unsubscribeCancel a subscription by id. Ownership is enforced — you can only cancel your own subscriptions. The row is deactivated (not deleted) so its historical events stay available via recent_alerts.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Subscription id (uuid) returned by subscribe. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate non-read-only, non-destructive, idempotent. Description adds that the row is deactivated (not deleted) so historical events persist. This clarifies exact mutation behavior and aligns with annotations (no contradiction).
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?
Three concise sentences, each serving a distinct purpose: action, constraint, and side-effect. No unnecessary words.
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 and simple single-parameter input, description covers ownership, effect (deactivation), and historical availability. Lacks error handling or state idempotent guarantee, but sufficient for typical use.
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 description for 'id' covers format (uuid) and source (returned by subscribe). Description adds the ownership constraint (id must belong to caller), which is meaningful context not in 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?
Description states 'Cancel a subscription by id' — a specific verb (cancel) and resource (subscription) with scope (by id). It distinguishes from sibling 'subscribe' by implication and mentions ownership enforcement.
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 'Ownership is enforced — you can only cancel your own subscriptions', a clear usage condition. Does not explicitly list alternatives like 'subscribe' but context suggests it's the inverse operation.
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.
ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all perform overlapping routed-search functions; ask_pipeworx_beta is even documented as currently identical to ask_pipeworx. The three Polymarket discovery tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) also have blurry boundaries around finding vs. validating vs. executing on edges.
Nearly all tools use snake_case with a verb_noun or descriptive pattern (ask_pipeworx, validate_claim, resolve_entity, list_subscriptions). Minor deviations exist — bare verbs like remember/recall/forget and noun_first names like bet_research or entity_profile — but the convention is largely predictable and readable.
32 tools is heavy, and the set spans unrelated domains: Pipeworx data routing, prediction-market trading, agent memory, subscription management, npm dependency checks, user-agent parsing, and llms.txt generation. The sub-clusters each earn their place individually, but as a single server surface the count is unjustifiably large and scattershot.
The Pipeworx research surface is quite complete (lookup, grounded answers, deep research, entity profiles, comparisons, validation, entity resolution, discovery, feedback), and memory/subscription lifecycles are fully covered. However, the server has no coherent single domain — user-agent parsing (the server's namesake) has only one tool, while unrelated utilities like generate_llms_txt and scan_dependency appear with no supporting ecosystem.