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). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide safety and idempotence cues. Description adds return field details and usage context. 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?
Two sentences: first states purpose and return, second gives usage guidance. No fluff, 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?
Sufficient for a simple list tool with one optional param. Return fields are enumerated. No missing critical 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 covers 100% of parameters, so description adds minimal value. The mention of 'active' aligns with default false for include_inactive.
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?
Description clearly states verb (list), resource (subscriptions), scope (caller's active), and lists returned fields. It distinguishes from sibling tools like subscribe/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: to review monitoring before adding more or to find an id to cancel. Implicitly contrasts with subscribe/unsubscribe.
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 server includes many overlapping tools (e.g., multiple ask_pipeworx variants, epa_regulation vs. epa_search vs. discover_tools). More critically, the tool set covers vastly different domains (Polymarket bets, npm packages, AI visibility, memory storage) alongside EPA regulations, making it hard for an agent to distinguish purposes.
Tool names use a mix of styles: underscore (epa_regulation, ask_pipeworx), camelCase (deep_research, suggest_questions), and verb phrases (scan_competitor_ai_presence). No consistent pattern is followed across the set.
33 tools is high for a server named 'Epa Regulations'. The vast majority are unrelated to EPA regulations (e.g., Polymarket, npm scanning, memory functions), making the scope mismatched. A focused server should have fewer, domain-specific tools.
For a server claiming to be about EPA regulations, only two tools (epa_regulation, epa_search) are directly relevant. The rest are from unrelated domains, leaving severe gaps in expected functionality like rule updates, compliance checks, or cross-referencing with other environmental data.