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=true and idempotentHint=true. The description adds value by specifying the return fields and clarifying that only active subscriptions are listed by default, with an option to include inactive ones. No contradiction.
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, no wasted words. Purpose is front-loaded, and the guidance is concise.
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 boolean parameter, the description covers the purpose, return fields, and usage context. No output schema exists, but the return fields are listed. Completely adequate.
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 parameter include_inactive is well-documented in the schema. The description implicitly references it by mentioning 'active subscriptions' but does not add new meaning beyond the schema. Baseline of 3 applies.
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 tool lists the caller's active subscriptions and enumerates the return fields (id, type, params, etc.). It distinguishes itself from sibling tools like subscribe and unsubscribe by focusing on listing.
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 guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent when to use it, though it doesn't mention 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.
Several tools have overlapping functionality, especially in the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the polymarket group (bet_research, polymarket_edges, etc.). Descriptions acknowledge these overlaps, making it difficult for an agent to choose the correct tool without deep understanding.
Naming conventions are inconsistent: some tools use snake_case (ask_pipeworx, bet_research), others use camelCase or PascalCase (ai_visibility_check, generate_llms_txt, polymarketArbitrage?). Also, tools like 'list_subjects' and 'get_data' have no clear pattern with the rest.
With 35 tools, the server feels overloaded and tries to cover too many distinct domains (data queries, prediction markets, subscriptions, memory, national statistics). This broad scope reduces coherence and makes the tool set harder to navigate.
The tool set covers a wide range of capabilities, from data retrieval to prediction market analysis and subscription management. However, the inclusion of Denmark-specific statistics tools (e.g., list_subjects, get_data) seems out of place and creates a niche gap for users interested in other national datasets.