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=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds value by enumerating the return fields (id, type, params, created_at, last_fired_at, fire_count) and clarifying the default behavior of only listing active subscriptions. This goes beyond the annotations without contradicting them.
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, front-loaded with the core purpose and return fields, followed by a practical usage note. Every word earns its place; no filler or redundancy. This is an exemplar of concise, well-structured tool documentation.
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 list tool with one optional parameter, rich annotations, and no output schema, the description is complete. It states the default scope, lists return fields, and explains the use case. Nothing essential is missing given the tool's low complexity.
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% for the single optional parameter include_inactive, whose description ('Include cancelled subscriptions in the response (default false)') already explains semantics. The description does not mention the parameter, so it adds no additional meaning beyond the schema. Baseline 3 is appropriate for high schema coverage.
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 a specific verb+resource ('List the caller's active subscriptions') and clearly defines scope (caller's active subscriptions, with optional include_inactive). It distinguishes itself from siblings like subscribe and unsubscribe by focusing on listing rather than mutation.
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 states when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This implies alternatives (subscribe for adding, unsubscribe for canceling) but doesn't name them explicitly. It provides clear context but could be more explicit about when not to use it.
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 serve near-identical purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical today, ask_pipeworx_grounded and deep_research overlap heavily, and bet_research/polymarket_edges/polymarket_arbitrage all scan prediction-market opportunities. An agent reading these names and descriptions cannot reliably pick one tool without reading very long descriptions.
The names are all lower_snake_case, but the naming style is not consistent across the server: verb_noun (get_holidays, validate_claim), bare noun phrases (next_holidays, entity_profile, bet_research), is/are predicates (is_today_holiday), and separate pipeworx/polymarket prefixed groups. There are recognizable subgroups, but no coherent naming convention unifies the full tool surface.
34 tools is over the healthy range and the majority have nothing to do with holidays; they belong to a broader Pipeworx data, prediction-market, and subscription platform. The holiday-specific surface is only three tools buried inside a much larger, unrelated toolkit, making the server feel overloaded and mislabelled.
For the actual holiday domain the core read workflows are covered: all public holidays by country/year, today's holiday status, and upcoming holidays. A small gap is the lack of a supported-country/region listing or date-range filtering, but these are easily worked around because get_holidays returns the full year set.