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 readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by listing all return fields (id, type, etc.) and any implied behavior like 'active subscriptions' (default filtering). 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 concise sentences with no filler. First sentence states purpose and return fields. Second sentence gives usage guidance. Every sentence is valuable.
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 no output schema, the description lists return fields adequately. However, it lacks details on pagination, sorting, or default limits. With annotations covering safety and idempotency, it is mostly complete for a simple list tool.
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'. The description does not mention this parameter, but the schema already documents it. No added 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?
The description clearly states 'List the caller's active subscriptions' with a specific verb and resource. It distinguishes from siblings like subscribe and unsubscribe by indicating this is the listing counterpart.
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 advises use cases: 'review what you're monitoring before adding more or to find an id to cancel', implying when to use this tool versus subscribing or unsubscribing. It could explicitly name siblings but the usage context is clear.
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 tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share routing and differ mainly in grounding/output, while polymarket_edges, polymarket_arbitrage, polymarket_kalshi_spread, and bet_research all surface opportunity-like signals. Strong descriptions mitigate but do not eliminate the risk of selecting the wrong one.
Most tools use snake_case, but conventions mix verb-first names (resolve_entity, generate_llms_txt), noun-first names (entity_profile, recent_alerts, polymarket_edges), and object-verb names (ai_visibility_check, bet_research). The polymorphic ask_pipeworx variants and pipeworx_* prefix group add further inconsistency, though each individual cluster is internally readable.
32 tools is well beyond the 16-25 range flagged as heavy, and the set includes unrelated subdomains: prediction markets, memory storage, npm dependency scanning, AI visibility probes, llms.txt generation, and a single meeting-analysis tool. The count feels like a bundled platform rather than a focused server.
The dominant Pipeworx data/research/prediction-market surface is strong: query, grounded query, deep research, entity resolution, comparison, change feeds, claim validation, arbitrage, fill-risk, subscriptions, and memory are covered. However, the server's namesake 'could have been email' domain contains exactly one tool, and unrelated utilities like generate_llms_txt and scan_dependency are not developed beyond single functions, leaving those implied surfaces incomplete.