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, destructiveHint=false. The description adds return field details and scoping to 'caller's' subscriptions, which is useful behavioral context 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 sentences: first states purpose and return fields, second gives usage guidance. No wasted words, front-loaded with key information.
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 param and no output schema, the description covers purpose, return fields, and usage. Could mention pagination if applicable, but overall complete.
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 description for the sole parameter. The description does not add new parameter meaning but reinforces default behavior (active subscriptions). Baseline 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 states 'List the caller's active subscriptions' – a specific verb (list) and resource (subscriptions). It clearly distinguishes from siblings like subscribe and unsubscribe by focusing on reading existing subscriptions.
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?
Explicitly advises 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This gives clear when-to-use context and aligns with sibling tools (subscribe, unsubscribe).
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.
Several tool groups blur together: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve overlapping discovery/routing purposes. The six polymarket_* tools similarly overlap in edge/arbitrage detection, and the three license tools could be confused despite distinct intents.
Naming is mostly snake_case and readable, with coherent families like ask_pipeworx*, polymarket_*, and pipeworx_*. However, conventions vary widely: bare verbs (remember, forget, recall), noun phrases (entity_profile, bet_research), and inconsistent verb prefixes (search_licenses, lookup_license, get_license_text) prevent a predictable pattern.
34 tools is heavy for a single MCP server, and the server is named 'Licenses' while only 3 of 34 tools actually relate to licenses. The bulk is a sprawling data-research, prediction-market, memory, and subscription platform, which makes the count feel bloated and misaligned with the server's stated identity.
For the actual (broad) scope, the tool surface is quite thorough: memory CRUD, subscription lifecycle, license search/lookup/text, entity resolution, profiling, comparison, claim validation, research routing, and prediction-market edge analysis are all covered. Minor gaps exist—no subscription update, no license comparison, and ask_pipeworx_beta is a redundant variant—but no major workflow dead-ends.