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?
Discloses key behavioral detail beyond annotations: 'The row is deactivated (not deleted) so its historical events stay available via recent_alerts.' This clarifies side effects and complements the annotations (destructiveHint=false).
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 with no wasted words. Front-loaded with action verb and object. Every sentence adds essential information.
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 single-parameter tool with no output schema, the description provides complete understanding: what it does, constraints, side effects, and relation to other tools. Combined with annotations, it is fully adequate.
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 has 100% coverage with a clear parameter description. The description adds cross-reference ('returned by subscribe'), adding marginal value beyond the schema. 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 the action ('Cancel a subscription by id') and distinguishes from sibling 'subscribe'. It specifies the resource and the precise operation.
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 mentions ownership enforcement ('you can only cancel your own subscriptions'), providing clear usage context. Does not explicitly state when not to use or name alternatives, but the context is sufficient.
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 ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research cluster is highly overlapping—beta is explicitly identical right now and grounded differs mainly in answer extraction. Several other pairs (ai_visibility_check vs scan_competitor_ai_presence, and the six prediction-market tools) also blur boundaries, making misselection likely.
Names are uniformly snake_case and benefit from clear prefixes (ask_pipeworx_, kcmo_, polymarket_, pipeworx_). However, conventions are mixed between bare verbs (forget, recall, remember), noun phrases (entity_profile, polymarket_edges, recent_alerts), and verb_noun forms, and similar names like polymarket_edges vs polymarket_edge_tracker add confusion.
34 tools is well into the bloat range, and only 3 are actually Kansas City-specific despite the server name. The surface bundles prediction markets, memory, feedback, llms.txt generation, and npm scanning alongside data lookup, making it heavy and unfocused; several meta-tools could be collapsed.
As a read-only data research platform, the surface is quite complete: discovery, querying, grounded answers, entity resolution, profiles, comparisons, claim verification, subscriptions, and memory are all covered with few dead ends. Minor gaps exist—no subscription update, no direct citation-URI fetch tool, and a thin KC-specific set—but agents can work around them.