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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds value by enumerating the exact fields returned (id, type, params, created_at, last_fired_at, fire_count) and emphasizing 'active subscriptions' as the default scope, which complements the include_inactive parameter. No contradictions with 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: the first states the core function and return fields; the second gives practical use cases. No filler, front-loaded with the action, and every sentence earns its place.
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 tool's simplicity (one optional parameter, no output schema, strong annotations), the description is complete. It tells what the tool lists, what it returns, and why to use it, which is sufficient for an agent to select and invoke it correctly. No gaps that hinder usage.
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%: the sole parameter include_inactive is fully described in the input schema. The description does not add further parameter-level detail, but it does refer to 'active subscriptions' which aligns with the parameter's meaning. Baseline 3 is appropriate since the schema handles parameter semantics entirely.
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 the tool lists the caller's active subscriptions, specifies the returned fields, and implicitly distinguishes itself from sibling tools like subscribe and unsubscribe by focusing on listing. The verb 'list' and resource 'subscriptions' are specific and unambiguous.
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?
Explicit guidance is provided: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This gives concrete scenarios (pre-subscribe review and pre-unsubscribe id lookup) and implies the alternative tools (subscribe/unsubscribe) by context. It's clear when to invoke this tool versus siblings.
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.
Most tools fall into recognizable families (data lookup, entity research, prediction markets, memory, subscriptions), and the detailed descriptions help separate them. However, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants, and several polymarket scanning tools overlap in purpose enough to cause misselection.
Nearly all tool names are snake_case and readable, and families share clear prefixes like ask_pipeworx_*, polymarket_*, and pipeworx_*. The main inconsistency is that the Brazilian data endpoints use bare nouns (quote, crypto, currency, inflation, prime_rate) while most other tools use verb-like action names, so there is no single verb_noun pattern throughout.
38 tools is well above the 25+ threshold for a heavy MCP surface, even though the server aggregates several distinct domains. Each tool may have a purpose, but the sheer count makes the set difficult to navigate and suggests the server is trying to be a platform rather than a focused toolset.
The server covers the full lifecycle for its core areas: lookup (ask_pipeworx, grounded, deep_research), entity workflows (resolve, profile, compare, recent_changes), memory (remember/recall/forget), and subscriptions (subscribe/list/unsubscribe/recent_alerts). Minor gaps exist, such as no subscription update/pause and no direct tool to fetch an arbitrary pipeworx:// citation, but agents can work around these.