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Airtable

by io.github.pipeworx-io

Airtable Create Record

airtable_create_record
Idempotent

Add a new record to an Airtable table with specified field values. Returns the created record ID and full record data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseIdYesAirtable base ID
fieldsYesObject of field name/value pairs to set on the new record
_apiKeyYesAirtable personal access token
tableIdOrNameYesTable ID or name

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesID of the newly created record
fieldsYesField values of the created record
createdTimeYesISO timestamp when record was created

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "createdTime": {
+      "description": "ISO timestamp when record was created",
+      "type": "string"
+    },
+    "fields": {
+      "description": "Field values of the created record",
+      "type": "object"
+    },
+    "id": {
+      "description": "ID of the newly created record",
+      "type": "string"
+    }
+  },
+  "required": [
+    "id",
+    "fields",
+    "createdTime"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-airtable-api-key",
+    "baseId": "appXXXXXXXXXXXX",
+    "fields": {
+      "Name": "New Project",
+      "Owner": "John Doe",
+      "Status": "In Progress"
+    },
+    "tableIdOrName": "Tasks"
+  }
+]
  3. First observed

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description matches the annotations (readOnlyHint=false indicates write operation) and adds that it returns the created record ID and full data. However, it does not elaborate on idempotency (despite idempotentHint=true) or potential side effects, adding limited value beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description consists of two concise sentences that succinctly state the tool's purpose and output. No redundant or irrelevant content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (create record) and presence of output schema and annotations, the description covers the essential behavior and return value. It could optionally mention error handling or authentication details, but current completeness is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema description coverage is 100%, so the schema already documents all parameters. The description only generically mentions 'specified field values' without adding syntax, format, or usage tips beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool adds a new record to an Airtable table with specified field values, clearly distinguishing it from sibling tools like airtable_get_record (read) and airtable_list_records (list).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when creating a new record but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. It lacks 'when-to-use' or 'alternatives' context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Airtable toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

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 Consistency2/5

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.

Tool Count2/5

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.

Completeness2/5

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.

Resources