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, and destructiveHint. The description adds useful context about return fields and the default behavior of listing only active subscriptions. No contradictions.
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?
The description is two sentences: the first covers purpose and output, the second gives usage guidance. It is concise, front-loaded, and contains no fluff.
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?
Given the tool's simplicity (one optional param, no output schema needed, annotations cover safety), the description is fully complete. It explains what it does, what it returns, and when to use it.
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%, so the schema already documents the single parameter. The description adds marginal value beyond the schema by implying the parameter's effect through default behavior, but does not explicitly describe it.
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 tool lists the caller's active subscriptions and specifies the returned fields. It distinguishes itself from sibling tools like 'subscribe' and 'unsubscribe' by being a list operation.
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 gives explicit guidance: use it to review subscriptions before adding more or to find an id to cancel. It could be more explicit about when not to use, but the context of siblings provides implicit differentiation.
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.
Several tools are effectively duplicates or near-duplicates: ask_pipeworx and ask_pipeworx_beta are described as currently identical, ai_visibility_check and scan_competitor_ai_presence overlap heavily, and the polymarket_arbitrage/polymarket_edges/polymarket_fill_risk cluster has fuzzy boundaries. Even within the DMV subset, de_dmv_ev_adoption and de_dmv_vehicle_registrations both answer overlapping EV-count questions.
The de_dmv_* tools follow one snake_case pattern, but the rest of the set mixes bare nouns, brand-prefixed verbs, and generic names (entity_profile, remember, generate_llms_txt, ask_pipeworx_beta). There is no consistent verb_noun or domain-prefix convention across the 36 tools.
36 tools is over the threshold where a tool set becomes hard to navigate, and most of them have nothing to do with a Delaware DMV server. Only five tools are DMV-related; the rest are a general-purpose Pipeworx data, memory, and prediction-market toolkit, which makes the set feel bloated and mis-scoped.
For a server named Delaware DMV, the surface is missing core DMV capabilities like driver licenses, vehicle titling, registration renewals, appointments, or fee lookups. The five de_dmv_* tools cover only EV adoption, rebates, charger rebates, crash stats, and registration counts, leaving obvious domain gaps.