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 declare readOnlyHint, idempotentHint, destructiveHint. Description adds that it returns specific fields and defaults to active subscriptions, which aids understanding beyond the 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, front-loaded with the core action and return fields, then usage advice. No unnecessary words.
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 no output schema, the description explains the returned fields, purpose, and usage, covering all necessary context.
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 clear description for the only parameter (include_inactive). The tool description does not add extra meaning beyond what the schema already provides.
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 it lists the caller's active subscriptions and mentions returned fields (id, type, etc.), differentiating it 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 provides specific use cases (review before adding more, find id to cancel), guiding when to use. It doesn't explicitly exclude scenarios, but the guidance is clear and sufficient.
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 distinct purposes with detailed descriptions; however, the family of ask_pipeworx tools (beta, grounded) and deep_research could cause selection ambiguity despite clear documentation.
Tool names lack a consistent pattern; they mix imperatives, descriptive nouns, and domain prefixes. While overall readable, the lack of uniformity makes it harder to predict naming conventions.
34 tools is on the higher side but still within reasonable range given the broad scope (data queries, prediction markets, scraping, subscriptions, memory). Each tool appears purposeful, though some consolidation (e.g., ask_pipeworx variants) could reduce count.
The tool set covers a wide range of tasks from data querying to prediction market analysis and entity management. Minor gaps might exist (e.g., no direct social media data), but the overall coverage is extensive and sufficient for the platform's purpose.