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Glama

Airtable

by io.github.pipeworx-io

Remember

remember
Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Discloses key-value scoping, persistence details (auth vs anonymous), and idempotency hints. No contradiction with annotations; annotations only hint at idempotent and non-destructive, and description aligns.

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?

Three concise sentences. First sentence states core purpose, second adds usage guidance, third provides behavioral detail. No wasted words.

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

Completeness5/5

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

Despite no output schema, the description fully explains what the tool does, how to use it, and its behavior. For a simple key-value store, this is complete.

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

Parameters4/5

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

Schema already covers both parameters fully (100% coverage). Description adds usage examples and naming conventions, providing extra value beyond schema descriptions.

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 uses specific verbs ('Save data') and resources ('across this conversation or across sessions') with concrete examples (ticker, address, preference). It clearly distinguishes from sibling tools recall and forget.

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

Usage Guidelines5/5

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

Explicitly states when to use ('discover something worth carrying forward') and pairs with recall/forget. Provides clear context for invocation.

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