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 indicate readOnlyHint and destructiveHint false. The description adds that it returns specific fields and defaults to active only, which provides additional context. No contradictions.
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?
The description is very concise: two sentences that convey purpose, return fields, and usage guidance. No unnecessary words, well-structured.
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 and no output schema, the description is sufficiently complete. It describes the action, return fields, and usage context. Slight lack of pagination info, but acceptable.
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 input schema has 100% coverage for the sole parameter include_inactive. The description only implicitly refers to the default behavior ('active'), but adds no new semantic meaning beyond the schema. Thus, baseline 3.
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 verb 'List', the resource 'subscriptions', and specifies scope 'caller's active subscriptions'. It also lists return fields, which helps the agent understand output. Differentiates 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?
The description explicitly states when to use: to review monitoring before adding more subscriptions or to find an id to cancel. This is clear guidance, though it does not explicitly mention when not to use.
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 tools overlap in purpose, particularly the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) where beta currently matches stable exactly, and the polymarket_* cluster with similar names. However, detailed descriptions clarify each tool's specific role, so an agent can usually select correctly with careful reading.
All names use snake_case and are generally descriptive, but they mix conventions: many are verb_noun (list_subscriptions, validate_claim), while others are noun phrases (entity_profile, recent_changes). Prefixes like oxylabs_ and polymarket_ are consistent, but the lack of a uniform verb-first pattern reduces predictability.
With 34 tools, the server exceeds the 25-tool threshold for 'too many'. While the broad scope (data lookup, scraping, prediction markets, memory, subscriptions) warrants a larger surface, the sheer number makes it difficult for agents to quickly identify the right tool without extensive scanning.
The tool set covers the core domain thoroughly: question answering, deep research, entity resolution, comparison, validation, web scraping, prediction market analysis, memory management, and subscriptions. Minor gaps like limited e-commerce scraping beyond Amazon and no direct data-writing tools exist, but they do not critically hamper workflows.