tablethis-ai

TableThis provides a shared database platform for AI‑built internal applications, offering typed tables and Markdown documentation that teams can collaborate on. It enables developers to connect large language models like Claude or Codex to a central data store, allowing AI assistants to read, write, and update information with version history and access controls. The service is free to start with 100 MB of storage and no credit card required, targeting teams that want to build internal tools powered by AI without managing infrastructure.

tablethis-ai is listed with 1 API on the APIs.io network. Tagged areas include AI, Database, Collaboration, Internal Apps, and Low-code.

tablethis-ai’s developer surface includes documentation, API reference, engineering blog, and 9 more developer resources.

🌐 Visit website 📡 Profile data on GitHub

19.0/100 emerging Agent 24/100 agent aware Full breakdown ↓
scored 2026-10-31 · rubric v0.25.0 · Show this score on your site →

1 API 1 MCP Servers

AIDatabase CollaborationInternal AppsLow-code

Kin Score

Kin Score How this is scored →
scored 2026-10-31 · rubric v0.25.0

Composite quality — 19.0/100 · emerging

Contract Quality 0.0 / 21

Developer Ergonomics 3.2 / 17

Access Clarity 5.8 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 5.5 / 9

Regulatory Posture 4.1 / 15

Agent readiness — 24/100 · 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 7 / 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

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
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
Request/Response Examples +7 pts — share of operations carrying request/response examples; else an Examples / Code Samples link

7 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
Developer Ergonomics scores 19.0 — up to +16.2 composite points
Access Clarity scores 34.2 — up to +13.2 composite points

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/tablethis-ai/apis.yml: open an issue on github.com/api-evangelist/tablethis-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.

TableThis API

TableThis provides an MCP‑based API for AI agents to read, write, and manage tables and documents. The API is accessed via the MCP endpoint and documented at the developer docs ...

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

tablethis-ai MCP Server

Remote MCP server at www.tablethis.ai.

MCP SERVER

Pricing Plans 1

Published pricing tiers and plan structures.

Tablethis Ai Plans Pricing

0 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Tablethis Ai Rate Limits

0 limits

RATE LIMITS

Security Posture 2

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

Tablethis Ai Authentication

0 schemes

SECURITY

Tablethis Ai Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Scopes 1

OAuth scopes governing access to this provider's APIs.

Tablethis Ai Scopes

OAuth 2.0 · no documented scopes

0 scopes

SCOPES

Resources

Get Started 1

Portal, sign-up, and the first successful call

Login

Documentation 2

Reference material describing how the API behaves

Documentation

Documentation

APIReference

APIReference

Agent Surfaces 2

MCP servers, agent skills, and machine-readable catalogs

MCPServer

MCPServer

WellKnown

WellKnown

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Commercial 2

Pricing, plans, and the legal terms of use

TermsOfService

TermsOfService

PrivacyPolicy

PrivacyPolicy

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: tablethis-ai
name: tablethis-ai
description: TableThis provides a shared database platform for AI‑built internal applications, offering typed tables and Markdown
  documentation that teams can collaborate on. It enables developers to connect large language models like Claude or Codex
  to a central data store, allowing AI assistants to read, write, and update information with version history and access controls.
  The service is free to start with 100 MB of storage and no credit card required, targeting teams that want to build internal
  tools powered by AI without managing infrastructure.
url: https://artifacts.apis.io/tablethis-ai/apis.yml
image: https://www.tablethis.ai/images/logo.svg
x-type: company
x-source: harvest:mcp-registry
x-tier: profiled
x-tier-reason: enrichment pass 2026-10-31 (modular-v1; was harvest)
specificationVersion: '0.20'
created: '2026-10-31'
modified: '2026-10-31'
x-coverage:
  state: unreadable
  reason: no-machine-readable-spec
  detail: Docs provide MCP endpoint but no OpenAPI, AsyncAPI, GraphQL, or other machine‑readable contract.
  evidence:
  - url: https://www.tablethis.ai/docs
    status: 200
  checked: '2026-10-31'
tags:
- AI
- Database
- Collaboration
- Internal Apps
- Low-code
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: MCPServer
  url: mcp/tablethis-ai-mcp.yml
- type: WellKnown
  url: well-known/tablethis-ai-well-known.yml
- type: Hosts
  url: hosts/tablethis-ai-hosts.yml
- type: Vendors
  url: vendors/tablethis-ai-vendors.yml
- type: Login
  url: https://www.tablethis.ai/users/log-in
- type: DomainSecurity
  url: security/tablethis-ai-domain-security.yml
- type: Website
  url: https://tablethis.ai/
- type: Documentation
  url: https://www.tablethis.ai/docs
- type: APIReference
  url: https://www.tablethis.ai/docs
- type: Blog
  url: https://www.tablethis.ai/blog
- type: TermsOfService
  url: https://www.tablethis.ai/terms
- type: PrivacyPolicy
  url: https://www.tablethis.ai/privacy
apis:
- name: TableThis API
  description: TableThis provides an MCP‑based API for AI agents to read, write, and manage tables and documents. The API
    is accessed via the MCP endpoint and documented at the developer docs page.
  humanURL: https://www.tablethis.ai/docs
  baseURL: https://app.tablethis.ai/mcp
  aid: tablethis-ai:tablethis-api
x-enrichment:
  date: '2026-10-31'
  status: enriched
  artifacts_added: 25
  pass: modular-v1
  engine: identity=oss,contract=oss,surfaces=oss,docs=oss,regulatory=oss

tablethis-ai API — frequently asked questions

Does tablethis-ai have an API?

Yes. APIs.io lists 1 API from tablethis-ai, including TableThis API.

How do I authenticate with the tablethis-ai API?

tablethis-ai documents its authentication (0 schemes). The details are profiled on APIs.io as Tablethis Ai Authentication.

Does tablethis-ai have an MCP server?

Yes. APIs.io lists 1 MCP server for tablethis-ai: Remote MCP server at www.tablethis.ai.

Is the tablethis-ai API ready for AI agents?

tablethis-ai has a Kin Score of 19.0 out of 100 (emerging) and an Agent Readiness score of 24.3 (agent-aware), scored by API Evangelist on 2026-10-31.

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.

https://apis.io/mcp

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get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
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