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). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only, idempotent, and non-destructive. The description adds context by disclosing the return fields and scoping to active subscriptions, which goes beyond the annotations and helps set expectations for the response shape.
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 sentences with no filler: the first states purpose and output, the second gives usage guidance. Information is front-loaded and every clause 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?
For a simple, single-parameter list tool with no output schema, the description is fully sufficient. It explains what is returned, the default scope, and when to use it, covering the essential context an agent needs.
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 a clear description for the include_inactive parameter. The description's mention of 'active subscriptions' aligns with the default behavior but adds no extra semantic detail beyond what the schema already provides, so a baseline score of 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 uses the specific verb 'List' and clearly identifies the resource as the caller's active subscriptions, naming the exact fields returned. It distinguishes itself from sibling 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 gives clear usage context: use it to review monitored topics before adding more or to find an id to cancel. It implicitly points to alternatives (subscribe/unsubscribe) without naming them, but the guidance is actionable and specific enough for an agent.
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.
Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer research questions, and the Kitsu-specific tools are mixed with unrelated Polymarket, memory, and utility tools. Despite detailed descriptions, the boundaries between many tools are unclear.
The server mixes single-word nouns (anime, manga, categories), verb_noun pairs (search_anime, top_anime), verb phrases (ask_pipeworx, generate_llms_txt), and domain-prefixed families (polymarket_*, pipeworx_*) with no consistent overall convention. While some sub-families are internally consistent, the set as a whole lacks a predictable pattern.
38 tools is excessive for a server ostensibly about Kitsu anime/manga, with only 7 tools actually serving that domain. The rest are unrelated (Pipeworx research, Polymarket trading, memory, subscriptions), making the set bloated and unfocused for its stated purpose.
The Kitsu domain lacks common operations like filtered search, character/episode data, or user lists. Meanwhile, the Pipeworx/prediction-market tools form an arbitrary subset of their domains (e.g., no general Kalshi data, no SEC full-text search), so the overall surface is incomplete for any single purpose.