Skip to main content
Glama

πŸ”„ Synced from a monorepo β€” but with a live history. aimarket-hub mirrors the canonical AI-Factory monorepo. History here is append-only (no force-push). Pull requests are welcome β€” merged PRs are imported back into the monorepo and re-synced here, so your contribution becomes canonical. πŸ’¬ Issues Β· Pull requests both welcome.

AIMarket Hub

🌐 English Β· Русский Β· EspaΓ±ol Β· FranΓ§ais Β· δΈ­ζ–‡ Β· Glossary

Agents can already pay each other. We make it possible for them to trust each other.

⚠️ Pay-on-Verified β€” read before relying on it. A buyer can make payment conditional on an independent verdict: the hub holds the price and pays the seller only if the delivery passes; a failing delivery is refunded. The verdict comes from an LLM jury (Metis) judging the delivery against the intent the buyer wrote β€” evidence, not proof. It works only on escrow-backed payment channels, for capabilities the hub executes itself, from $0.05. β†’ What it does not promise Β· How to enable it Β· A run on real money

🟒 Just want to try the marketplace? Install nothing.

Paste https://modelmarket.dev/mcp into Cursor, Claude, or any Streamable-HTTP MCP client. Two tools: market_search + market_invoke. A few trial invokes per caller, then 402; each invoke returns a signed receipt. Pass source_hub from search back into invoke β€” most of the catalogue is federated, and omitting it 404s. Live trial size: free_trial.max_invokes_per_visitor in /.well-known/ai-market.json. β†’ docs/hosted-mcp-endpoint.md

{
  "mcpServers": {
    "aimarket": {
      "type": "streamable-http",
      "url": "https://modelmarket.dev/mcp"
    }
  }
}

Cursor: .cursor/mcp.json. Claude Desktop: claude_desktop_config.json. Same shape. Agent skill (no clone): docs/agent-install.

πŸ›  Run your own hub

pip install "aimarket-hub[pqc]"     # Python 3.11+, or: uvx --from "aimarket-hub[pqc]" aimarket …
aimarket quickstart --name "My hub" && aimarket serve

Then aimarket add lists your own API (buyers pay your wallet directly), aimarket doctor runs the admission check other hubs will run on you, and one curl knocks on the federation. Docker: docker compose run --rm hub quickstart then docker compose up -d; Railway, Render and Fly files are included. β†’ Run your own hub Β· on a platform Already run an API? aimarket node makes it a node without a hub β†’ run a node. No server at all? Sell a Python function on HESTIA β†’ sell without a server.

Ecosystem: AICOM overview & live demos Β· Package: aimarket-hub on PyPI Β· Community: Discord Β· Pollux Β· Telegram Β· Castor

Federation hub for AI capability discovery, micropayment routing, and plugin-extensible invoke.

Reference implementation of AIMarket Protocol v2. One HTTP surface to search a federated catalog, open payment channels, invoke capabilities with safety and compliance hooks, and settle on-chain β€” without custodial wallets.

Codex subcontracting case study β€” A real order from Codex: two GAIA calls, 0.002 USDC on Base, signed receipts and β‰ˆ $0.00477 total including gas.

One-call pipelines Β· New live test from Codex β€” 2026-09-30.

Demo


Related MCP server: meshkore-mcp

Table of contents


Overview

AIMarket Hub sits between capability providers (factory-shipped products, oracles, peer hubs, data-cap publishers) and consumers (Flutter desktop apps, agents, embeddable widgets, MCP clients).

Problems it solves

Problem

Hub answer

Fragmented AI APIs

Federated search over .well-known/ai-market.json peers

Per-call payment friction

Pre-funded channels β€” one deposit, N micro-invokes, one settlement

Trust in anonymous sellers

Reputation scores + stake bonds (plugin)

Compliance & audit

Provenance receipts on every invoke (Ed25519 + W3C VC)

Unsafe prompts

Safety pre-check with signed rejection + refund

Zero-Trust Agent Discovery

No human app-store reviewer. Agents find peers over federation, pass safety + attestation gates, and invoke only verified capabilities β€” cryptographic trust replaces marketing trust.

What

Federated discover β†’ safety / reputation / TEE plugins β†’ routed invoke

Why

Scales to millions of micro-capabilities; malicious listings can’t drain channels

Deep dive

docs/killer-feature-zero-trust-discovery.md Β· Ecosystem capabilities


Architecture

System context

C4Context
  title AIMarket Hub β€” system context

  Person(consumer, "Consumer", "App, agent, or widget user")
  Person(provider, "Provider", "Lists capabilities on a hub")

  System(hub, "AIMarket Hub", "Search, route, invoke, settle")
  System_Ext(peers, "Peer hubs", "Federated catalogs")
  System_Ext(factory, "AI-Factory", "Shipped products β†’ capabilities")
  System_Ext(chain, "Base L2", "USDT channels")

  Rel(consumer, hub, "discover Β· channel Β· invoke")
  Rel(provider, hub, "manifest Β· capabilities")
  Rel(hub, peers, "crawl Β· route")
  Rel(hub, factory, "import shipped products")
  Rel(hub, chain, "open/close channel")

Container diagram (this repository)

flowchart TB
  subgraph hub_process["aimarket_hub (FastAPI)"]
    API["api.py β€” REST /ai-market/v2/*"]
    CRW["crawler.py β€” BFS federation"]
    DB["database.py β€” capability index"]
    CH["channels.py β€” ledger + settle"]
    SG["safety_gate.py"]
    PR["plugin.py β€” PluginRegistry"]
    FB["factory_bridge.py"]
    SIG["signing.py β€” Ed25519 manifests"]
  end

  subgraph plugins["plugins/ (entry_points)"]
    P1["safety Β· provenance Β· channels …"]
  end

  subgraph storage["Persistence"]
    SQL[("SQLite / PostgreSQL")]
  end

  API --> CRW
  API --> DB
  API --> CH
  API --> SG
  API --> PR
  API --> FB
  CRW --> DB
  FB --> DB
  DB --> SQL
  PR --> P1
  P1 -.->|"pre/post hooks"| API

Factory import path

Shipped AI-Factory products are indexed as local capabilities on hub startup:

sequenceDiagram
  participant Factory as AI-Factory pipeline.db
  participant Loader as factory_products_loader
  participant Bridge as factory_bridge
  participant Index as database (SQLite)

  Note over Factory,Index: On hub startup or sync script
  Factory->>Loader: COMPLETED / DEPLOYED products
  Loader->>Bridge: normalize capabilities
  Bridge->>Index: upsert source_hub=local
  Index-->>Bridge: indexed count

Sync ops: ../scripts/sync_pipeline_mirror_and_hub.py


Repository layout

aimarket-hub/
β”œβ”€β”€ aimarket_hub/           # Core package
β”‚   β”œβ”€β”€ api.py              # HTTP routes (search, invoke, federation, plugins)
β”‚   β”œβ”€β”€ crawler.py          # Peer discovery (SSRF-hardened BFS)
β”‚   β”œβ”€β”€ database.py         # Capability + peer index
β”‚   β”œβ”€β”€ channels.py         # Payment channel ledger
β”‚   β”œβ”€β”€ plugin.py           # setuptools aimarket.plugins loader
β”‚   β”œβ”€β”€ factory_bridge.py   # AI-Factory product import
β”‚   β”œβ”€β”€ safety_gate.py      # Built-in safety fallback
β”‚   └── …
β”œβ”€β”€ plugins/                # Hub-local plugins (e.g. aimarket-provenance)
β”œβ”€β”€ tests/                  # pytest suite
β”œβ”€β”€ Dockerfile
β”œβ”€β”€ LICENSE                 # Apache-2.0
β”œβ”€β”€ CONTRIBUTORS.md
β”œβ”€β”€ SECURITY.md
└── docs/
    └── value.md

Sibling packages (monorepo root plugins/): 15 plugins β€” top-5 on PyPI (install guide); full set bundled in Docker.


Quick start

Prerequisites

  • Python 3.11+

  • Optional: Docker for container deploy

Install & run

# the post-quantum verifier is a core dependency since 3.15.18 (without it the Hub cannot index
# peers that sign hybrid); [pqc] stays valid and keeps older versions right
pip install "aimarket-hub[pqc]"
# optional core plugins (TEE, channels, reputation, safety, MCP packager):
pip install "aimarket-hub[plugins]"
# production Hub with PostgreSQL, payer-proof/escrow crypto and ML-DSA-65:
pip install "aimarket-hub[postgres,escrow,pqc]"
# sign hybrid (Ed25519 + ML-DSA-65); back up <key>_mldsa together with the Ed25519 key
export AIMARKET_PQC=1
aimarket serve
# β†’ http://localhost:9083

Taking money from the first day: Running this hub for yourself

For a local MCP client or Glama, use python -m aimarket_hub mcp --init. This serves the local hub's tools over stdio without opening an HTTP port; logs go to stderr. The usual hub settings apply. See Glama build settings for the exact build and launch commands. (python -m aimarket_hub quickstart also reports how the Hub signs and where its keys are).

Verify discovery and search:

curl -s http://localhost:9083/.well-known/ai-market.json | jq .
curl -s "http://localhost:9083/ai-market/v2/search?intent=translate&budget=1" | jq .
curl -s http://localhost:9083/ai-market/v2/plugins | jq '.plugins | length'

Publish a capability (community providers)

Third-party developers list an HTTP endpoint in the catalog and earn USDC when agents invoke it. Production hubs require stake, LUMEN trust scoring, and Ed25519-signed provider responses β€” see docs/supply-security.md.

cd examples/hello-capability && python3 server.py   # terminal 1 β€” prints provider_pubkey
export AIMARKET_ALLOW_LOCAL_PUBLISH=1               # dev only
# production: POST /ai-market/v2/supply/stake first, with your own credential and a
# tx_hash for EVERY positive amount β€” the deposit is verified on-chain and single-use,
# whatever its size, so sub-minimum drip-feeding cannot reach the stake gate.
aimarket publish capability.json --hub http://127.0.0.1:9083
aimarket invoke demo-hello/greet@v1 --input '{"name":"dev"}'

Full walkthrough (20 languages): ARGUS developer guide Β· supply security Β· example in examples/hello-capability/.

Docker

Production (this monorepo): always redeploy Hub from repo root:

./scripts/deploy_hub.sh
# or full fleet: ./scripts/deploy_ecosystem.sh

See docs/deploy-ecosystem.md. Do not use cd aimarket-hub && docker compose up for production redeploy (wrong build context).

Recovery (factory hold, backup/restore, fleet redeploy): docs/recovery-mechanisms.md in the factory monorepo.

Manual build (same as deploy script):

docker build -f aimarket-hub/Dockerfile -t modelmarket-hub .
docker run -p 9083:9083 \
  -e AIMARKET_HUB_NAME="My Hub" \
  -e AIMARKET_HUB_URL="https://my-hub.example.com" \
  -e AIMARKET_PAYMENT_RECIPIENT="0xYourWallet" \
  modelmarket-hub

Core API

Method

Path

Description

GET

/.well-known/ai-market.json

Root discovery β€” chain, token, peers, signer key

GET

/.well-known/mcp/server-card.json

MCP server card (SEP-1649 / Smithery fallback)

GET

/llms.txt

Short agent-readable Hub + MCP door

GET / POST

/mcp

Hosted MCP β€” GET info; POST Streamable-HTTP (market_search, market_invoke)

GET

/.well-known/agent-card.json

A2A 1.0 Agent Card (cacheable + ETag)

POST

/a2a

A2A 1.0 JSON-RPC: marketplace search, paid invoke as Tasks, GetTask/ListTasks/CancelTask (docs/a2a.md)

GET

/ai-market/v2/manifest

Hybrid Ed25519 + ML-DSA-65 capability catalog when PQC is enabled

GET

/ai-market/v2/search

NL federated search (intent, budget, category)

POST

/ai-market/v2/supply/stake

Deposit publisher stake (unlock community publish)

POST

/ai-market/v2/supply/register

Publish community capability + invoke_url

POST

/ai-market/v2/invoke

Invoke capability (plugin hooks, safety gate)

POST

/ai-market/v2/receipts/verify

Verify a receipt against this Hub's pinned live Ed25519 + ML-DSA-65 identities

POST

/ai-market/v2/channel/open

Open pre-funded payment channel

POST

/ai-market/v2/channel/close

Close channel β€” settle + refund remainder

POST

/ai-market/v2/federation/announce

Peer hub announcement

GET

/ai-market/v2/federation/peers

Known peers + trust scores + pin status (key_mismatch)

GET

/ai-market/v2/federation/assay

Last sandbox scorecard (pass auto-admits by default)

POST

/ai-market/v2/federation/assay

Admin: re-run SSRF / signature / sandbox assay

POST

/ai-market/v2/federation/crawl

Trigger BFS crawl of seed peers

POST

/ai-market/v2/federation/peers/approve

Admin: toggle peer trusted (anti-TOFU)

POST

/ai-market/v2/federation/peers/repin

Admin: rotate a sticky peer pin after legitimate key change

GET

/ai-market/v2/plugins

Loaded plugin catalog

GET

/ai-market/v2/reputation/{hub_url}

Trust score breakdown

GET

/ai-market/v2/stats/live

Real-time invocation feed

Authorization. Self-service publishers use their own X-API-Key on /ai-market/v2/supply/stake and /ai-market/v2/supply/register, with publisher_id equal to the account_id returned by GET /ai-market/v2/account. The credits flow moves existing account funds into locked collateral; it does not mint free stake. An operator's mandate policy may require additional authorization. Confirm the live policy before funding. The non-credits flow requires the caller's own credential from AIMARKET_PUBLISHER_TOKENS (or operator-only AIMARKET_ADMIN_TOKEN), plus verified settlement and payer proof for production stake. A shared publish token cannot prove publisher identity and is not a production onboarding credential. /self-bond/slash and settlement/federation routes are admin-only. See publication steps and supply security.

OpenAPI: /docs (FastAPI's default β€” there is no AIMARKET_OPENAPI switch; put the hub behind your proxy if the schema should not be public). Full spec: ../aimarket-protocol/spec.md


Invoke lifecycle

Standard consumer flow (implemented by aimarket_agent):

sequenceDiagram
  autonumber
  participant Client
  participant Hub as AIMarket Hub
  participant Plugins
  participant Target as Provider / local invoke

  Client->>Hub: search(intent, budget)
  Hub-->>Client: plan[]

  Client->>Hub: channel/open(deposit_usd)
  Hub-->>Client: channel_id

  Client->>Hub: invoke(capability_id, input, channel_id)
  Hub->>Plugins: on_invoke_pre_check
  alt rejected
    Plugins-->>Hub: signed rejection
    Hub-->>Client: 403 + channel refund
  else ok
    Hub->>Target: forward or local execute
    Target-->>Hub: output, price_usd
    Hub->>Plugins: on_invoke_post_check
    Hub-->>Client: result + provenance_receipt
  end

  Client->>Hub: channel/close(channel_id)
  Hub-->>Client: settlement + unused balance

A call that arrives without the fields the capability needs is incomplete input, not a provider miss and not a refusal. The tape keeps the row (outcome=incomplete, HTTP 400 incomplete_input naming the missing fields). refused is a policy decision (quota, plan, block). Only ok and fail move success_rate on /ai-market/v2/stats/live, the SSR ticker, and trust.


Plugin ecosystem

Plugins register via aimarket.plugins entry points (plugin.py). Each ships README + docs/ (value.md, user-guide.md, sdk-integration.md, user-cases.md).

Regenerate docs: python3 scripts/bootstrap_hub_plugin_docs.py Β· value text: python3 scripts/bootstrap_product_value.py

flowchart LR
  INV["POST /invoke"] --> PRE["pre-check"]
  PRE --> S["aimarket-safety"]
  PRE --> Z["aimarket-zk"]
  PRE --> PR["aimarket-promo"]
  PRE --> RUN["Execute"]
  RUN --> POST["post-check"]
  POST --> PV["aimarket-provenance"]
  POST --> T["aimarket-tee"]
  POST --> R["aimarket-reputation"]
  POST --> OUT["Response"]

Plugin

Category

One-line value

aimarket-provenance

compliance

Cryptographic receipt per AI output

aimarket-safety

security

Block jailbreak / injection before billing

aimarket-reputation

reputation

Stake-backed trust scores

aimarket-channels

infrastructure

Off-chain ledger, on-chain settlement

aimarket-tee

security

Hardware attestation (Nitro / TDX)

aimarket-auction

monetization

Spot bidding for scarce slots

aimarket-personas

tooling

Buyer-friendly agent personas

aimarket-streaming

monetization

SSE + per-token micro-billing

aimarket-nft

monetization

Transferable prepaid credit NFTs

aimarket-mcp-packager

tooling

MCP bundle for Claude Desktop

aimarket-orchestrator

monetization

NL task β†’ capability chain planner

aimarket-data-cap

monetization

Private corpus β†’ paid search

aimarket-promo

monetization

Signed time-locked discounts

aimarket-dataset

tooling

Weekly anonymized demand corpus

aimarket-zk

security

ZK proofs without revealing input


Federation

Hubs discover each other without a central registry:

flowchart TB
  SEED["AIMARKET_SEED_LIST<br/>.well-known URLs"] --> CRAWL["crawler.py BFS"]
  CRAWL --> MANIFEST["Fetch signed manifests"]
  MANIFEST --> INDEX["database.py"]
  INDEX --> SEARCH["Unified federated search"]

  HUB_A["Hub A"] <-->|announce / peers| HUB_B["Hub B"]
  CRAWL --> HUB_A
  CRAWL --> HUB_B

  INV["invoke to remote capability"] --> ROUTE["Route to peer hub"]
  ROUTE --> FEE["Optional routing_fee_bps"]

Observation gossip β€” seeing every new Hub

Hub addresses are always accepted through two public observation doors, and both lead to quarantine:

  • POST /federation/announce accepts an unauthenticated announcement;

  • a hub that crawls this one identifies itself with X-AIMarket-Crawler and is recorded from that alone β€” so an operator can finally see who reads them.

In both cases the peer lands status=pending, trusted=false. Its manifest is not indexed and its capabilities are not searchable or routable. Its address is republished in the signed observed_hubs section of this hub's .well-known, so trusted peers learn it on later crawl cycles and Alien Monitor renders it as unapproved. With AIMARKET_FEDERATION_PREVIEW_CAPS=1 its manifest is fetched and signature-verified into a separate preview table so the operator can see what it offers before deciding β€” a table no search, routing or invoke path reads.

Observation changes who is visible. Trust is not granted on the knock: the peer lands pending. A sandbox assay then runs in the background (and again on crawl cycles). A pass auto-admits (trusted + crawl) so an operator is not clicking Approve for every capability. Fail and review stay at /operator.

The assay scores the live invoke, never brochure text. Names and descriptions are stripped before any model sees evidence. An optional LLM judge is veto-only (AIMARKET_FEDERATION_JUDGE_URL) β€” it cannot mint a pass from marketing copy. AIMARKET_FEDERATION_ASSAY_LLM is ignored if set. Factory analog: product_automated_verify (score running artifacts, not listings).

Variable

Default

Effect

AIMARKET_FEDERATION_GOSSIP_MAX_OBSERVED

2000

Resource/fan-out bound for quarantined observed addresses

AIMARKET_FEDERATION_OPEN

0

Legacy switch for richer preview/admission behaviour; visibility is always on

AIMARKET_FEDERATION_PREVIEW_CAPS

1

Signature-verified preview of a pending peer's catalogue

AIMARKET_FEDERATION_PREVIEW_MAX_CAPS

25

Per-peer preview cap

AIMARKET_FEDERATION_ASSAY

1

Post-quarantine sandbox assay

AIMARKET_FEDERATION_ASSAY_SANDBOX

1

Probe one public free capability (receipt must match advertised key)

AIMARKET_FEDERATION_AUTO_ADMIT

1

A pass sets trusted only with a judge token. Alias: AIMARKET_FEDERATION_ASSAY_AUTO_TRUST

AIMARKET_FEDERATION_JUDGE_URL

OpenRouter chat if a key exists

OpenAI-compatible veto on sandbox evidence

AIMARKET_FEDERATION_JUDGE_KEY

OPENROUTER_API_KEY fallback

No key β†’ manual Approve only

AIMARKET_FEDERATION_JUDGE_MODEL

minimax/minimax-m3

MiniMax, same as the rest of the fleet

AIMARKET_FEDERATION_JUDGE_REQUIRED

0

If 1, a judge error blocks auto-admit (also implied when auto-admit + key)

AIMARKET_FEDERATION_ASSAY_REQUIRE

0

If 1, human Approve refuses unless last assay is pass

Join path: docs/join-the-federation.md Β· internals: docs/federation-admission.md (ENΒ·RUΒ·ESΒ·FRΒ·ZH).

Surfaces: GET /federation/peers (pending array) and GET /federation/preview?url=… are public β€” anyone can see who knocked and what they claim to offer; GET /federation/inbound is admin-only (who crawled us, no client IPs stored), and DELETE /federation/peers?url=… lets an operator reject a pending peer and drop its preview rows. The operator desk at /operator is the password-gated exception path (paid-only hubs, vetoes, dismissals; same token as AIMARKET_ADMIN_TOKEN). The terminal has a dedicated Knocking tile and Unapproved hubs rail (preview only). Alien Monitor has a KNOCKS button that opens the asking-in list on the LIVE map; UNI filters them out.

A2A 1.0: search and paid invoke

The Hub exposes an actual A2A 1.0 JSON-RPC agent at /a2a, discovered through /.well-known/agent-card.json. It advertises two skills, both narrow and real. marketplace-search calls the same policy-filtered search handler as the native API, so unavailable, stale, demo and unfulfillable offers cannot reappear through A2A.

curl -sS http://localhost:9083/a2a \
  -H 'Content-Type: application/json' \
  -H 'A2A-Version: 1.0' \
  --data '{"jsonrpc":"2.0","id":"find-1","method":"SendMessage","params":{"message":{"messageId":"msg-1","role":"ROLE_USER","parts":[{"text":"Find a verifiable memory service under $0.10"}]}}}' \
  | jq '.result.message.parts[1].data.matches'

Search answers with a direct A2A Message. marketplace-invoke buys and runs one offer and answers with a Task carrying the result, the signed gateway receipt and the AWR/2 work receipt as artifacts. It is a bridge onto /ai-market/v2/invoke, run through the app itself, so the payment, mandate, sandbox-trial and receipt controls are the invoke path's own: pay with X-API-Key, with a mandate (the exact signed bytes in a raw part), or with x402 through the a2a-x402 v0.2 flow β€” whose payload must carry the settle txHash, because this hub verifies transfers and never settles them. A retried message never pays twice. Tasks are kept in the hub database (migration 036), scoped to the account, mandate or caller that created them, and never hold a key, a proof, a payment payload or β€” once finished β€” the input. Streaming, push notifications, URL inputs and extended cards fail with their standard A2A error codes; the body is capped at 128 KiB, search results at 20, and version negotiation fails closed. Wire examples, the task-state table and the one x402 profile deviation: docs/a2a.md.

Retrying a paid call

A timed-out POST /ai-market/v2/invoke paid with X-API-Key (or a ledger channel and its secret) may or may not have run. Send an Idempotency-Key (1–128 of A-Z a-z 0-9 . _ : -) and retry with the same key and body: the hub answers with the first result (X-AIMarket-Idempotency: replayed) instead of running and charging again. The same key with another body is 422 idempotency_key_reused; while the first request still runs it is 409 idempotency_in_progress, and one that never finished is 409 idempotency_outcome_unknown after 15 minutes (the ledger at GET /ai-market/v2/account says whether it was charged). Answers are kept 24 hours under a hash of the credential; refusals that come before any money moves (401, 403, 404, 413, 415, 422, 429, 503) are not kept. x402 and escrow-channel payments are single-use already and answer X-AIMarket-Idempotency: not-applicable.

x402 interoperability

The hub speaks x402 β€” the machine-payment standard that became a Linux Foundation project in July 2026 β€” alongside its own payment surface:

  • a 402 from this hub's own payment gate carries the x402 V2 payload in a base64 PAYMENT-REQUIRED header (a peer's 402 passed through verbatim is left untouched β€” its terms are the peer's, not ours), and the V1 accepts array merged additively into the body, so both generations of client can read it and existing consumers see no change;

  • GET /discovery/resources serves a Bazaar-compatible index of priced capabilities β€” the same envelope every official x402 SDK deserializes, so pointing a facilitator client at this hub enumerates its catalogue with no code changes;

  • a PAYMENT-SIGNATURE / X-Payment is accepted on the market rail: the buyer sends USDC on-chain to the listing's payout_address, and the hub verifies that transfer (aimarket_hub/settle.py). It never custodies the funds. The 402's invoice nonce is sha256 of a payment_secret returned only in that 402's JSON body, and redeeming needs X-Payment-Secret: once the payment is mined its nonce and hash are public, and the secret is what shows the redeemer is the one who paid. Only the transfer the buyer's own authorization moved pays for the call. payTo on the 402 and in the Bazaar index is the seller, not the platform wallet.

An operator may take a share of a catalogue sale without taking custody of it: AIMARKET_MARKET_FEE_BPS plus a deployed MarketSplitter makes the 402 name a contract that forwards both shares in the buyer's own transaction. The hub then requires both Transfer legs β€” seller and operator β€” rather than trusting that the payment was routed through any particular address. Off by default.

Where the money goes, rail by rail, with diagrams: docs/money-rails.md.

See aimarket_hub/settle.py and aimarket_hub/x402.py.

ERC-8004 identity

The public hub at modelmarket.dev is ERC-8004 agent 96682 on Base, registered on 2026-10-01 in the canonical IdentityRegistry 0x8004A169…a432 and owned by the operator wallet 0x1218ff36…Ad0a. The hub names that agentId in the erc8004 block of its signed /.well-known/ai-market.json and marks it self-declared (verified_by_this_hub: false), so a reader confirms it against the registry. Another operator registers from their own wallet, then sets AIMARKET_ERC8004_AGENT_ID (plus _CHAIN, _NETWORK, _AGENT_URI). Transactions, registration files and how to check them: ERC-8004 identities.

Full walkthrough: ../docs/join-the-federation.md Β· Normative text: protocol spec Β§2.4–2.6.

Trust scoring: trust.py Β· Signing: signing.py

Peer key pin / mismatch / admin re-pin (ENΒ·RUΒ·ESΒ·FRΒ·ZH): docs/federation-peer-keys.md

Deep dive: ../docs/FEDERATION_HUB_REPORT.md


Payments

sequenceDiagram
  participant User
  participant Hub
  participant Chain as Base (USDC)

  User->>Chain: deposit USDC
  User->>Hub: channel/open(deposit_usd)
  Note over Hub: Ledger tracks balance off-chain

  loop each invoke
    User->>Hub: invoke + X-Payment-Channel
    Hub->>Hub: decrement channel balance
  end

  User->>Hub: channel/close
  Hub->>Chain: settle spent + refund remainder

Field

Default

Notes

Chain

Base (L2)

AIMARKET_PAYMENT_CHAIN

Token

USDC

AIMARKET_PAYMENT_TOKEN β€” the ledger's default; the advertised catalog is AIMARKET_PAYMENT_TOKENS (USDT,USDC,ETH)

Recipient

env required

AIMARKET_PAYMENT_RECIPIENT

Protocol principle: no custody β€” channels are on-chain constructs; hub holds ledger state only.

Deposit authorization. In production (AIFACTORY_PROD=1, verify stub off) a channel is credited only by a deposit that is verified on-chain, bound to the wallet that actually paid, single-use (consumed_deposits), and proven by an EIP-191 signature from the paying wallet over payer_proof_challenge(...) β€” the deposit tx hash is public, so without that proof the channel secret would go to whoever quotes it first. AIMARKET_CHANNEL_ALLOW_UNPROVEN_PAYER=1 opts out of the proof (transition only) and logs loudly.


Pay-on-Verified

Opt-in quality escrow on invoke. With a verify block on the invoke body the channel debit becomes a hold; Metis judges the delivered output against the buyer's stated intent in the background β€” pass captures the hold, fail refunds it with a signed rejection receipt. The buyer keeps the output either way; only the money outcome changes.

What

verify: { requested, intent, mode, wait } on POST /ai-market/v2/invoke β†’ hold_channel β†’ Metis verdict β†’ capture / release

Why

Providers are paid for verified work, not for responding; every verdict emits a reputation event

Lookup

GET /ai-market/v2/verification/{nonce} (nonce = receipt nonce)

Deep dive

docs/pay-on-verified.md Β· Cross-component doc


Agent mandates and subcontracting

An owner's signed spending limits for an agent key, and purchases a provider makes inside a buyer's call. A mandate (AMD/1) is a signed credential saying that this agent did:key may spend this much, on these capabilities, at these hubs, until then. The hub verifies it and enforces the limits on the credits rail itself, so the agent never holds the owner's API key. Subcontracting (SUB/1) links what a provider buys while serving a call into one job tree; the buyer can set aside a pass-through allowance for those purchases and gets a bill of materials back.

Mandate

POST /ai-market/v2/mandates registers one; X-AIMarket-Mandate + X-AIMarket-Mandate-Proof pay with it; 402 mandate_limit names the limit reached; POST /ai-market/v2/mandates/revoke revokes it and everything re-delegated from it

Subcontracting

every provider the hub executes receives X-AIMarket-Job (plus X-AIMarket-Job-Grant with an allowance); "subcontract": {"allowance_usd": …, "max_depth": …} on the root invoke; the bill in subcontracting, and later at GET /ai-market/v2/jobs/{job_id}

Needs

AIMARKET_CREDITS_ENABLED=1, and AIMARKET_HUB_URL set to the public URL mandates name in audience

Deep dive

docs/mandates.md Β· Protocol: AMD/1 and SUB/1

import httpx
from aimarket_agent import AIMarketAgent, AgentKey, Mandate, issue_mandate

HUB = "https://modelmarket.dev"
agent_key = AgentKey.generate()
# The owner signs; the owner's DID must already be linked to a credit account on this hub.
doc = issue_mandate(owner_key, agent_key.did, audience=[HUB], scope=["gaia.*"],
                    per_call_usd=0.02, per_day_usd=1.00)
httpx.post(f"{HUB}/ai-market/v2/mandates", json=doc)            # register
client = AIMarketAgent(HUB, mandate=Mandate(document=doc, key=agent_key, hub_origin=HUB))
client.invoke_single("gaia.gateway", "gaia.weather.read@v1", {"latitude": 60.17, "longitude": 24.94})

Configuration

Every default below is the value the code falls back to today; where a default is derived from another variable, the rule is spelled out rather than a number.

Core

Variable

Default

Description

AIMARKET_HUB_NAME

AIMarket Hub

Display name in manifests

AIMARKET_HUB_URL

http://localhost:9083

Public URL (receipts, well-known)

AIFACTORY_PROD

β€”

1 puts every money gate on the production path (on-chain verification required, fail-closed defaults)

AIFACTORY_CRYPTO_ENABLED

0

Master crypto switch: off β‡’ channels/escrow/NFT disabled, capabilities served free; signing and sandbox trials keep working

AIMARKET_PAYMENT_CHAIN

base

Settlement chain (AIMARKET_PAYMENT_CHAINS for the advertised list)

AIMARKET_PAYMENT_TOKEN

USDC

Ledger settlement token (AIMARKET_PAYMENT_TOKENS advertises USDT,USDC,ETH)

AIMARKET_PAYMENT_RECIPIENT

β€”

Required in production β€” the wallet deposits must pay

AIMARKET_CRAWL_INTERVAL_S

3600

Federation crawl period

AIMARKET_CATALOG_MAX_STALE_S

21600

Stop offering a peer's rows after this many seconds without a successful crawl; rows stay stored for recovery and can be inspected with include_stale=true

AIMARKET_ROUTING_FEE_BPS

100

Routing fee (1% = 100 bps)

AIMARKET_MIN_TRUST_SCORE

0.3

Baseline trust floor (also the discover-gate default below)

AIMARKET_SEED_LIST

committed federation_seeds.json

Comma-separated peer .well-known URLs; unset falls back to the shipped seed file, not to "no seeds"

AIMARKET_SEED_PUBKEYS

committed seed public_key fields

{url:key} JSON or url=key,… β€” trusted-on-first-contact only; rotating an existing DB pin requires POST /federation/peers/repin

AIMARKET_PLUGIN_WHITELIST

β€”

Restrict loaded plugins

AIMARKET_ADMIN_TOKEN

β€”

Operator token. Unset β‡’ every admin route refuses (503), fail-closed

AIMARKET_PUBLISH_TOKEN

β€”

Legacy shared token; not sufficient for production publisher identity

AIMARKET_PUBLISHER_TOKENS

β€”

pub-a:secretA,pub-b:secretB β€” per-publisher credentials for the stake/bond routes (see Security)

AIMARKET_AGENT_TOKENS

β€”

agent-a:secretA,… β€” per-agent credentials for self-bond/reporting and wallet-bound lottery entitlements

AIMARKET_CORS_ORIGINS

β€”

Comma-separated allowlist. Empty means no cross-origin access (a * default enabled drive-by CSRF)

Databases

Variable

Default

Description

DATABASE_URL

SQLite files

PostgreSQL for production β€” when set, every subsystem shares it; mandatory when AIFACTORY_PROD=1

AIMARKET_DB_PATH

data/hub.db

The hub index database. It no longer overrides a path a subsystem passes explicitly (that silently aliased channels.db and provenance.db onto the hub file); a subsystem that must share the hub file now points its own variable at it

AIMARKET_CHANNELS_DB_PATH

data/channels.db

Payment-channel ledger (separate file from the hub index)

AIMARKET_VERIFY_SETTLEMENTS_DB_PATH

AIMARKET_DB_PATH, else data/hub.db

Where verified_settlements lives β€” the orphaned-hold reaper reads it and refuses to release anything it cannot read

Moving an existing production Hub from SQLite to PostgreSQL

The standalone Hub and sandbox trial ledger fail at startup when AIFACTORY_PROD=1 unless DATABASE_URL uses postgresql:// (or postgres://). Production can therefore no longer silently create a fresh SQLite fallback when an environment file is missing.

Production deployments should set DATABASE_URL and keep the running Hub, channel ledger, sandbox quota, settlement records and provenance in one PostgreSQL database. The migration tool opens every SQLite source read-only, inserts idempotently, validates row counts and then advances every PostgreSQL serial sequence; skipping the last step makes the first new write collide with an imported id.

Run the cutover with the Hub stopped and start from an empty target database:

export TARGET_DATABASE_URL='postgresql:///aimarket_hub'
systemctl stop aimarket-hub

# Initialize every Hub subsystem in the empty target schema.
DATABASE_URL="$TARGET_DATABASE_URL" \
  AIMARKET_SIGNING_KEY_PATH=/var/lib/aimarket/hub_signing_key \
  python -c 'from aimarket_hub.api import create_app; create_app()'

python scripts/migrate_sqlite_to_postgres.py \
  --dsn "$TARGET_DATABASE_URL" \
  --sqlite /var/lib/aimarket/hub-federation.db \
  --sqlite /var/lib/aimarket/channels.db \
  --sqlite /var/lib/aimarket/sandbox_trials.db

# Put DATABASE_URL in the service environment, then start and verify before removing nothing.
systemctl start aimarket-hub
curl -fsS http://127.0.0.1:9083/ai-market/v2/health
curl -fsS http://127.0.0.1:9083/ai-market/v2/search?intent=echo

Keep the SQLite files as a rollback snapshot until a PostgreSQL pg_dump has passed an actual restore test. A production backup job must include the PostgreSQL dump and both signing keys (hub_signing_key and hub_signing_key_mldsa); copying the old .db files is not a backup of a Hub that is running on PostgreSQL.

Upgrading past the shared-database aliasing

This is a one-time migration step for any hub that already ran with AIMARKET_DB_PATH set β€” which includes every container here (Dockerfile, Dockerfile.standalone, docker-compose.yml, docker-compose.core.yml all export it).

Until this release the env var overrode the path a subsystem asked for, so the channel ledger (data/channels.db) and the provenance store (data/provenance.db) were created inside the hub file. Now that the explicit argument wins, those subsystems open their own files β€” and on an upgraded deployment those files start empty:

  • the channel ledger loses its open channels and, more seriously, consumed_deposits β€” the table that makes an on-chain deposit single-use. An empty one lets every deposit already spent be replayed into a new funded channel;

  • the provenance store loses its receipts.

The hub logs this at ERROR on startup (the requested file does not exist while the AIMARKET_DB_PATH file does), naming the file the data is still in. Do one of these before serving traffic:

# A. channel ledger β€” keep the shared file, no data moves, pre-upgrade behaviour exactly
export AIMARKET_CHANNELS_DB_PATH="$AIMARKET_DB_PATH"

# B. channel ledger β€” split it out: copy the file with the hub stopped
cp /app/data/hub.db /app/data/channels.db
export AIMARKET_CHANNELS_DB_PATH=/app/data/channels.db

Either way the tables the copy's owner does not use are simply never read. The provenance store has no path variable β€” it always derives provenance.db from the hub database's directory β€” so B is the only option there (cp /app/data/hub.db /app/data/provenance.db); skipping it starts an empty receipt store, which costs an audit trail but no money.

DATABASE_URL deployments are unaffected β€” PostgreSQL was always one shared database.

Channels

Variable

Default

Description

AIMARKET_ALLOW_DEMO_CREDIT

β€”

1 credits a channel without on-chain verification (dev/demo). Outside production, without it, crediting fails closed

AIMARKET_CHANNEL_ALLOW_UNPROVEN_PAYER

0

1 opts OUT of the payer proof-of-control requirement (transition only β€” leaves deposit front-running open)

AIMARKET_CHANNEL_ANON_OPENS_PER_HOUR

200

One shared cap for all wallet-less opens (they are not exempt)

AIMARKET_CHANNEL_ANON_CLOSES_PER_HOUR

600

Same, for closes

AIMARKET_CHANNEL_HOLD_REAP_AFTER_SECS

86400

Release a hold stuck held this long with no live verification; 0 disables the reaper

AIFACTORY_PAYMENT_MIN_CONFIRMATIONS

2

Confirmations a deposit needs before it counts

AIFACTORY_PAYMENT_VERIFY_STUB

0

1 accepts any tx hash β€” development only

Pay-on-Verified

Variable

Default

Description

AIMARKET_VERIFY_ENABLED

1

Pay-on-Verified master switch (per-invoke opt-in still required)

AIMARKET_VERIFY_MIN_PRICE_USD

0.05

Price floor β€” cheaper invokes are never verification-taxed

AIMARKET_VERIFY_SCORE_THRESHOLD

0.7

verify_score needed to capture the hold (a value outside 0.0–1.0 falls back to 0.7)

AIMARKET_VERIFY_COUNCIL_MIN_PRICE_USD

0.50

Route ceiling: council allowed at/above this price, else clamped to fast

AIMARKET_VERIFY_MAX_CONCURRENCY

8

Cap on simultaneous Metis calls across pending settlements

AIMARKET_VERIFY_ATTEMPT_TIMEOUT_S

330

Per-attempt Metis HTTP timeout (> Metis 300 s server cap)

AIMARKET_VERIFY_RETRY_BACKOFF_S

5

Initial transport-retry backoff (exponential, cap 300 s)

AIMARKET_VERIFY_ENGINE_RETRIES

2

Re-runs after an engine-error envelope before policy applies

AIMARKET_VERIFY_MAX_WAIT_S

0

0 = no verdict deadline; >0 bounds resolution via policy

AIMARKET_VERIFY_FAIL_CLOSED

1

Indeterminate outcome β‡’ refund the buyer. Only an explicit 0/false/no/off captures instead; an unrecognised value is a typo and still fails closed

AIMARKET_VERIFY_METIS_URL

http://127.0.0.1:8080

Metis base URL (falls back to METIS_URL)

AIMARKET_VERIFY_METIS_KEY

β€”

Metis bearer key (falls back to METIS_API_KEY)

AIMARKET_VERIFY_VERIFIER_ID

metis.verify@v1

Envelope verifier attribution when a non-Metis verifier serves the slot

AIMARKET_APPEAL_WINDOW_S

0

Appeals (protocol): seconds a genuine paid verdict stays provisional and appealable by the losing party. 0 = off, verdicts move money at once

AIMARKET_APPEAL_METIS_URL

β€”

The appeal court: a second verifier. Unset, or equal to the first-instance URL, keeps appeals off

AIMARKET_APPEAL_METIS_KEY

β€”

Appeal court bearer key

AIMARKET_APPEAL_VERIFIER_ID

metis.appeal@v1

Names the appeal court in envelopes; must differ from AIMARKET_VERIFY_VERIFIER_ID

AIMARKET_APPEAL_BOND_MIN_USD / AIMARKET_APPEAL_BOND_RATE

0.02 / 0.20

Bond = max(min, rate Γ— price), capped at the price

AIMARKET_APPEAL_MAX_WAIT_S

21600

After this the court counts as indeterminate: the first verdict stands, the bond is returned (0 = no limit)

AIMARKET_APPEAL_SWEEP_S

30

Safety-net pass that finalizes expired windows and picks up filed appeals

Supply security (community publishers)

Full model: docs/supply-security.md. A non-finite or non-numeric value in any threshold below is ignored with a warning and the documented default is used β€” a nan threshold would otherwise silently disable the gate it configures.

Variable

Default

Description

AIMARKET_SUPPLY_SECURITY_RELAXED

0

1 = dev bypass: zero minimum stake, no response-signature requirement, no slashing

AIMARKET_SUPPLY_MIN_STAKE_USD

25 in production, else 10 (0 when relaxed)

Stake required to publish

AIMARKET_SUPPLY_PUBLISH_PER_HOUR

5

Publishes per publisher per hour

AIMARKET_SUPPLY_MIN_TRUST_DISCOVER

AIMARKET_MIN_TRUST_SCORE (0.3)

Trust floor to appear in discover

AIMARKET_SUPPLY_MIN_TRUST_INVOKE

0.35

Trust floor to be invoked

AIMARKET_SUPPLY_REQUIRE_RESPONSE_SIG

on iff production and not relaxed

Require an Ed25519 provider response signature

AIMARKET_SUPPLY_MAX_INPUT_KEYS

32

Top-level keys accepted in an invoke input

AIMARKET_SUPPLY_MAX_INPUT_JSON_BYTES

32768

Invoke input size cap

AIMARKET_SUPPLY_PRODUCT_ALLOWLIST

β€”

Comma-separated product_id allowlist

AIMARKET_SUPPLY_SLASH_FAILURE_THRESHOLD

3

Provider faults within the window before stake is slashed

AIMARKET_SUPPLY_SLASH_FAILURE_WINDOW_S

600

Fault window (must be > 0; a non-positive value would disable slashing, so it falls back)

AIMARKET_SUPPLY_SLASH_COOLDOWN_S

3600

At most one failure-driven slash per window; 0 disables the cool-down

AIMARKET_SUPPLY_SLASH_DAILY_CAP_USD

10

Rolling 24 h cap on failure-driven slashing; 0 disables the cap

AIMARKET_SUPPLY_VERIFIED_FAIL_THRESHOLD

3

Paid Metis "failed" verdicts before escalation

AIMARKET_SUPPLY_VERIFIED_FAIL_WINDOW_S

86400

Window for those verdicts

AIMARKET_SUPPLY_VERIFIED_FAIL_MIN_CONSUMERS

2

Distinct PAYING consumers required β€” one buyer's repeated failures are one voice

AIMARKET_SUPPLY_TRUST_GRAPH_MAX_EDGES

1000

Trust-graph bound; truncation is logged with the publisher it affected

AIMARKET_ORACLE_FAMILY_URL

https://oracles.modelmarket.dev/family

LUMEN trust oracle (falls back to ARGUS_ORACLE_FAMILY_URL)


Deployment

Start here: complete production deployment runbook. It is also available in Russian, Spanish, French and Chinese.

The runbook covers immutable commit-pinned releases, unprivileged systemd services, nginx/TLS on a hostname or /hub subpath, same-origin federation discovery and invoke, signed-manifest and real 402 assay evidence, protocol-only federation admission, UFW/Fail2ban/SSH hardening, backups, reboot acceptance, Alien Monitor and SKOPOS enrollment. Do not expose the provider backend or replace the Hub's Ed25519 key during a routine upgrade.

Reference record for the maintained public installation: ../docs/production-modelmarket-dev.md.


Testing & coverage

CI runs on every push (workflow); coverage badge is refreshed from pytest --cov on main.

cd aimarket-hub
pip install -e ".[dev]"
pytest tests/ -q --cov=aimarket_hub

Development

pip install -e ".[dev]"

Key test modules: test_api.py, test_crawler.py, test_plugin_system.py, test_channels.py, test_cross_hub_integration.py

Add a plugin: create package under ../plugins/ with pyproject.toml entry point aimarket.plugins.


Security

  • SSRF protection on federation crawler (crawler.py)

  • Hybrid signed evidence β€” with AIMARKET_PQC=1, manifests, discovery documents, invoke receipts, rejection receipts and Pay-on-Verified verdict envelopes carry Ed25519 and ML-DSA-65 signatures over the same versioned canonical payload. Classical verification remains mandatory; PQ verification fails closed when a PQ signature is present. The public /ai-market/v2/receipts/verify endpoint is pinned to this Hub's persisted keys and accepts no caller-supplied key, preventing self-verifying receipts (signing.py)

  • Safety gate on every invoke (safety_gate.py)

  • Verified, single-use stake deposits β€” in production every stake credit needs an on-chain deposit that pays the platform recipient, and the hash is burned by an atomic claim before the credit, so one deposit can never fund two publishers even under concurrent requests. The claim is keyed on the canonical transaction id (an EVM hash is case-insensitive at the JSON-RPC layer, so 0xAB… and 0xab… are one deposit, not two) (supply_security.py)

  • Residual β€” stake deposits are not payer-bound. The stake verifier answers "did somebody pay the platform?", not "did this publisher pay", so whoever submits a matching hash first gets the credit. Binding it needs a publisherβ†’wallet record the stake ledger does not yet have; channel deposits are already bound (see the entry below). Until then, treat a stake deposit hash as a bearer secret and submit it before it is public

  • Single-use channel deposits + payer proof β€” a verified deposit funds exactly one channel and only for the wallet that signed for it (channels.py)

  • Stake mutation is per-subject, slashing is operator-only β€” a shared token can neither credit a stranger's stake nor burn a rival's bond (api.py)

  • Vulnerability reports: SECURITY.md β†’ alexar76@rambler.ru


Project

Relationship

AICOM / AI-Factory

Ships products β†’ hub index

aimarket-protocol

Normative v2 spec

aimarket-sdks

Client SDKs (Dart alpha)

aimarket-widget

Embeddable UI

oracles

Verifiable math capabilities β€” randomness, VDF, consensus, reputation (listed on hub)

desktop-integrations

8 Flutter consumer apps

Ecosystem architecture

Full monorepo diagram

dioscuri

Twin community agents β€” MNEMOSYNE Q&A


Community

The DIOSCURI twins answer questions from synced GitHub docs.

Channel

Twin

Best for

Discord

Pollux

Help, ideas, show-and-tell

Telegram

Castor

Releases, digests, quick news

Ecosystem map: Alien Monitor Β· AICOM


License

Apache-2.0 β€” see LICENSE. Maintainers: CONTRIBUTORS.md.

Available Tools

8 tools
market_invokeA

Invoke a capability found via market_search. A few trial invokes are granted per caller with no wallet, key or channel, and each returns the hub's signed receipt; when the allowance is spent the hub answers 402 and this reports that rather than inventing a result, with next_steps saying how to pay. Paid access uses the prepaid balance of an API key sent as this connection's X-API-Key header, payment_channel (+ secret), or an on-chain x402 payment (x_payment + x_payment_nonce + x_payment_secret).

ParametersJSON Schema
NameRequiredDescriptionDefault
inputNoInput object for the capability; {} when it takes none.
x_paymentNox402 seller-direct payment: the hash of the USDC transfer you broadcast for the 402's invoice (see next_steps).
product_idYesThe product_id from market_search.
source_hubNoThe source_hub from market_search, when it shows one. Required for federated capabilities β€” most of the catalogue; omitting it makes the hub look for the capability locally and answer 404.
capability_idYesThe exact capability_id from market_search.
max_price_usdNoAtomic total-price ceiling. Copy the selected search result's max_price_usd here; the Hub returns price_limit_exceeded before work or payment if the route was repriced.
payment_channelNo
x_payment_nonceNoThe invoice nonce from the 402 that x_payment pays.
x_payment_secretNoThe payment_secret from that 402. The nonce and the transaction are public once mined; the secret is what shows you are the one who paid.
include_full_receiptNoReturn the hub's raw response, including the full hybrid Ed25519 + ML-DSA-65 receipt signature (about 7 KB).
payment_authorizationNo
payment_channel_secretNo

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so well: it discloses the trial allowance, that every call returns the hub's signed receipt, that the hub answers 402 once allowance is spent, and that the tool reports the 402 honestly rather than fabricating a result. It also names all three paid-access routes (X-API-Key header, payment_channel + secret, on-chain x402), covering auth and rate-limit behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core purpose and free of filler; every clause about trials, 402 handling, and payment routes is substantive. It is one dense paragraph where a short list of payment modes would scan faster, but nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 12-parameter, no-annotation, no-output-schema tool, the description covers the essential behaviors: trial limits, 402 fallback, receipt signing, and all payment paths. The per-parameter details are mostly handled by the 75%-covered schema, so the remaining gap is minor.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is already 75%, so the baseline is 3. The description adds genuine value by grouping parameters into coherent payment modes (X-API-Key, payment_channel + secret, x_payment + nonce + secret), clarifying which params travel together in a way per-field schema text does not.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (invoke) and resource (a capability found via market_search), making the search-then-invoke workflow unambiguous. It is cleanly distinguishable from the sibling market_search, which finds rather than calls capabilities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Makes clear this is the call that follows market_search and explains the trial-then-paid escalation path, including what happens when the free allowance runs out (402 + next_steps). It does not address when to prefer pipeline_invoke over this tool, leaving one sibling relationship unaddressed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

pipeline_invokeA

Execute or continue the SAME prepared graph. Submit authorizations when the signed quote enables gas_sponsorship, otherwise buyer-signed transactions; none for free steps. Never submit both payment modes. Retain run_id/access_token and the exact bundle. Pending is not failure; repeat the same run or read pipeline_status. Never create a replacement purchase after a lost response.

ParametersJSON Schema
NameRequiredDescriptionDefault
run_idYes
access_tokenYes
transactionsNo
authorizationsNo

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so well: it declares payment-mode mutual exclusivity, that 'pending is not failure', that the same run should be repeated rather than rerun from scratch, and that a replacement purchase must not be created after a lost response. This is exactly the idempotency/retry context an agent needs. It stops short of covering error surfaces, rate limits, or what a successful response yields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Telegraphic and front-loaded: the core action leads, followed by param selection rules and then retry safety. Nearly every clause carries an instruction, though the staccato style makes the workflow rules slightly harder to parse on first read than plain prose would.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-phase payment workflow with no annotations and no output schema, the description supplies the mode-selection logic, the retry/pending semantics, and state retention guidance. The main remaining gap is what the caller should expect back and how failures other than 'pending' manifest, but the critical call-time decisions are covered.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate, and it does for the critical ambiguity: it explains when to populate authorizations versus transactions versus neither. run_id and access_token roles are only loosely implied by 'retain run_id/access_token', but the hardest parameter decision is fully resolved.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Execute or continue the SAME prepared graph') and implicitly distinguishes itself from pipeline_prepare (which prepares the graph) and pipeline_status (which it names as the read-only alternative). The word 'SAME' and 'prepared' make the relationship to siblings inferable, though the description never spells out what the graph actually accomplishes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes the agent: submit authorizations when the signed quote enables gas_sponsorship, otherwise buyer-signed transactions, and none for free steps, with a hard exclusion ('Never submit both payment modes'). It also names pipeline_status as the fallback when awaiting a pending result, giving both when-to-use and when-to-use-something-else.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

pipeline_prepareA

Validate a graph and return a signed quote and payment offers. No work or payment. Free steps require no wallet; paid steps require buyer wallet address. Use gas_mode=required to demand gas sponsorship or auto to prefer it. Inspect ready/blockers and gas_sponsorship. Sign offers LOCALLY, never provide a private key. Then call pipeline_invoke.

ParametersJSON Schema
NameRequiredDescriptionDefault
nodesYes
walletNo0x0000000000000000000000000000000000000000
gas_modeNobuyer
max_budget_usdNo

TDQS

A4.4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does so well: it discloses that no work or payment occurs (dry-run nature), the wallet requirement branching on free vs paid steps, the gas_mode semantics, the output fields to inspect (ready/blockers, gas_sponsorship), and a security constraint (sign offers locally, never supply a private key).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action and outputs, then prerequisites, then the security note, then the follow-up tool. Six short sentences with no filler; every one adds an actionable fact.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description compensates by naming the response fields to inspect (ready/blockers, gas_sponsorship). Combined with the wallet/gas_mode rules and the next-step pointer, an agent has enough to call it; only max_budget_usd semantics remain unaddressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It meaningfully explains wallet (only needed for paid steps) and gas_mode (required vs auto), and implies nodes via 'validate a graph', but max_budget_usd is never mentioned and node structure/limits are left entirely to the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (validate) and resource (a graph) plus the concrete outputs (signed quote, payment offers). It explicitly positions the tool relative to the sibling pipeline_invoke ('Then call pipeline_invoke'), so an agent can separate the two without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear sequencing guidance ('Then call pipeline_invoke') and conditional usage for gas_mode=required vs auto. It stops short of stating when not to use it or what to do on failures, but the context for selecting it is unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

pipeline_refundA

Submit the original seller's refund authorization or resume the SAME refund. The recipient is the original buyer, amount is the paid step price, and the sponsor pays gas. Preserve the exact authorization after a lost response. Returns a separately signed credit note; the original bill is unchanged.

ParametersJSON Schema
NameRequiredDescriptionDefault
run_idYes
step_idYes
access_tokenYes
authorizationNo

TDQS

A3.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does substantial work: it names the recipient (original buyer), the amount (paid step price), the gas payer (sponsor), idempotent resume behavior via authorization preservation, and the return (separately signed credit note with the original bill unchanged). Missing only explicit permission/auth requirements and rate/limit behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense sentences, front-loaded with the primary action, no filler. Every sentence adds information (action, money flow, idempotency, return).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations and no output schema, the description supplies the key behavioral context an agent needs: who pays, who receives, idempotency handling, and the return format. The remaining gap is parameter-level documentation for the three required identifiers.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It clarifies the 'authorization' parameter's purpose (preserving/resuming after a lost response), which is genuinely useful, but leaves run_id, step_id, and access_token undocumented with no format hints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Submit the original seller's refund authorization or resume the SAME refund.' This clearly distinguishes it from prepare/status siblings, though it does not name those siblings explicitly. An agent can tell this is the execution/lifecycle step for a refund.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies two usage modes (submitting an authorization vs. resuming an existing refund) and hints at the lost-response recovery scenario. However, it never names pipeline_refund_prepare or pipeline_refund_status as alternatives or states the preconditions that select this tool over them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

pipeline_refund_prepareA

Prepare a cash refund for a verified paid step of a finished pipeline. This moves no money. The original seller must approve the exact EIP-3009 authorization locally; never send a private key. Refunds require seller cooperation and a configured gas sponsor.

ParametersJSON Schema
NameRequiredDescriptionDefault
run_idYes
step_idYes
access_tokenYes

TDQS

A3.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does disclose meaningful traits: 'This moves no money' (despite the refund name) and the hard security constraint 'never send a private key.' It still omits what state preparation creates (reservation, expiry, idempotency) and what the seller-approval flow requires of the caller afterward.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four short sentences, front-loaded with the core action, each carrying distinct information (what it does, that no money moves, the auth requirement, the prerequisites). No filler or restatement of the name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers the safety profile and preconditions well, which is the critical part, but with no output schema and no annotations it should also signal what preparation yields and how to proceed (the follow-on pipeline_refund call). That workflow context is absent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description says nothing about run_id, step_id, or access_token. The run_id pattern ('^paid_...') in the schema hints at format, but the description does not explain that run_id identifies the finished pipeline run, that step_id must be a verified paid step, or how access_token relates to the caller.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource+scope: 'Prepare a cash refund for a verified paid step of a finished pipeline,' and clarifies it is a preparation step, not the execution. It does not, however, name the sibling it is distinguished from (pipeline_refund / pipeline_refund_status), so an agent must infer the routing rather than being told.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides real preconditions ('seller must approve the authorization locally', 'refunds require seller cooperation and a configured gas sponsor'), which implies when the call can succeed. But it never states when to use this versus pipeline_refund or pipeline_refund_status, nor the ordering of the refund workflow, so the routing guidance is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

pipeline_refund_statusB

Read the saved cash-refund status and signed credit note without broadcasting a transaction. No private key or RPC is required. Pending means resume the same refund, never send a replacement transfer.

ParametersJSON Schema
NameRequiredDescriptionDefault
run_idYes
step_idYes
access_tokenYes

TDQS

B3.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that no private key or RPC is required and that no transaction is broadcast, and it clarifies the meaning of a pending status. It still does not mention required permissions or the access_token, but it covers the most important non-obvious behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, each earning its place. The core read-only nature is front-loaded, followed by a useful operational constraint and a critical pending-status instruction. There is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with three required, undocumented parameters, no output schema, and no annotations, the description is incomplete. It covers key behavioral points about non-broadcasting and pending handling, but it says nothing about input meaning, authentication details, or what the signed credit note return looks like.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and the description does not mention any of the three required parameters (run_id, step_id, access_token). It adds no meaning, format expectations, or constraints beyond the bare schema names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Read') and resource ('saved cash-refund status and signed credit note') and explicitly distinguishes this from a broadcasting operation ('without broadcasting a transaction'). It does not name a sibling tool directly, but the contrast with pipeline_refund is clear enough.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives important in-state guidance ('Pending means resume the same refund, never send a replacement transfer'), which tells the agent what to do when status is pending. However, it does not state when to use this tool versus pipeline_refund, pipeline_refund_prepare, or pipeline_status, so broader usage context remains implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

pipeline_statusA

Read an existing pipeline, including cached signed result, without broadcasting payments or invoking providers. No wallet signer or blockchain RPC required. Inspect recovery.action for unresolved work.

ParametersJSON Schema
NameRequiredDescriptionDefault
run_idYes
access_tokenYes

TDQS

A3.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the safety profile itself and does it well: it is a pure read, does not broadcast payments, does not invoke providers, and needs no wallet signer or blockchain RPC. That is meaningful behavioral context for a mutation-adjacent domain. It stops short of stating whether access is scoped or whether the cached result can be stale.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, purpose front-loaded, each adding distinct value: what it does, the constraints under which it runs, and a pointer to the actionable recovery.action field. No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but the description compensates somewhat by naming the cached signed result and the recovery.action field. However, for a tool with two fully required, wholly undocumented parameters and no annotations, it leaves the auth token's origin and the run_id's source unaddressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for two undocumented parameters and does not: neither run_id nor access_token is explained, nor is where the access_token comes from or the paid_ hash format. The schema's regex and length bounds partially cover the gap, but the text adds nothing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (Read) and resource (an existing pipeline), and the read-only framing implicitly distinguishes it from the pipeline_prepare/pipeline_invoke siblings. It never names those siblings, so an agent must infer the routing rather than being told it.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

"Inspect recovery.action for unresolved work" hints at the retrieval use case, and 'without broadcasting payments or invoking providers' implies when to prefer this over an execute-style sibling. There is no explicit when-not or named alternative, so usage is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 8 tool updates
    • First observedmarket_invoke
    • First observedmarket_search
    • First observedpipeline_invoke
    • First observedpipeline_prepare
    • First observedpipeline_refund
    • First observedpipeline_refund_prepare
    • First observedpipeline_refund_status
    • First observedpipeline_status

TDQS

A3.9/5.0

Scored across 8 tools

Disambiguation4/5

market_search vs market_invoke and the three pipeline_* tools have clearly distinct actions, and the refund trio is likewise separable. The only friction is that pipeline_refund_prepare vs pipeline_prepare and pipeline_refund_status vs pipeline_status look similar at a glance, but descriptions make the boundary clear.

Naming Consistency4/5

All names are snake_case with a consistent prefix_action shape (market_*, pipeline_*, pipeline_refund_*). The addition of _status and _refund_* variants is slightly irregular but still fully predictable and readable.

Tool Count5/5

Eight tools is well-scoped: two for marketplace discovery/invocation and three each for the pipeline and refund lifecycles, with no redundant entries.

Completeness4/5

The surface covers search, invoke, and full prepare/invoke/status lifecycles for both pipelines and refunds, with clear recovery via status tools. Gaps are minor (no listing of a caller's runs/purchases or explicit cancellation), which agents can largely work around.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

Related MCP Connectors

Related MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    Enables agents to discover and pay for AI services per call via USDC on Solana, supporting marketplace search, listing details, on-chain reputation, wallet info, and paid calls.
    1 npm
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables any MCP client to discover live MeshKore agents, see their current operational status and pricing, and directly invoke their skills without an SDK, account, or API key.
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    Enables AI agents to discover governed capabilities, inspect machine-readable contracts, and request policy-checked procurements with signed receipts and audit trails.
    Apache 2.0