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 indicate readOnly, idempotent, non-destructive. The description adds value by specifying the returned fields and optional parameter, without contradicting annotations.
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 front-loading purpose and usage guidance, with no wasted words.
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?
For a simple list tool with no output schema, the description covers purpose, return fields, usage guidance, and the optional parameter adequately.
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% for the single parameter; the description does not add additional meaning beyond what the schema provides.
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 explicitly states 'List the caller's active subscriptions' with a specific verb and resource, and distinguishes from siblings like 'subscribe' and 'unsubscribe'.
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 advises using this tool to 'review what you're monitoring before adding more or to find an id to cancel', providing clear context for its use.
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.
Several tools occupy adjacent territory—ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions, and the Polymarket family has five overlapping analysis tools. The descriptions do a decent job of differentiating them, but an agent could still plausibly select the wrong variant in a mixed workflow.
Most names follow a readable lowercase snake_case style, and there are coherent families like ask_pipeworx_*, polymarket_*, and pipeworx_*. However, conventions are mixed across the set—some are verb_noun (list_subscriptions, resolve_entity), some are bare verbs (forget, recall, reverse), and some are noun-phrase-only (entity_profile, recent_alerts)—so no single predictable pattern governs the whole server.
33 tools is well past the 25+ threshold for a heavy tool surface, even accounting for the broad data-domain ambitions of the server. Many of these tools are meta-tools or thin variants of one another, so the set feels larger than necessary and imposes meaningful selection cost on an agent.
The server covers its apparent domain thoroughly: querying, grounded verification, deep research, entity resolution, profiles, comparisons, change tracking, claim validation, memory, subscriptions, and prediction-market analytics are all represented. Minor gaps exist—such as no direct tool for retrieving a raw pipeworx:// citation record and no account/auth flow—but agents can generally complete core workflows without dead ends.