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, idempotentHint, and destructiveHint. Description adds that it returns specific fields and defaults to active subscriptions, which is useful 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 without any fluff. The first states purpose and output, the second gives usage guidance. Efficient and 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 tool with one optional param and no output schema, the description explains what is returned and when to use it. It is complete enough for an agent to use correctly.
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%, and the description does not add significant new meaning about the parameter beyond what the schema provides. Baseline 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?
Clearly states it lists active subscriptions and specifies the returned fields. It is distinct from sibling tools like subscribe/unsubscribe, though no explicit differentiation is made.
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 recommends using it before adding more subscriptions or to find an id to cancel, providing clear context. Does not mention when not to use or alternatives, but sufficient for the simple 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.
Most tools have clearly distinct purposes, especially with the prefix groupings (polymarket_*, neso_*, ask_pipeworx variants). However, the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and deep_research could cause confusion about which to use, though descriptions are detailed enough to differentiate. Overlap between ai_visibility_check and scan_competitor_ai_presence is also manageable.
All tool names follow a consistent snake_case convention, with clear verb_noun or domain_prefix patterns (e.g., neso_search_datasets, polymarket_arbitrage, resolve_entity). Naming is highly predictable and organized by functional domain.
With 35 tools, the surface is heavy, exceeding the typical well-scoped range of 3-15. While the server spans diverse domains (data lookup, prediction markets, energy, memory, subscriptions), many tools are meta-tools or variations (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, discover_tools, suggest_questions) that inflate the count. It feels over-stuffed, though not chaotic.
The tool set covers a broad and rich set of capabilities: data lookups, entity resolution, research, prediction market analysis, energy data, memory management, and subscriptions. Core workflows like CRUD for memory (remember/recall/forget) and subscriptions (subscribe/unsubscribe/recent_alerts) are complete. Minor gaps exist (e.g., no subscription update, no trade execution on prediction markets), but these seem intentional.