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 declare readOnly=true and destructive=false; description adds return fields and default 'active' scope. Does not contradict annotations.
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 efficient sentences with purpose first. No redundancy, every phrase adds value.
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 (one optional param, no output schema), the description fully covers purpose, return fields, and usage 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 coverage is 100% with clear description for 'include_inactive'. Description adds no extra meaning beyond schema.
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?
Verb 'list' + resource 'subscriptions' clearly stated. Returns specific fields, distinguishing from siblings like '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 says when to use: 'review what you're monitoring before adding more or to find an id to cancel'. Does not explicitly exclude alternatives but context strongly implies when not to use.
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.
Multiple tools occupy the same general query/research space: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, validate_claim, entity_profile, and recent_changes all overlap in what they can return. The descriptions are detailed and try to steer usage, but the boundaries are fuzzy enough that agents can easily select the wrong tool.
All names are snake_case and descriptive, but there is no consistent verb_noun convention across the set. It mixes bare verbs (remember, recall, forget), noun phrases (entity_profile, recent_changes), domain-prefixed families (securitytrails_*, polymarket_*), and Pipeworx meta-tools (ask_pipeworx_*), so the pattern is predictable only within each family.
35 tools is above the 25+ threshold for a coherent MCP surface, and many are highly specialized (Polymarket arbitrage, AI visibility checks, npm dependency scans) rather than core Securitytrails functionality. The set feels like multiple products merged into one rather than a well-scoped toolset.
The broad data-research workflows are well covered: routing, entity resolution, profiling, comparison, validation, subscriptions, memory, and basic Securitytrails domain lookups. But for a server named Securitytrails, there are obvious missing security-intelligence operations such as associated domains, IP/certificate enrichment, and broader DNS infrastructure enumeration.