Kadal

Kadal is an AI product studio based in Chennai, Tamil Nadu, India, founded by Mithunish Prabhakaran. The studio creates immersive AI-driven experiences, visualizations, and interactive applications, showcased through a sea-themed website that metaphorically represents depth and exploration. Kadal emphasizes privacy, minimal data collection, and offers various products across multiple subdomains such as work.kadal.cc, store.kadal.cc, and UI tools.

Kadal is listed with 1 API on the APIs.io network. Tagged areas include Artificial Intelligence, Studio, Immersive, Chennai, and Privacy.

Kadal’s developer surface includes documentation, engineering blog, and 10 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

13.7/100 emerging Agent 22/100 agent aware Full breakdown ↓
scored 2026-11-13 · rubric v0.28.0 · Show this score on your site →

1 API

Artificial Intelligence Studio ImmersiveChennaiPrivacy

Kin Score

Kin Score How this is scored →
scored 2026-11-13 · rubric v0.28.0

Composite quality — 13.7/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 3.6 / 17

Access Clarity 2.9 / 17

Operational Transparency 0.5 / 11

Contract Governance 0.0 / 10

Discoverability 4.9 / 9

Regulatory Posture 1.9 / 15

Agent readiness — 22/100 · agent aware

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 12 / 12

Machine-Readable Auth 10 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 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 5 / 5

Registration Without a Human 6 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

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 Ask about this →
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author Ask about this →
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) Ask about this →
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes Ask about this →
A2A Agent Card +8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0 Ask about this →
Rate-Limit Signaling +7 pts — OpenAPI responses document X-RateLimit-* / RateLimit headers (verified), else a rate_limits artifact or RateLimits link (documented) Ask about this →

8 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points Ask about this →
Access Clarity scores 17.0 — up to +16.6 composite points Ask about this →
Developer Ergonomics scores 21.4 — up to +15.7 composite points Ask about this →

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.

Not sure how this applies to your stack? Ask about any one item. Kin Lane reads every question, looks at your profile and your situation, and replies with what a fix would take — scoped to that one area, priced up front, yours whether you ship it or not. Ask a question →

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/kadal-cc/apis.yml: open an issue on github.com/api-evangelist/kadal-cc 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 →

Standards implemented 3

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

Model Context Protocol (MCP)

measured — observed by APIs.io (documented)

OAuth 2.0 Protected Resource Metadata

measured — observed by APIs.io (verified)

Dynamic Client Registration

measured — observed by APIs.io (published)

APIs 1

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

Kadal API

ConnectRPC backend exposing blush.crm.v1 services via ConnectRPC and Protobuf.

Security Posture 1

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

Kadal Domain Security

TLSv1.3 · DMARC

SECURITY

Resources

Get Started 1

Portal, sign-up, and the first successful call

DeveloperPortal

DeveloperPortal

Documentation 1

Reference material describing how the API behaves

Documentation

Documentation

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt

LLMsTxt

WellKnown

WellKnown

Build 1

SDKs, sample code, and the tooling you integrate with

GitHubOrganization

GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Commercial 2

Pricing, plans, and the legal terms of use

PrivacyPolicy

PrivacyPolicy

TermsOfService

TermsOfService

Company 2

The organization behind the API

Website

Website

Blog

Blog

Other 2

Properties that don't map to a standard resource type

Hosts

Hosts

Vendors

Vendors

Source (apis.yml)

aid: kadal-cc
name: Kadal
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  probe_state: unreadable
  source:
  - security
  generated: '2026-11-13'
  method: derived
image: https://kadal.cc/og.jpg?v=5
description: Kadal is an AI product studio based in Chennai, Tamil Nadu, India, founded by Mithunish Prabhakaran. The studio
  creates immersive AI-driven experiences, visualizations, and interactive applications, showcased through a sea-themed website
  that metaphorically represents depth and exploration. Kadal emphasizes privacy, minimal data collection, and offers various
  products across multiple subdomains such as work.kadal.cc, store.kadal.cc, and UI tools.
url: https://artifacts.apis.io/kadal-cc/apis.yml
x-type: company
x-source: harvest:mcp-registry
x-tier: profiled
x-tier-reason: enrichment pass 2026-11-13 (modular-v1; was harvest)
specificationVersion: '0.20'
created: '2026-11-13'
modified: '2026-11-13'
tags:
- Artificial Intelligence
- Studio
- Immersive
- Chennai
- Privacy
tags_raw:
- AI
- Studio
- Immersive
- Chennai
- Privacy
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: LLMsTxt
  url: llms/kadal-cc-llms.txt
- type: WellKnown
  url: well-known/kadal-cc-well-known.yml
- type: Hosts
  url: hosts/kadal-cc-hosts.yml
- type: Vendors
  url: vendors/kadal-cc-vendors.yml
- type: GitHubOrganization
  url: https://github.com/kadalcc
- type: Documentation
  url: https://docs.kadal.cc/
- type: DomainSecurity
  url: security/kadal-cc-domain-security.yml
- type: Website
  url: https://kadal.cc/
- type: DeveloperPortal
  url: https://work.kadal.cc
- type: PrivacyPolicy
  url: https://kadal.cc/privacy
- type: TermsOfService
  url: https://kadal.cc/terms
- type: Blog
  url: https://pmithunish.com
apis:
- name: Kadal API
  description: ConnectRPC backend exposing blush.crm.v1 services via ConnectRPC and Protobuf.
  humanURL: https://work.kadal.cc
  aid: kadal-cc:api
x-coverage:
  state: unreadable
  reason: js-rendered-docs
  detail: Docs are rendered JavaScript shells with no machine‑readable OpenAPI spec.
  evidence:
  - url: https://docs.kadal.cc/
    status: 200
  checked: '2026-11-13'
x-enrichment:
  date: '2026-11-13'
  status: enriched
  artifacts_added: 13
  pass: modular-v1
  engine: identity=local,contract=local,surfaces=local,docs=local,regulatory=local

Kadal API — frequently asked questions

Does Kadal have an API?

Yes. APIs.io lists 1 API from Kadal, including Kadal API.

Is the Kadal API ready for AI agents?

Kadal has a Kin Score of 13.7 out of 100 (emerging) and an Agent Readiness score of 21.8 (agent-aware), scored by API Evangelist on 2026-11-13.

Work with this as data

Every provider here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

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get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
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