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, destructiveHint; description adds that it returns specific fields and defaults to active subscriptions, which is consistent.
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 with no waste: first states purpose and return fields, second gives usage guidance.
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?
Covers purpose, return fields, and usage context. No output schema, but return fields are specified. Adequate for a simple list tool.
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 covers the parameter fully; description doesn't add extra semantics beyond default behavior implied by 'active subscriptions'.
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 the caller's active subscriptions, specifying return fields. Distinct 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?
Explicitly advises use before adding more subscriptions or to find an id to cancel, providing clear context for when to invoke.
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.
Many tools have overlapping purposes, such as ask_pipeworx vs ask_pipeworx_grounded and the multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research). While some tools are clearly distinct (e.g., geocode vs forecast), the high number of similar tools increases the risk of agent misselection.
Naming conventions are inconsistent. Some tools use a consistent verb_noun pattern (e.g., ask_pipeworx, resolve_entity), while others have no prefix (remember, recall) or use a domain prefix (polymarket_arbitrage, pipeworx_feedback). The mix of styles and lack of a unified pattern reduces predictability.
With 33 tools, the set is too large for a focused server, especially given the name 'Open Meteo' which implies weather tools only. Many tools are redundant or cover vastly different domains, making the count feel bloated and difficult for an agent to navigate efficiently.
The tool set covers an impressively wide range of capabilities: weather data, SEC filings, Polymarket analysis, memory management, and more. For the actual scope of data querying and analysis, there are few obvious gaps (e.g., no direct database query tool beyond ask_pipeworx). However, the completeness relative to the implied weather domain is poor.