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 non-destructive nature. Description adds return field details and default behavior (active subscriptions), adding value 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 efficient sentences: first states purpose and output, second provides usage guidance. No unnecessary 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?
Fully specifies return fields, explains input parameter, and usage context. No output schema needed as description covers 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?
Schema has 100% coverage with clear description for the one parameter. Description reinforces default behavior ('active subscriptions') but does not add significant new semantics.
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?
Clearly states it lists subscriptions with specific fields returned, distinguishing 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?
Provides explicit use cases: review before adding more, find id to cancel. Contextualizes when to use the tool.
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 overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route natural-language questions to the same underlying data catalog, and the polymarket_* family has several near-synonymous scanning tools. Entity_profile, compare_entities, and recent_changes also have fuzzy boundaries. Only the three cat tools are clearly distinct, but an agent could easily pick the wrong tool across the Pipeworx family.
The names are consistently snake_case and several families share clear prefixes like ask_pipeworx, polymarket_, and list_. However, the set mixes imperative verb-first names with noun-style names, and the cat_* tools do not share any naming pattern with the dominant Pipeworx family.
34 tools is too many for a server named Cataas, and only 3 of them are actually cat-related. The other 31 tools belong to an unrelated data-research and prediction-market platform, making the tool count inappropriate for the apparent purpose.
As a cat-image server, the surface is thin: random_cat, cat_by_tag, and list_tags cover basics but omit common Cataas operations like GIFs or text-on-cat. As a data-research platform, the cat tools are noise, so the mixed set is incomplete and incoherent for either apparent purpose.