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 convey readOnlyHint and destructiveHint, so the safety profile is covered. The description adds value by detailing the return fields (id, type, params, created_at, last_fired_at, fire_count) and the caller-specific scope, enhancing transparency beyond the structured data.
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?
Three sentences: purpose, return fields, and usage guidance. Information is front-loaded and each sentence serves a distinct purpose with no filler or redundancy.
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?
The tool is simple (one optional parameter, no output schema) and the description provides sufficient context: what is returned, the scope ('caller's'), and typical use cases. Combined with strong annotations, there are no significant gaps for an agent to mis-invoke it.
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 single optional parameter include_inactive is fully described in the schema with 100% coverage. The tool description does not add additional meaning about the parameter, so it relies on the schema, which is acceptable per the baseline.
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 ('List') and resource ('subscriptions'). It distinguishes itself from sibling mutation tools like subscribe and unsubscribe by focusing on read-only listing.
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 explicitly suggests when to use the tool: to review current monitoring before adding more subscriptions or to find a subscription id to cancel. While it doesn't name specific alternative tools, the context is clear enough for an agent to decide between this list operation and mutation operations.
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 server is named 'wikipedia' but most tools are unrelated Pipeworx/Polymarket tools, so an agent asked to use Wikipedia tools will face a large misleading option set. Even within families there is blurriness: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research overlap, and the five polymarket_* tools have closely related purposes that require reading very long descriptions to disambiguate.
Naming conventions are mixed: some tools use clean verb_noun patterns (search_wikipedia, resolve_entity, validate_claim) while others use product prefixes (ask_pipeworx, pipeworx_trending, polymarket_edges) or noun-phrase names (entity_profile, recent_changes, bet_research). There is no single consistent pattern across the set.
36 tools is already heavy, but it is especially inappropriate for a server named 'wikipedia' — only a handful are actually Wikipedia tools, while the rest belong to unrelated domains (Pipeworx data, Polymarket betting, memory, subscriptions, npm scanning). The count reflects a kitchen-sink scope rather than a focused purpose.
The Wikipedia-reading subset (search, summary, sections, extract, random) is decent but lacks editing, category, or link features. The broader Pipeworx/Polymarket surface is quite comprehensive, so completeness depends entirely on which implicit domain you judge it against; as a 'wikipedia' server it is incomplete, and as a unified data platform the scope is still incoherent.