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 and destructiveHint, so the safety profile is clear. The description adds return field details (id, type, params, etc.), offering moderate behavioral context 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with the main purpose. Every sentence adds value 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, read-only listing tool with one parameter and no output schema, the description is fully complete: purpose, field details, and usage guidance are all covered.
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% with one parameter fully described. The description does not add significant meaning to the parameter beyond what the schema provides, so baseline 3 is appropriate.
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 action (list) and resource (subscriptions), specifying it returns active subscriptions and listing the fields. It distinguishes from sibling tools 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 provides explicit usage scenarios: review before adding more subscriptions or find an id to cancel. It implies alternatives (subscribe, unsubscribe) but does not explicitly state when not to 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 have overlapping or near-identical purposes: ask_pipeworx and ask_pipeworx_beta are currently exactly the same behavior, and polymarket_edges, polymarket_arbitrage, and bet_research all present as 'find a betting opportunity' scanners. The detailed descriptions help, but an agent still needs careful triage to avoid picking the wrong entry point.
The set is uniformly snake_case and many tools follow verb_noun (get_package, list_releases, resolve_entity), but conventions are split between product-prefixed families (ask_pipeworx, pipeworx_feedback, polymarket_*), noun-led names (entity_profile, recent_alerts, deep_research), and bare-verb memory tools (remember, recall, forget). The result is readable but not a single predictable pattern.
35 tools is well into the overgrown range, and the count is especially mismatched for a server labeled Pypi: only a handful of tools actually relate to Python packages, while the rest cover Pipeworx data lookup, prediction markets, AI visibility, memory, and subscriptions. This looks like several unrelated tool surfaces merged under one server.
For the broad Pipeworx data-research surface, coverage is strong: lookup, grounded answers, deep research, entity profiles, comparisons, validation, subscriptions, and memory are all represented. However, for a PyPI-focused server the surface is incomplete—there is no package search, upload, or account/maintainer tooling—and the PyPI tools feel like an afterthought.