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 indicate read-only, idempotent, non-destructive behavior. Description adds value by listing exact return fields (id, type, params, etc.), giving additional behavioral context beyond 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 sentences, front-loaded with the main action, no wasted words. Efficient and to the point.
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?
Simple tool with one optional parameter and no output schema. Description adequately covers purpose, usage, and return fields. Complete for the tool's complexity.
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% for the single parameter (include_inactive), and description does not add extra meaning beyond what the schema provides. Baseline score of 3 is appropriate.
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 'List the caller's active subscriptions' with specific verb and resource, and distinguishes from sibling tools 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 to use for reviewing monitoring before adding more subscriptions or finding an id to cancel, providing clear context without explicitly mentioning 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.
Several tools have overlapping or near-identical purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are one base query flow with different flavors, while entity_profile, compare_entities, and recent_changes all overlap on company information. The Polymarket family and the discovery/onboarding tools (discover_tools, suggest_questions, pipeworx_trending) also create boundary confusion despite good descriptions.
The naming is consistently snake_case and mostly readable, but it mixes verb_noun tools (compare_entities, resolve_entity, validate_claim) with noun-phrase tools (entity_profile, polymarket_arbitrage, pipeworx_trending, datasets, metadata). The ask_pipeworx variants use a beta/grounded suffix pattern that is not applied uniformly across the other tool families.
34 tools is well above the 25+ threshold and feels like several servers merged into one: US DOT catalog access, a generic Pipeworx data router, prediction-market tools, memory/subscription management, AI-visibility checks, and meta/discovery utilities. Many tools could be consolidated (the three ask_pipeworx variants, the five Polymarket tools, and the discovery trio).
The core data lifecycle is broadly covered: catalog search, metadata, querying, natural-language lookup, grounded verification, entity resolution, entity profiles, comparison, research, subscriptions, and memory all have working paths. However, the server is named around US DOT data but only datasets/metadata/query are DOT-specific, and the rest is an unrelated general-purpose data and prediction-market toolkit, leaving the stated domain feeling incomplete.