Airtable Schema

Airtable Schema Export Skill

Purpose

This skill covers running airtable-export-schema to dump an Airtable base's full schema — tables, fields (with types and descriptions), and views — to JSON and/or Markdown.

Direct Airtable Access via MCP

If the user wants Claude to directly read or modify Airtable data, use an Airtable MCP server instead:

/plugin install airtable@claude-plugins-official

The official plugin bundles Airtable's hosted MCP server (OAuth or PAT, nothing to run locally) and is the only one that can read Interface pages and create whole bases.

The community airtable-mcp-server by domdomegg is a self-hosted alternative (run via npx or its HTTP transport, PAT auth only) and is the one that can delete records and work with record comments. Its HTTP transport has no built-in auth, so only run it behind a reverse proxy or in a secured environment.

Both cover read/search/create/update of records and create/update of tables and fields.

This skill is for exporting schema metadata to a local file.

Quick Start (Agents)

Just run it, then react to any error:

airtable-export-schema

• The command is already on PATH when this plugin is installed — don't search the plugin cache for the script or invoke it via uv run --script (the shebang handles uv, and uv installs the requests dependency automatically on first run). In a standalone checkout, run bin/airtable-export-schema from the repo root.
• Credentials load automatically (see Credentials) — don't pre-check .env or the environment first. If something is missing, the script exits with an error naming the missing variable and how to provide it; react to that error instead.

Personal Access Token (PAT)

Create a PAT at airtable.com/create/tokens.

Required scopes:

The token must have access to the target base(s).

Running the Exporter

# Using CLI flags
airtable-export-schema --token YOUR_PAT --base appXXXXXXXXXX

# Using environment variables (preferred for repeated use)
export AIRTABLE_TOKEN=patXXXXXXXXXX
export AIRTABLE_BASE_ID=appXXXXXXXXXX
airtable-export-schema

# Choose output format (default: both)
airtable-export-schema --token YOUR_PAT --base appXXXXXXXXXX --format json

Credentials

Credentials are resolved in order: CLI flags → environment variables → .env file (current working directory, then the script's own directory).

Agents: never read .env (no cat, head, grep, or the Read tool) — it contains secret values and access is typically deny-listed. Don't pre-check that credentials exist either. Just run the command: it loads .env automatically and exits with a clear error naming the missing variable if credentials aren't found. React to that error — the message suggests fnox exec -- <command> only when fnox is installed; retry with that if a fnox.toml is in scope, otherwise relay the error to the user.

Using a .env File

Place a .env file in the directory where you run the command. The script loads it automatically.

# .env
AIRTABLE_TOKEN=patXXXXXXXXXX
AIRTABLE_BASE_ID=appXXXXXXXXXX

Then just run:

airtable-export-schema

Per-folder credentials: Because the script looks for .env in the current working directory first, you can keep a separate .env per project folder, each pointing at a different base or token.

Using fnox (optional)

If the project manages secrets with fnox (a fnox.toml in the current directory or a parent), wrap the command so fnox resolves the secrets at run time:

fnox exec -- airtable-export-schema

Note: fnox's fnox activate cd-hook only fires in interactive shells — in non-interactive (agent) shells the fnox exec wrapper is required.

Output files are written to the current directory, named:

{base_id}_{base_name}_{timestamp}_schema.json
{base_id}_{base_name}_{timestamp}_schema.md

The {timestamp} is YYYY-MM-DD_HH-MM-SS (down to the second). If a file with the same name already exists, a _1, _2, … counter is appended, so an export never overwrites a previous one.

Output Format

JSON — machine-readable, suitable for feeding to other scripts or tools:

{
  "base": { "id": "appXXX", "name": "My Base" },
  "summary": {
    "tableCount": 1,
    "fieldCount": 2,
    "viewCount": 1,
    "tables": [
      { "id": "tblXXX", "name": "Tasks", "primaryFieldName": "Name", "fieldCount": 2, "viewCount": 1 }
    ]
  },
  "tables": [
    {
      "id": "tblXXX",
      "name": "Tasks",
      "fields": [
        { "id": "fldXXX", "name": "Name", "type": "singleLineText", "description": "" },
        { "id": "fldYYY", "name": "Status", "type": "singleSelect", "options": { "choices": [...] } }
      ],
      "views": [
        { "id": "viwXXX", "name": "Grid view", "type": "grid" }
      ]
    }
  ]
}

The summary object gives per-table stats (field count, view count, and resolved primary field name) plus base-wide totals — all derived from the schema, so no extra API calls or token scopes are needed. The raw tables array is unchanged, so downstream tools (diff, standards check) are unaffected.

Markdown — human-readable summary of the schema. The header shows base-wide totals (Tables · Fields · Views), and each table lists its primary field plus field/view counts before the fields table. The fields table has columns # | Field Name | Field ID | Type | Options | Description. The Options column summarises each field's configuration (number/currency precision, date/time format, select choices, rating max, linked-table ID with reversed/single flags, rollup/lookup/count source fields and result type, etc.). Nothing is truncated — descriptions and choice lists are shown in full.

Workflow: Schema Before Scripting

When writing Airtable scripts, always export the schema first so you have accurate table, field, and view IDs. Feed the JSON output as context when using the airtable-scripting skill.

1. Export schema: airtable-export-schema --token ... --base ...
2. Read the output JSON to find exact IDs for tables, fields, and views
3. Use those IDs (never names) in scripts — see the airtable-scripting skill