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 the tool as read-only, idempotent, and non-destructive, so the description doesn't need to repeat that. It adds value by revealing the exact fields returned (id, type, params, created_at, last_fired_at, fire_count) and clarifying that only the caller's subscriptions are shown. This goes beyond the structured metadata.
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?
The description is two sentences, front-loaded with the primary purpose, then return fields, then a practical use case. Every sentence carries useful information with no fluff or repetition.
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), the description is reasonably complete. It covers the scope ('caller's subscriptions'), the returned fields, and the intended use case. It does not mention potential quirks like pagination or rate limits, but these are not critical for a basic list operation and the annotations cover safety aspects.
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?
The input schema covers the only parameter (include_inactive) with a complete description, so the schema already documents the semantics. The tool description does not add additional meaning to the parameter, but it does imply the default behavior by saying 'active subscriptions'. This aligns with the schema's default of false, yet no extra detail is provided.
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, using a specific verb and resource. It distinguishes itself from sibling tools like subscribe and unsubscribe, and even enumerates the return fields for clarity.
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 when-to-use guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' It implies this is the appropriate tool for reviewing subscriptions before subscribe/unsubscribe actions, though it does not explicitly state when not to use it or mention alternative tools.
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.
Heavy overlap within the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta which is currently identical, ask_pipeworx_grounded, deep_research) and among prediction market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) makes selection genuinely ambiguous. Some tools are distinct (remember/recall/forget), but the clustering blurs boundaries.
Conventions are mixed: some tools use verb_noun (list_games, get_game, filter_games, subscribe, unsubscribe, recall, remember) while others use ad-hoc noun phrases or brand-style names (polymarket_arbitrage, entity_profile, deep_research, ask_pipeworx, bet_research). No single pattern dominates, making it harder to predict tool names.
34 tools is heavy, and the mismatch is severe: the server is named 'videogames' but only 3 of 34 tools (list_games, get_game, filter_games) have any relation to video games. The bulk are Pipeworx data/prediction-market/memory utilities that do not belong under this server's apparent purpose. This is a fundamental scope failure.
As a video game server, the surface is extremely thin: only list/get/filter by tags and platform, with no search by name, no CRUD, no reviews, no categories beyond the fixed filter set. The other 31 tools are irrelevant to the videogames domain, so the stated purpose is largely uncovered. The mismatch makes coverage assessment nearly impossible for the actual server name.