airtable_get_base_schemaGet the structure of an Airtable base—all tables, field names, field types, and configurations. Use first to understand available data before querying or creating records.
| Name | Required | Description | Default |
|---|---|---|---|
| baseId | Yes | Airtable base ID | |
| _apiKey | Yes | Airtable personal access token |
| Name | Required | Description | Default |
|---|---|---|---|
| tables | Yes | List of tables in the base |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "tables": {
+ "description": "List of tables in the base",
+ "items": {
+ "properties": {
+ "fields": {
+ "description": "Fields defined in this table",
+ "items": {
+ "properties": {
+ "id": {
+ "description": "Field ID",
+ "type": "string"
+ },
+ "name": {
+ "description": "Field name",
+ "type": "string"
+ },
+ "type": {
+ "description": "Field type (e.g., singleLineText, number, checkbox)",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "id": {
+ "description": "Table ID",
+ "type": "string"
+ },
+ "name": {
+ "description": "Table name",
+ "type": "string"
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ }
+ },
+ "required": [
+ "tables"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {
+ "_apiKey": "your-airtable-api-key",
+ "baseId": "appXXXXXXXXXXXX"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description adds minimal extra transparency. It mentions 'all tables, field names, field types, and configurations', which is slightly more detail but not essential. No additional behavioral context like rate limits or authentication nuances is added.
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?
Two sentences, front-loaded with purpose, followed by usage guidance. Every sentence adds value, no filler.
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 output schema exists, the description need not explain return values. It sufficiently covers the tool's purpose, usage context, and output nature. All necessary information is present for an agent to use this 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?
Input schema has 100% coverage with descriptions for both baseId and _apiKey. The description does not add any further meaning to the parameters beyond what the schema provides, so baseline 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 'Get the structure of an Airtable base—all tables, field names, field types, and configurations', using a specific verb and resource. It effectively distinguishes from sibling tools like airtable_list_bases and airtable_get_record.
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 second sentence 'Use first to understand available data before querying or creating records' provides explicit guidance on when to use this tool as a preliminary step. It does not mention when not to use, but the advice is clear and useful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The Airtable tools are distinct, but the set is dominated by a large Pipeworx research family with multiple near-identical entries (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and several overlapping prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage). An agent would frequently struggle to pick the right tool among the many data-lookup and research options, especially given the server is supposedly named Airtable.
Naming conventions are mixed: some tools use verb_noun snake_case (airtable_create_record, list_subscriptions, resolve_entity), while others use domain-prefixed names (pipeworx_feedback, polymarket_edges) or bare verbs (remember, forget, recall, subscribe). There is no single predictable pattern across the set.
36 tools is heavy for any single server's scope, and the mismatch is worse because the server is named Airtable yet only 5 of 36 tools relate to Airtable. The rest form an unrelated Pipeworx/Polymarket/memory grab-bag, suggesting poor scoping and no clear purpose for the set as a whole.
For the stated Airtable domain, the surface is incomplete: records can be created, fetched, and listed, but there is no update_record or delete_record. For the broader Pipeworx/prediction-market domain the coverage is extensive but unfocused, and given the server's name the Airtable gap is glaring.