Airtable OAuth MCP Server
Provides tools to interact with Airtable bases, including listing bases and tables, describing table schemas, and performing CRUD operations on records with filtering, sorting, and search.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Airtable OAuth MCP Serverlist records from my 'Tasks' table in Airtable"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Airtable OAuth MCP Server
A production-ready Model Context Protocol (MCP) server for Airtable with secure OAuth 2.0 authentication. This server enables AI assistants and applications to interact with Airtable bases through a standardized MCP interface, providing complete API coverage for all Airtable operations.
๐ Features
Core Functionality
๐ OAuth 2.0 Authentication - Secure token-based authentication with Airtable
๐ Complete Airtable API Coverage - 10 comprehensive MCP tools covering all operations
โก FastMCP Framework - Built on the high-performance FastMCP framework
โ๏ธ Cloud-Ready - Production-ready deployment support
๐ Dual Transport - Support for both STDIO and HTTP transport protocols
Security & Reliability
๐ Environment-based Configuration - Secure credential management
โ Type Safety - Full type hints and validation with Pydantic
๐งช Comprehensive Testing - Unit tests with pytest and coverage reporting
๐ Code Quality - Linting with Ruff and type checking with MyPy
Developer Experience
๐ Rich Documentation - Comprehensive setup and usage guides
๐ง Easy Setup - Simple installation with uv package manager
๐ฏ Typed Parameters - Clear, typed tool parameters for better IDE support
๐ Flexible Querying - Advanced filtering, sorting, and search capabilities
Related MCP server: Airtable MCP Pro
๐ Prerequisites
Python 3.11+ - Latest Python version for optimal performance
uv - Fast Python package manager (install guide)
Airtable Developer Account - To create OAuth applications (sign up)
๐ Quick Start
1. Installation
Clone the repository and install dependencies:
git clone https://github.com/onimsha/airtable-mcp-server-oauth.git
cd airtable-mcp-server-oauth
uv sync2. Airtable OAuth Setup
Create an Airtable OAuth Application:
Visit Airtable Developer Hub
Create a new OAuth integration
Note your
Client IDandClient SecretSet redirect URI to
http://localhost:8000/oauth/callback
3. Environment Configuration
Copy the environment template and configure your credentials:
cp .env.example .envEdit .env with your values:
# Airtable OAuth Configuration
AIRTABLE_CLIENT_ID="your_airtable_client_id_here"
AIRTABLE_CLIENT_SECRET="your_airtable_client_secret_here"
AIRTABLE_REDIRECT_URI="http://localhost:8000/oauth/callback"
# Server Configuration
HOST="0.0.0.0"
PORT=8000
LOG_LEVEL="INFO"4. Testing with MCP Inspector
Use the official MCP Inspector to test and interact with your server:
Start the server:
uv run python -m airtable_mcp httpOpen MCP Inspector: Visit https://modelcontextprotocol.io/docs/tools/inspector
Connect to your server:
Select "HTTP Streaming" transport
Enter the URL:
http://localhost:8000/mcpClick "Connect"
Authenticate with Airtable:
The server will guide you through OAuth authentication
Use the inspector to test available MCP tools
5. Run the Server
STDIO Transport (default):
uv run python -m airtable_mcp
# or
uv run airtable-oauth-mcpHTTP Transport:
uv run python -m airtable_mcp http
# or with custom host/port
uv run python -m airtable_mcp http localhost 8001Additional Options:
# Set log level
uv run python -m airtable_mcp --log-level DEBUG
# Show help
uv run python -m airtable_mcp --help
# Show version
uv run python -m airtable_mcp --versionThe HTTP server will be available at http://localhost:8000/ (or custom host:port) with OAuth endpoints for web integration.
MCP Tools Available
The server provides 10 MCP tools for Airtable operations:
Base Operations:
list_bases()- List all accessible baseslist_tables(base_id, detail_level?)- List tables in a basedescribe_table(base_id, table_id)- Get detailed table schema
Record Operations:
list_records(base_id, table_id, view?, filter_by_formula?, sort?, fields?)- List records with filteringget_record(base_id, table_id, record_id)- Get a specific recordcreate_record(base_id, table_id, fields, typecast?)- Create a single recordcreate_records(base_id, table_id, records, typecast?)- Create multiple recordsupdate_records(base_id, table_id, records, typecast?)- Update multiple recordsdelete_records(base_id, table_id, record_ids)- Delete multiple recordssearch_records(base_id, table_id, filter_by_formula, view?, fields?)- Search records with formulas
All tools now use typed parameters instead of generic args, making them more transparent to MCP clients.
Parameter Flexibility:
fieldsparameter accepts either a single field name (string) or array of field namessortparameter expects array of objects:[{"field": "Name", "direction": "asc"}]
๐ก Usage Examples
Basic Record Operations
# List all records in a table
records = await client.call_tool("list_records", {
"base_id": "appXXXXXXXXXXXXXX",
"table_id": "tblYYYYYYYYYYYYYY"
})
# Create a new record
new_record = await client.call_tool("create_record", {
"base_id": "appXXXXXXXXXXXXXX",
"table_id": "tblYYYYYYYYYYYYYY",
"fields": {
"Name": "John Doe",
"Email": "john@example.com",
"Status": "Active"
}
})
# Search records with filtering
filtered_records = await client.call_tool("search_records", {
"base_id": "appXXXXXXXXXXXXXX",
"table_id": "tblYYYYYYYYYYYYYY",
"filter_by_formula": "AND({Status} = 'Active', {Email} != '')",
"fields": ["Name", "Email", "Status"]
})Advanced Querying
# List records with sorting and filtering
records = await client.call_tool("list_records", {
"base_id": "appXXXXXXXXXXXXXX",
"table_id": "tblYYYYYYYYYYYYYY",
"view": "Grid view",
"filter_by_formula": "{Priority} = 'High'",
"sort": [
{"field": "Created", "direction": "desc"},
{"field": "Name", "direction": "asc"}
],
"fields": ["Name", "Priority", "Created", "Status"]
})
# Batch operations
batch_create = await client.call_tool("create_records", {
"base_id": "appXXXXXXXXXXXXXX",
"table_id": "tblYYYYYYYYYYYYYY",
"records": [
{"fields": {"Name": "Record 1", "Value": 100}},
{"fields": {"Name": "Record 2", "Value": 200}},
{"fields": {"Name": "Record 3", "Value": 300}}
],
"typecast": True
})Schema Discovery
# List all bases you have access to
bases = await client.call_tool("list_bases")
# Get detailed information about a specific table
table_info = await client.call_tool("describe_table", {
"base_id": "appXXXXXXXXXXXXXX",
"table_id": "tblYYYYYYYYYYYYYY"
})
# List all tables in a base
tables = await client.call_tool("list_tables", {
"base_id": "appXXXXXXXXXXXXXX",
"detail_level": "full"
})๐ ๏ธ Development
Getting Started
Fork and Clone:
git clone https://github.com/onimsha/airtable-mcp-server-oauth.git cd airtable-mcp-server-oauthSetup Development Environment:
uv sync --all-extrasRun Tests:
uv run pytest uv run pytest --cov=src/airtable_mcp --cov-report=html
Code Quality
Type Checking:
uv run mypy src/Linting:
uv run ruff check src/
uv run ruff format src/Pre-commit Hooks:
pip install pre-commit
pre-commit installTesting
The project includes comprehensive test coverage:
Unit Tests: Test individual components and functions
Integration Tests: Test OAuth flow and Airtable API interactions
Coverage Reports: Ensure >90% code coverage
# Run all tests
uv run pytest
# Run with coverage
uv run pytest --cov=src/airtable_mcp
# Run specific test files
uv run pytest tests/test_oauth.py
uv run pytest tests/test_tools.pyProject Structure
src/
โโโ airtable_mcp/ # Main MCP server package
โ โโโ __init__.py # Package initialization
โ โโโ __main__.py # Module entry point
โ โโโ main.py # CLI and application entry
โ โโโ api/ # Airtable API client
โ โ โโโ __init__.py
โ โ โโโ client.py # HTTP client for Airtable API
โ โ โโโ exceptions.py # API-specific exceptions
โ โ โโโ models.py # Pydantic models for API responses
โ โโโ mcp/ # MCP server implementation
โ โโโ __init__.py
โ โโโ schemas.py # MCP tool schemas
โ โโโ server.py # FastMCP server with tools
โโโ mcp_oauth_lib/ # Reusable OAuth library
โโโ __init__.py # Library initialization
โโโ auth/ # Authentication components
โ โโโ __init__.py
โ โโโ context.py # Auth context management
โ โโโ middleware.py # OAuth middleware
โ โโโ utils.py # Auth utilities
โโโ core/ # Core OAuth functionality
โ โโโ __init__.py
โ โโโ config.py # OAuth configuration
โ โโโ flow.py # OAuth flow implementation
โ โโโ server.py # OAuth server endpoints
โโโ providers/ # OAuth provider implementations
โ โโโ __init__.py
โ โโโ airtable.py # Airtable OAuth provider
โ โโโ base.py # Base provider interface
โโโ utils/ # OAuth utilities
โโโ __init__.py
โโโ pkce.py # PKCE implementation
โโโ state.py # State managementโ๏ธ Configuration
All configuration is handled through environment variables (loaded from .env):
Required Variables
AIRTABLE_CLIENT_ID- OAuth client ID from AirtableAIRTABLE_CLIENT_SECRET- OAuth client secretAIRTABLE_REDIRECT_URI- OAuth callback URL
Optional Variables
HOST- Server host (default:0.0.0.0)PORT- Server port (default:8000)LOG_LEVEL- Logging level (default:INFO)MCP_SERVER_NAME- Server name (optional)MCP_SERVER_VERSION- Server version (optional)
๐ค Contributing
We welcome contributions! Please see our contribution guidelines:
Fork the repository and create a feature branch
Write tests for any new functionality
Ensure code quality with our linting and formatting tools
Update documentation for any API changes
Submit a pull request with a clear description
Contribution Areas
๐ Bug fixes - Help us squash bugs
โจ New features - Add new Airtable API endpoints
๐ Documentation - Improve setup guides and examples
๐งช Testing - Increase test coverage
๐ Performance - Optimize API calls and caching
๐ License
This project is licensed under the MIT License - see the LICENSE file for details.
๐ Acknowledgments
FastMCP - Excellent MCP framework
Airtable - Powerful database platform
Model Context Protocol - Standard for AI tool integration
๐ Documentation
Additional Resources
๐ Support
Issues: GitHub Issues
Discussions: GitHub Discussions
Documentation: Project Wiki
Available Tools
10 toolscreate_recordC
Create a single record
| Name | Required | Description | Default |
|---|---|---|---|
| base_id | Yes | The Airtable base ID | |
| table_id | Yes | The table ID or name | |
| fields | Yes | Field values for the new record | |
| typecast | No | Enable automatic data conversion |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It only states the action, missing details like permission requirements, return behavior, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, making it concise, but lacks structure and does not provide useful information beyond the name. It is efficient but insufficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description is too minimal for a tool with 4 parameters and sibling tools. It leaves many questions unanswered, such as the return value and handling of optional parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage for all 4 parameters, so the description adds no extra value. Baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Create a single record' states the verb and resource, and hints at single-record creation, but it doesn't specify the context (e.g., Airtable) nor clearly differentiate from 'create_records'. It is a slight improvement over a tautology but remains generic.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus siblings like 'create_records' or 'update_records'. The description lacks prerequisites, context, or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_recordsC
Create multiple records
| Name | Required | Description | Default |
|---|---|---|---|
| base_id | Yes | The Airtable base ID | |
| table_id | Yes | The table ID or name | |
| records | Yes | List of records to create | |
| typecast | No | Enable automatic data conversion |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description does not disclose behavioral details such as error handling, atomicity, or limits. Minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise (two words) but under-specified. Sacrifices necessary detail for brevity, lacking structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite an output schema and full parameter descriptions, the description is too short to provide complete context. No mention of success behavior or constraints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% parameter description coverage, so baseline is 3. The description adds no further meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Create multiple records' clearly states the action and distinguishes from the sibling 'create_record' (singular). Verb and resource are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool over alternatives like 'create_record' or other tools. Context about batch limits or trade-offs is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_recordsC
Delete multiple records
| Name | Required | Description | Default |
|---|---|---|---|
| base_id | Yes | The Airtable base ID | |
| table_id | Yes | The table ID or name | |
| record_ids | Yes | List of record IDs to delete |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It only states the action without disclosing effects like irreversibility, permission requirements, or whether the operation is atomic. The agent receives no safety or side-effect information beyond the obvious destructive hint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very brief at only two words, which is efficient. However, it lacks structure or front-loading of key information like input requirements. It earns its place but could be more informative in the same length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's destructive nature and lack of annotations, the description is insufficient. It does not explain return values (though output schema exists), constraints on record count, or any post-deletion behavior. The tool's complexity (3 required params) demands more context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with concise parameter descriptions. The description adds no additional meaning beyond what's in the schema; baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Delete multiple records' clearly states the action and resource, distinguishing it from siblings like create_records or list_records. However, it could be more specific by mentioning the target system (Airtable) as inferred from schema parameter descriptions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like update_records or delete single records. There are no conditions or exclusions mentioned, leaving the agent without context for appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
describe_tableB
Get detailed information about a specific table
| Name | Required | Description | Default |
|---|---|---|---|
| base_id | Yes | The Airtable base ID | |
| table_id | Yes | The table ID or name |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only says 'get detailed information', which implies a read operation but does not disclose any behavioral details like permission requirements, data freshness, or potential errors. However, it does not contradict any annotations as none exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but may be too terse. It front-loads the key action and resource, but could benefit from additional context without becoming verbose. It is adequate but not exemplary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema documenting return values, and full schema coverage for parameters, the description is minimally complete. However, it lacks usage context and does not leverage the existing structured data to reduce redundancy. It is sufficient for a simple read operation but leaves gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and both parameters ('base_id' and 'table_id') have clear descriptions in the schema itself. The tool description adds no additional meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'get' and resource 'a specific table', which is distinct from sibling tools like 'list_tables' (lists all tables) and 'get_record' (gets a record). It provides a specific and unambiguous purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives such as 'list_tables' or 'get_record'. It does not mention any prerequisites or context for invocation, leaving the agent to infer from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_recordB
Get a specific record by ID
| Name | Required | Description | Default |
|---|---|---|---|
| base_id | Yes | The Airtable base ID | |
| table_id | Yes | The table ID or name | |
| record_id | Yes | The record ID |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. While 'Get' implies a read operation, it does not explicitly state that the tool is read-only, non-destructive, or what happens if the record is missing. More detail is needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally concise at one sentence with no wasted words. However, it could benefit from slightly more structured detail, such as mentioning the expected output or context, but it remains efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, full parameter coverage, and the presence of an output schema, the description is mostly adequate. However, it omits any mention of prerequisites, error cases, or typical use context, leaving some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters are fully described in the input schema (100% coverage), so the description's job is minimal. The description adds no extra meaning beyond what the schema already provides, resulting in a baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get'), the resource ('a specific record'), and the method ('by ID'). It effectively distinguishes from sibling tools like create_record or list_records, making it easy for an AI agent to understand the tool's core function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It lacks context about prerequisites, when not to use it, or suggestions to consider sibling tools for different operations like searching or updating.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_basesA
List all accessible Airtable bases
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden. It implies a read-only operation but lacks details on pagination, authentication, or rate limits. Adequate for a simple list.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is concise, front-loaded, and contains no extraneous words. Excellent efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and existing output schema, the description sufficiently informs the agent of the tool's purpose and return value. No missing context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so baseline score of 4 applies. Description adds no parameter information, which is unnecessary.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the verb 'list', resource 'bases', and scope 'all accessible', distinguishing it from sibling tools like list_tables and list_records.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description is purely functional.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_recordsB
List records from a table with optional filtering
| Name | Required | Description | Default |
|---|---|---|---|
| base_id | Yes | The Airtable base ID | |
| table_id | Yes | The table ID or name | |
| view | No | View name or ID | |
| filter_by_formula | No | Airtable formula for filtering | |
| sort | No | Sort configuration - array of {field: string, direction: 'asc'|'desc'} | |
| fields | No | Specific fields to include (field name or array of field names) |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behaviors. It mentions 'optional filtering' but does not disclose pagination, rate limits, record limits, or ordering. For a listing tool, this lack of behavioral context is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise at 8 words, front-loading the core purpose. However, it could include a brief note on pagination or differentiation from search_records without much expansion. Still, it avoids unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (6 parameters, sibling search_records tool), the description is incomplete. It does not mention pagination, record limit defaults, or how filtering compares to search_records. Output schema exists but description still needs to cover behavioral gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds only 'with optional filtering', which hints at available filtering parameters but does not add meaning beyond the schema. Schema already fully describes all 6 parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List records from a table with optional filtering', specifying the action (list) and resource (records from a table). It distinguishes from siblings like get_record (single record) and search_records (more advanced search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for listing records with optional filtering, but does not explicitly differentiate from sibling search_records or state when to use this over alternatives. No guidance on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_tablesA
List tables in a specific base
| Name | Required | Description | Default |
|---|---|---|---|
| base_id | Yes | The Airtable base ID | |
| detail_level | No | Level of detail to include in response | tableIdentifiersOnly |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'List' implies a read-only operation, but the description does not explicitly disclose safety, permissions, or side effects. It is adequate but not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no extraneous words. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (handling return values) and the tool's simplicity (2 params, 1 required), the description is sufficiently complete. It could add brief detail about the detail_level parameter, but not necessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters. The description adds no extra meaning beyond the schema, meeting baseline expectations without exceeding them.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List tables in a specific base' clearly states the verb (list), resource (tables), and scope (specific base). It naturally distinguishes from sibling tools like list_bases (which lists bases) and describe_table (which describes a single table).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly provide when to use or when not to use this tool relative to siblings. Usage is implied, but no guidance on exclusions or alternatives is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_recordsB
Search records using a formula filter
| Name | Required | Description | Default |
|---|---|---|---|
| base_id | Yes | The Airtable base ID | |
| table_id | Yes | The table ID or name | |
| filter_by_formula | Yes | Airtable formula for filtering | |
| view | No | View name or ID | |
| fields | No | Specific fields to include (field name or array of field names) |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It does not state that this is a read-only operation, nor mention any potential side effects, pagination, or limits. While search is likely safe, the lack of explicit disclosure leaves uncertainty.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no wasted words. It is front-loaded and efficiently communicates the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters and an output schema, the description is adequate but minimal. It does not mention that the tool returns multiple records or any pagination details, but the output schema may cover the return structure. Overall, it meets minimum viability.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema coverage is 100%, so parameters are already well-documented. The description adds minimal value beyond restating the filter mechanism. Baseline 3 is appropriate as the description does not significantly enhance understanding of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches records using a formula filter. The verb 'search' and resource 'records' are specific, and the mention of 'formula filter' distinguishes it from sibling tools like list_records (likely unfiltered) and get_record (single record by ID). However, it doesn't explicitly differentiate from these siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance is provided. The usage is implied by the description: use when you need to filter records via a formula. But alternatives like list_records or get_record are not mentioned, leaving the agent to infer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_recordsC
Update multiple records
| Name | Required | Description | Default |
|---|---|---|---|
| base_id | Yes | The Airtable base ID | |
| table_id | Yes | The table ID or name | |
| records | Yes | List of record updates | |
| typecast | No | Enable automatic data conversion |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description gives no behavioral details beyond the update action. With no annotations, the tool's effects (e.g., partial vs full replacement, error handling, idempotency) are completely opaque. The output schema exists but isn't referenced.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
At three words, the description is severely under-specified. While concise, it sacrifices clarity and fails to front-load critical information (e.g., 'update multiple records in an Airtable table').
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a batch update operation and the existence of an output schema, the description is woefully incomplete. It omits return value structure, error behavior, limits, and caveats, leaving an agent with insufficient information to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for all four parameters. The tool description adds no additional meaning beyond the schema, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Update multiple records' clearly states the verb (update) and resource (records), distinguishing it from create, delete, or single-record tools. However, it does not explicitly mention the Airtable context or differentiate from similar batch operations beyond the name and sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidelines are provided on when to use this tool versus alternatives (e.g., create_records, delete_records). There is no mention of prerequisites, limits, or situations where this tool is inappropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
10 tool updates
v0.1.0- First observed
create_record - First observed
create_records - First observed
delete_records - First observed
describe_table - First observed
get_record - First observed
list_bases - First observed
list_records - First observed
list_tables - First observed
search_records - First observed
update_records
TDQS
Each tool targets a distinct operation: creating single vs multiple records, listing different resources (bases, tables, records), searching with filters, and updating/deleting. No ambiguity between tools.
All tools follow a consistent verb_noun pattern using snake_case (e.g., create_record, list_bases, describe_table). No mixing of styles or irregular naming.
10 tools cover the essential Airtable operations (base/table discovery, record CRUD, search, and table metadata) without being excessive or too sparse for the domain.
The set covers core workflows: listing bases/tables, CRUD for records (including bulk operations), search, and table description. Minor gap: no explicit single-record update or delete, but bulk operations likely handle those cases.
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