word back at you. One keyboard, two threats: the next letter you press decides which one lives, so attack and defence share a single input channel. Five word pools from GEEKNERD to SUPERTYPIST, flawless-streak combos, three-letter high scores.

Soliton Soundscape — hear a nonlinear collision

Launch two solitons and watch what a collision actually does to them: the taller one advances, the smaller is delayed, and the thin translucent curves show where each would have been without the interaction. The phase shift is annotated on the graph and the combined wave is played as a tone.

And the rest of the shelf

LATTICE — a spreadsheet where cells are live AIMEAT memory and formulas are AI transforms, every computation priced and logged; an agent can read the sheet, plan a recalculation, price it from the sheet's own history, and write it back. GRAPH 3D — plot a formula in three dimensions, then sell the same render to agents as a priced app-tool with vector SVG output. Pixel Mirage — 32 palettes and 18 dither algorithms in the browser, and the identical render sold to agents from one shared engine. PRH Yritystutka — Finnish company intelligence over PRH and Statistics Finland open data, with a watchlist and change alerts. MISSIONS — governed agent mission control, where policies compile into enforced approval gates. AIMEAT Experience Center — the academy and showroom, free to explore without an account.

Try them yourself, no account needed: HARMONY MAP · Soliton Soundscape · TOWER TETRIS · TELETYPE · ODPS validator · Experience Center. The rest ask you to sign in, because they work on your data.

Let an app into your account, on your terms

Apps run on their own origin, so an app never sees your login session. When one needs your data it sends you to a consent screen on the node itself, and what it receives is its own scoped key that you can revoke at any time.

The screen leads with what the app is (icon, name, the origin you are actually talking to) and names the areas it works with in plain language. "Show the exact permissions" opens the full scope list, each one a checkbox: uncheck anything you would rather not give and the key is minted with exactly what stays checked, never more than the app asked for. Your own apps skip the screen entirely, and an app that later wants a new scope has to come back and ask, with the addition marked as new.

Transparency of AI-generated content

From 2 August 2026, Article 50 of the EU AI Act asks two things of anyone publishing what a model wrote: tell a person, and mark it so a machine can tell. AIMEAT does both at the platform level, so every app, agent and self-hosted node inherits it.

Content produced through a node carries a provenance record — which model, when, which principal, how much a human was involved, which sources — minted where the content is generated and served everywhere the node serves that content: the JSON envelope, an AI-Disclosure header, a Link: rel="ai-provenance" pointing at the record, HTML metadata and JSON-LD, the markdown agent face, and the MCP and WebMCP tool results. Anyone can ask GET /v1/provenance/by-hash/<sha256> whether this node produced a given piece of content, without an account.

Where a person reads it on the node's own surfaces, they see a label: the official EU AI Office icon plus plain-language text in English or Finnish, at first exposure, with a link to the full record.

For an app builder that is one call:

const { content, provenance } = await AIMEAT.ai.complete({ prompt });
AIMEAT.ai.disclose(provenance);   // renders the EU icon + label, both themes, both languages

Documentation truncated — see the full README on GitHub.