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 read-only, idempotent, non-destructive behavior. Description adds that it returns specific fields and that 'active' is the default, with an optional parameter to include cancelled ones.
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 purpose, no wasted words. Structure is efficient and clear.
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 and no output schema, the description provides complete context: purpose, return structure, default behavior, and use cases.
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 single parameter include_inactive with full description. The tool description does not add meaning beyond the schema, meeting the baseline for high coverage.
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 'list the caller's active subscriptions' with specific verb and resource. Returned fields are listed. Distinguishes from sibling tools 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 states when to use: 'review what you're monitoring before adding more or to find an id to cancel.' Provides clear context without exclusions.
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 (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all answer questions via the same router). The set also includes generic memory and subscription tools that are unrelated to the DMV domain, making it hard for an agent to distinguish which tool to use for a given task.
No consistent naming pattern across the set. Some tools use a ny_dmv_ prefix, while others use generic verbs like ask_pipeworx, deep_research, or single-word names like recall, subscribe. This mix of conventions and styles makes the naming chaotic.
At 37 tools, the count is too high for a server purportedly about the NY DMV. Only about 7 tools are DMV-specific; the rest are generic Pipeworx utilities that inflate the surface unnecessarily. A focused DMV server should have fewer, more targeted tools.
The DMV-specific tools cover key areas: driving schools, facilities, offices, road test sites, registrations, and EV adoption. However, common DMV operations like license renewal, appointment scheduling, or fee lookups are missing. The generic tools add breadth but not depth for the DMV domain.