MakerPerks MCP-AQL Adapter

Find and claim the free credits, discounts, and startup programs you qualify for — and let your AI assistant do the hunting and the paperwork.

There are hundreds of thousands of dollars in perks for builders — cloud and AI credits, discounts, startup and student programs — scattered across the web, and most people never claim them. This connects that directory to your AI assistant so it can find what fits you and even apply for you.

What you can do with it:

1. Connect it to your AI assistant — Claude, Cursor, and other AI tools. (For the hosted version, there's nothing to install.)

2. Ask what you can get. Tell it about yourself — "I'm a bootstrapped two-person startup building with AI" — and it searches the whole directory and shows you the perks that fit, and how much each is worth.

3. Let it work out how to apply. For any program, it figures out the exact steps to sign up.

4. Have it apply for you — with your approval. It fills in the application from details you've saved and submits it, pausing to check with you before anything sensitive. Whether a program signs you up through a direct connection or a normal web form, your assistant can handle it — or hand you a ready-to-submit form to finish yourself.

You stay in control the whole way: you choose how much it does on its own, and it never claims something you don't qualify for. For the strongest control, set your MCP client to ask before the mutating tools run (everything that acts on your behalf is on mcp_aql_execute and the other mutating verbs; reads are separate) — see Keep a human in the loop.

A native MCP-AQL server over MakerPerks — the browseable, agent-friendly directory of builder perks (free credits, discounts, and programs for startups, students, OSS maintainers, indie devs, and non-profits).

It speaks MCP-AQL: instead of registering a separate MCP tool for every query and action, the server exposes a small set of semantic verbs — read · create · update · delete · execute (~120 tokens each) — and the agent discovers the operations behind them at runtime via introspection. So an agent's tool-registration cost stays nearly flat even as the number of operations grows past 30 — a fraction of a conventional "a tool per operation" server. (The public endpoint is read-only — just the read verb; the full server exposes all five.)

Beyond reading, the adapter is a substrate for action and curation: agents discover and propose application flows, an operator accepts them, the directory can federate many opportunity feeds, and the server can produce feeds too — with safety checks throughout, and it only ever acts with your permission, on your behalf.

System at a glance

flowchart TB
  agent["MCP agent<br/>(Claude Desktop / Code, Cursor, …)<br/>brings the model + web"]

  subgraph core["One shared core (same code everywhere)"]
    direction TB
    ops["What an agent can do here:<br/>• browse + search the directory<br/>• discover + propose application flows<br/>• curate + publish them (operator)<br/>• drive signups, with consent"]
  end

  subgraph ro["Read-only endpoint — makerperks.mcpaql.com"]
    direction TB
    rostd["stateless · hardened<br/>serves directory + published flows"]
  end

  subgraph stf["Stateful endpoint — makerperks-dev.mcpaql.com"]
    direction TB
    dos["GitHub OAuth · per-user DO<br/>profile/vault · flow registry DO"]
  end

  feeds[("perks.json feeds<br/>(one or many, federated)")]
  flows[("flows.json overlay<br/>+ accepted overlay")]
  kv[("shared KV mirror<br/>operator-published flows")]

  agent <-->|stdio / Streamable HTTP| core
  core --- ro
  core --- stf
  feeds --> core
  flows --> core
  stf -->|reconcile_flows| kv
  kv --> ro

In the picture:

• MCP agent — your client (Claude Desktop / Code, Cursor, …). It brings the model and any web access; the adapter brings the directory, the tools, and the guardrails.

• The core — one request router. The same code runs three ways: local stdio (a personal tool), the public read-only Worker, and the stateful Worker (per-user GitHub login + Durable Objects).

• Read-only endpoint (makerperks.mcpaql.com) — public, no login, stateless and hardened. Serves the directory and any flows an operator has published.

• Stateful endpoint (makerperks-dev.mcpaql.com) — per-user GitHub login, a per-user profile + encrypted credential vault, the shared flow registry, and operator-gated curation.

• Feeds → flows → KV mirror — the data: one or many opportunity feeds federated into the directory; a curated flows overlay (how to actually apply); and a shared mirror that pushes operator-blessed flows to the public endpoint with no redeploy.

What makes it unique:

• A near-flat tool cost — a few semantic verbs + runtime discovery, so an agent's setup cost barely grows as the operation count does.

• Model-agnostic flow discovery — the server hands a connected agent a research scaffold and the safety gates; the agent supplies the intelligence. No model or provider SDK is baked in.

• Curation that can't be vandalized — anyone may propose a change, but only a trusted operator can accept and publish it, so untrusted input can't damage the shared directory. Publishing back to an upstream source is always operator-driven — the server never opens pull requests or edits anyone's repository on its own.

• A federating, producing substrate — point it at many feeds (perks, grants, programs, camping slots, …); it can also generate a feed of its own, which round-trips back in as a source.

• One core, three deployments — personal tool, public read-only, and full stateful, from the same code.

Related MCP server: IndieStack

Connect

• Hosted (zero install): add https://makerperks.mcpaql.com as a remote MCP connector (claude.ai, Claude Code, Cursor, …). OAuth registers automatically.

• Local (stdio): npm install && npm run build, then point your MCP client at node dist/index.js.

• Your own directory: run it locally or self-host it and point it at your own feed(s) (perks.json, grants.json, …) — see docs/INSTALL.md.

Then call mcp_aql_read with { "operation": "introspect" } to discover the operations.

What it does

• Read the directory — list_programs / get_program / search_programs / get_application_flow, carrying decision signal (value, audience, eligibility, verified date, redemption URL) so an agent decides without a second call.

• Discover & propose flows — a model-agnostic toolkit (get_discovery_brief → verify_flow_proposal → propose_flow) a connected agent drives to turn a bare perk into an automatable, verified application flow. The server supplies the scaffold and the gates; the agent brings the model and the web.

• Curate (operator-gated) — users are untrusted and may only propose; a configured operator accepts flows into the served set and reconcile_flows publishes them to the public endpoint. The server holds no write credentials and opens no PRs.

• Federate & produce — ingest one or many perks.json-shaped feeds (perks / grants / college programs / camping slots …) into one directory, and emit a schema-valid feed of its own (export_perks) — a general opportunity-directory substrate, not just a MakerPerks app.

• Act on your behalf — with consent — the adapter can drive the actual signup whether or not the program has an API. It assembles the application from your saved profile and an encrypted credential vault and runs it under an autonomy switch you control (review every step / auto-submit low-risk / full-auto within limits), pausing before anything sensitive, routing payment / real-identity steps to an out-of-band check, and never claiming eligibility you don't have. When there's an API, it submits directly. When there isn't, it discovers the application flow and the connected agent carries it out with its own browser automation (computer-use / browser-use) — same assembled data, same guardrails. That's the superpower: no API required, as long as the flow is discoverable and the agent can drive a browser. (If it can't, the pre-filled application is a ready-to-finish handoff.) The adapter supplies the flow, the data, and the safety rails; the agent supplies the doing.

Status

Everything described above is built, tested, and deployed — browsing and search, the discover-and-propose flow toolkit, operator-gated curation, multi-feed federation, feed production, and the consent-based signup pipeline (profile + encrypted vault + autonomy switch). Two endpoints are live:

• Read-only (public, no login): https://makerperks.mcpaql.com

• Stateful (per-user GitHub login): https://makerperks-dev.mcpaql.com

Every capability is specified and validated under openspec/specs/, with 200+ automated tests passing. Still ahead: broader provider coverage, a contribution pipeline back to the upstream directory, and anti-abuse limits — see docs/ROADMAP.md for the full plan.

Documentation

• docs/INSTALL.md — install + point it at your own feed(s) + self-hosting

• docs/ARCHITECTURE.md — system model, the capability map, and diagrams (flow lifecycle, federation, the trust boundary)

• docs/flows-roundtrip.md — the flows.json round-trip + the operator publish/contribute workflow

• docs/ROADMAP.md — the staged plan and status

• CLAUDE.md — project configuration & conventions

• openspec/specs/ — the spec baseline (every capability, with requirements + scenarios)

License

Code & schemas: AGPL-3.0 (commercial tiers available, like the rest of the MCP-AQL org). Docs: CC BY 4.0. The directory data is MIT (MakerPerks); only MIT-safe data crosses back to the upstream directory — no AGPL code does. The AGPL covers the engine, not the feeds it reads or emits — see LICENSING.md for the full data boundary.