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=true, destructiveHint=false, idempotentHint=true. The description adds value by specifying return fields (id, type, params, etc.) and default behavior (active only). Contradictions: none.
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 sentence defines purpose and output, second sentence provides usage guidance. Front-loaded with key information.
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 tool with one optional parameter, good annotations, and no output schema, the description covers purpose, parameters, return values, and usage advice. No gaps.
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 one parameter fully described. The description adds 'active subscriptions' context, which is already implied by the parameter's default false. Baseline 3 is appropriate; marginal additional meaning.
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 verb 'List' and the resource 'subscriptions', specifying it returns the caller's active subscriptions. It distinguishes from sibling tools like subscribe/unsubscribe by focusing on listing rather than creating or canceling.
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 explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel,' providing clear context for when to invoke. It does not explicitly exclude alternative use cases, but the guidance is 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.
The DMV-specific tools are individually distinct, but they are buried among ~30 unrelated Pipeworx utilities with several overlapping pairs (ask_pipeworx variants, entity_profile/compare_entities/recent_changes, and the polymarket suite). An agent pointed at this server cannot reliably tell which tools belong to the California DMV domain versus the general data platform.
Most tools use lowercase snake_case, and the ca_dmv_* family is consistent, but there is no coherent semantic pattern across the set: some names are noun phrases (entity_profile), some verb phrases (discover_tools, validate_claim), and many are product-specific prefixes (ask_pipeworx, polymarket_*). The formatting is consistent, but the naming conventions are mixed.
37 tools is far too many for a California DMV server; only 6 are actually DMV-specific. The remaining ~30 tools cover general Pipeworx data lookup, prediction markets, memory, subscriptions, and AI visibility, which belong in a separate server entirely.
For the stated DMV scope, the surface is thin: it covers registrations, licenses, offices, forms, insurance codes, and EV adoption, but misses common DMV queries like registration fees, title/status lookups, and appointment or transaction data. The 30 unrelated tools do not fill these domain gaps and instead obscure them.