list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare safety (readOnly, idempotent). Description adds value by specifying return fields and caller scoping, beyond what annotations provide.
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 concise sentences with no wasted words, front-loaded with core action.
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 simple tool (1 optional param, no output schema, strong annotations), description is fully complete: what, returns, when to use, and parameter behavior.
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% and the parameter description in schema is adequate. Description adds no new semantics beyond noting default behavior, so baseline 3.
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 it lists active subscriptions and enumerates returned fields. It distinguishes itself from siblings like subscribe/unsubscribe, though not explicitly naming them.
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?
Description recommends using the tool to review monitoring before adding more or to find an id to cancel, providing clear usage context. It implicitly contrasts with subscribe/unsubscribe.
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.