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 mark it read-only/idempotent; description adds that it defaults to active subscriptions and specifies the return fields, enhancing transparency.
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: first covers purpose and output, second covers usage. No extraneous words, information is front-loaded.
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?
Complete for a simple list tool: documents return fields, parameter behavior, and usage context. No output schema needed given the list of fields.
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 100% of the single parameter with description; tool description only reinforces schema content without adding new semantic value.
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?
Explicitly states the tool lists active subscriptions and enumerates the returned fields (id, type, etc.), clearly distinct from subscribe/unsubscribe siblings.
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?
Directly advises using this tool to review monitoring before adding more subscriptions or to find an id to cancel, providing clear when-to-use context.
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 multiple ask_pipeworx variants and several prediction market tools. This causes ambiguity for agents trying to select the right tool.
Tool names mix verb_noun patterns (ask_pipeworx, forget) with noun phrases (entity_profile) and inconsistent prefixes (pipeworx_, polymarket_). No consistent naming convention.
With 32 tools, the set is excessive for a server named 'Buzzword Density' and includes many redundant or overlapping tools. A more focused set of 10-15 would be more coherent.
The tool set covers a wide range of data sources and operations (retrieval, comparison, monitoring, memory), missing only minor lifecycle operations like updating stored data.