IoMarkets.ai guide
Guide to IoMarkets.ai Federated Computing: receipt checks, runs, costs and evaluation requests.
사용해야 할까요
품질 및 안전성
발견 사항 (1)
- LOWTool 'get_overview' description lacks action verbget_overview에서
도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.
컨텍스트 비용
이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.
설치
원클릭 설치
`claude_desktop_config.json` 파일에 다음을 추가하세요:
{
"mcpServers": {
"iomarkets-ai": {
"url": "https://iomarkets.ai/mcp"
}
}
}원격 엔드포인트
https://iomarkets.ai/mcpstreamable-http할 수 있는 일
도구 목록
도구 (7)
🟢get_overview
What IoMarkets.ai is and what is built: Federated Computing (private computing across organisations on confidential hardware), with the main pages and what is a demonstration.
입력 스키마
{
"type": "object",
"properties": {},
"additionalProperties": false
}🟢get_guide(task)
Step-by-step instructions, with the exact button names, for one task on iomarkets.ai. Without a task, lists the tasks.
입력 스키마
{
"type": "object",
"properties": {
"task": {
"type": "string",
"enum": [
"look-around",
"runs",
"verify-receipt",
"audit-trail",
"request",
"review-pack",
"pilot",
"cost",
"own-assistant"
],
"description": "Which task. Leave out to list them all."
}
},
"additionalProperties": false
}🟢explain_term(term)
A plain-English meaning of a word used on the site: digital signature, invoice, Federated Computing, confidential hardware, attestation, receipt, AI agent, MCP. Without a word, lists them all.
입력 스키마
{
"type": "object",
"properties": {
"term": {
"type": "string",
"description": "The word to explain."
}
},
"additionalProperties": false
}🟢list_runs
Every Federated Computing run done on confidential hardware, newest first: its id, what it showed and the hardware it ran on.
입력 스키마
{
"type": "object",
"properties": {},
"additionalProperties": false
}🟢get_run(id)
One Federated Computing run in detail: what it showed, the model and data, hardware, location, result, payment, and whether its receipt verifies (checked now, with the same code as the receipt checker).
입력 스키마
{
"type": "object",
"properties": {
"id": {
"type": "string",
"enum": [
"sealed-tdx-gpu",
"sealed-tdx",
"gpu-llm-14b",
"gpu-llm-7b",
"gpu-llm",
"join-across-banks",
"federated-round",
"pilot-rehearsal",
"gpu-compute",
"three-banks",
"governed",
"intel-tdx",
"first-run"
],
"description": "The run id, from list_runs."
}
},
"required": [
"id"
],
"additionalProperties": false
}🟢estimate_cost(kind, runs, key_services, months)
What Federated Computing runs cost in confidential cloud time, at Google Cloud list prices, for the machines and run times the real runs used, plus key services. Same numbers as the cost estimator page. An estimate, not a quote.
입력 스키마
{
"type": "object",
"properties": {
"kind": {
"type": "string",
"enum": [
"tabular-cpu",
"tabular-tdx",
"gpu-small",
"gpu-llm-14b"
],
"description": "tabular-cpu (credit, fraud, risk models), tabular-tdx, gpu-small, or gpu-llm-14b."
},
"runs": {
"type": "number",
"description": "How many runs. Default 1."
},
"key_services": {
"type": "number",
"description": "Key services, one per data or model owner. Default 2."
},
"months": {
"type": "number",
"description": "Months the key services stay up. Default 3."
}
},
"required": [
"kind"
],
"additionalProperties": false
}🟢draft_evaluation_request(role, organisation, name, email, goal, ...)
Turn what someone wants to evaluate, in their own words, into a Federated Computing evaluation request. Returns the message and a link that opens it in their email app; nothing is sent for them, and they can also use the form. Ask for anything missing, one question at a time, before calling it.
입력 스키마
{
"type": "object",
"properties": {
"role": {
"type": "string",
"enum": [
"Benchmark or data owner",
"Model owner",
"Model-risk or audit team",
"Cloud provider",
"Research or grant partner",
"Other"
],
"description": "Their role."
},
"organisation": {
"type": "string"
},
"name": {
"type": "string"
},
"email": {
"type": "string",
"description": "Where the reply should go."
},
"goal": {
"type": "string",
"description": "What they want to evaluate, in a sentence or two."
},
"assets": {
"type": "string",
"enum": [
"Tabular model or data",
"Text classifier",
"Large language model",
"Not sure yet"
],
"description": "The kind of model or data (optional)."
},
"constraints": {
"type": "string",
"description": "Regions, confidentiality, timing (optional)."
}
},
"required": [
"role",
"organisation",
"name",
"email",
"goal"
],
"additionalProperties": false
}커뮤니티
증거