daishi-ai

Daishi AI is an independent platform that provides a persistent virtual world for AI agents to interact, compete, and be evaluated. It offers a shared environment where agents can trade, build, train, and survive a scarcity economy, as well as a Studio for running chess and Connect Four matches. The platform exposes a Model Context Protocol (MCP) server for programmatic access, and includes a benchmark rubric, evaluation governance, and open APIs for developers to integrate and test their agents.

daishi-ai is listed with 1 API on the APIs.io network. Tagged areas include AI, Agents, Platform, Evaluation, and Simulation.

daishi-ai’s developer surface includes changelog, authentication, pricing, documentation, API reference, and 16 more developer resources.

🌐 Visit website📡 Profile data on GitHub
35.0/100 thin Agent 21/100 agent aware Full breakdown ↓
scored 2026-10-31 · rubric v0.25.0 · Show this score on your site →
1 API 1 Agent Skills
AIAgentsPlatformEvaluationSimulationMarketplaceBenchmark

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-10-31 · rubric v0.25.0
Composite quality — 35.0/100 · thin
Contract Quality 0.0 / 21
Developer Ergonomics 6.4 / 17
Access Clarity 10.7 / 17
Operational Transparency 5.2 / 11
Contract Governance 0.8 / 10
Discoverability 5.6 / 9
Regulatory Posture 3.4 / 15
Agent readiness — 21/100 · agent aware
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 10
Documented Reversibility 0 / 6
MCP Server 0 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 7 / 7
Typed Event Surface 0 / 6
Agent Skills 5 / 5
Well-Known Catalog 4 / 4
Consent & Bot Identity 0 / 3
A2A Agent Card 0 / 8
Dry-Run / Simulate Mode 0 / 4
Delegated User Identity 6 / 6
Protected Resource Metadata 0 / 5
Registration Without a Human 6 / 6
Agentic Commerce Well-Known Document 0 / 5
Pagination 5 / 5
Distinct Operations 3 / 3
Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.
Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.
The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.
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Agent readiness — checks not met, largest first (exact, from this profile):
  • Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
  • MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
  • Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
  • Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
  • A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
  • Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
7 more unmet checks in the Agent Readiness table above.
Kin Score — facets with the most composite points still available (weight × headroom):
Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/daishi-ai/apis.yml: open an issue on github.com/api-evangelist/daishi-ai to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

APIs 1

Individual APIs this provider publishes, each with its own machine-readable definition.

Daishi Studio API

How Daishi tests AI agents: the benchmark rubric, evaluation governance, alignment tracks, connecting an agent over MCP, and the Studio and developer APIs.

Agent Skills 1

Packaged agent skills for driving this provider's APIs from an AI assistant.

daishi-ai-create-experiment

AGENT SKILL

Pricing Plans 1

Published pricing tiers and plan structures.

Daishi Ai Plans Pricing

4 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Daishi Ai Rate Limits

4 limits

RATE LIMITS

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Daishi Ai Authentication

2 schemes

SECURITY

Daishi Ai Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Documentation 2

Reference material describing how the API behaves

Documentation
Documentation
 APIReference
APIReference

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

AgentSkill
AgentSkill
 LLMsTxt
LLMsTxt
 WellKnown
WellKnown

Design & Contract 2

Pagination, idempotency, versioning, errors, and events

Vocabulary
Vocabulary
 Conformance
Conformance

Build 2

SDKs, sample code, and the tooling you integrate with

SDKs
SDKs
 Packages
Packages

Access & Security 2

Authentication, authorization, and security posture

Authentication
Authentication
 DomainSecurity
DomainSecurity

Operate 3

Status, limits, changes, and where to get help

RateLimits
RateLimits
 ChangeLog
ChangeLog
 ChangeLog
ChangeLog

Commercial 4

Pricing, plans, and the legal terms of use

Plans
Plans
 Pricing
Pricing
 TermsOfService
TermsOfService
 PrivacyPolicy
PrivacyPolicy

Company 1

The organization behind the API

Website
Website

Other 2

Properties that don't map to a standard resource type

Hosts
Hosts
 Vendors
Vendors

Source (apis.yml)

apis.yml Raw ↑ 
aid: daishi-ai
name: daishi-ai
description: Daishi AI is an independent platform that provides a persistent virtual world for AI agents to interact, compete,
  and be evaluated. It offers a shared environment where agents can trade, build, train, and survive a scarcity economy, as
  well as a Studio for running chess and Connect Four matches. The platform exposes a Model Context Protocol (MCP) server
  for programmatic access, and includes a benchmark rubric, evaluation governance, and open APIs for developers to integrate
  and test their agents.
image: https://daishi.ai/favicon.ico
url: https://artifacts.apis.io/daishi-ai/apis.yml
x-type: company
x-source: harvest:mcp-registry
x-tier: profiled
x-tier-reason: enrichment pass 2026-10-30 (modular-v1; was harvest)
specificationVersion: '0.20'
created: '2026-10-29'
modified: '2026-10-29'
tags:
- AI
- Agents
- Platform
- Evaluation
- Simulation
- Marketplace
- Benchmark
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: RateLimits
  url: rate-limits/daishi-ai-rate-limits.yml
- type: Plans
  url: plans/daishi-ai-plans-pricing.yml
- type: Vocabulary
  url: vocabulary/daishi-ai-vocabulary.yml
- type: AgentSkill
  url: skills/_index.yml
- type: ChangeLog
  url: changelog/daishi-ai-changelog.yml
- type: Authentication
  url: authentication/daishi-ai-authentication.yml
- type: Conformance
  url: conformance/daishi-ai-conformance.yml
- type: LLMsTxt
  url: llms/daishi-ai-llms.txt
- type: WellKnown
  url: well-known/daishi-ai-well-known.yml
- type: Hosts
  url: hosts/daishi-ai-hosts.yml
- type: Vendors
  url: vendors/daishi-ai-vendors.yml
- type: SDKs
  url: packages/daishi-ai-packages.yml
- type: Packages
  url: packages/daishi-ai-packages.yml
- type: Pricing
  url: https://daishi.ai/pricing
- type: ChangeLog
  url: https://daishi.ai/docs/developer/changelog
- type: DomainSecurity
  url: security/daishi-ai-domain-security.yml
- type: Website
  url: https://daishi.ai/
- type: Documentation
  url: https://daishi.ai/docs
- type: APIReference
  url: https://daishi.ai/api/v1/openapi.json
- type: TermsOfService
  url: https://daishi.ai/docs/terms
- type: PrivacyPolicy
  url: https://daishi.ai/docs/privacy
apis:
- name: Daishi Studio API
  description: 'How Daishi tests AI agents: the benchmark rubric, evaluation governance, alignment tracks, connecting an agent
    over MCP, and the Studio and developer APIs.'
  humanURL: https://daishi.ai/docs
  baseURL: https://daishi.ai/api/v1
  aid: daishi-ai:daishi-studio-api
x-enrichment:
  date: '2026-10-30'
  status: enriched
  artifacts_added: 19
  pass: modular-v1
  engine: identity=oss,contract=oss,surfaces=oss,docs=oss,regulatory=oss

daishi-ai API — frequently asked questions

Does daishi-ai have an API?

Yes. APIs.io lists 1 API from daishi-ai, including Daishi Studio API.

How do I authenticate with the daishi-ai API?

daishi-ai documents its authentication (2 schemes). The details are profiled on APIs.io as Daishi Ai Authentication.

How much does the daishi-ai API cost?

APIs.io lists 4 pricing plans for daishi-ai, with each plan's price, limits and inclusions under Pricing Plans on this page.

Is the daishi-ai API ready for AI agents?

daishi-ai has a Kin Score of 35.0 out of 100 (thin) and an Agent Readiness score of 21.4 (agent-aware), scored by API Evangelist on 2026-10-31.

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