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?
The description adds important behavioral details beyond annotations: the row is deactivated (not deleted) and historical events remain available via recent_alerts. This complements the destructiveHint=false and idempotentHint annotations without contradicting them.
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 action, and every sentence provides useful context without redundancy.
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 single-parameter tool with no output schema, the description covers the action, ownership constraint, and the deactivation side-effect. It could mention error behavior, but the description is sufficiently complete for its 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?
The input schema already fully describes the 'id' parameter (uuid returned by subscribe) with 100% coverage. The description merely says 'by id', adding no new semantics beyond the schema, so baseline 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?
The description states 'Cancel a subscription by id', a specific verb and resource, and distinguishes this from siblings like subscribe and list_subscriptions.
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?
It clearly explains ownership enforcement and that only own subscriptions can be cancelled, providing context for when this tool is appropriate. It also notes the deactivation behavior. However, it doesn't explicitly name alternatives or state when not to use it.
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 near-duplicate tool clusters exist: ask_pipeworx, ask_pipeworx_beta (explicitly identical right now), and ask_pipeworx_grounded overlap heavily, as do polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread. The three ArcGIS tools (search_datasets, layer_info, query_layer) are distinct, but their purpose is drowned out by the unrelated Pipeworx data tools.
Most names use snake_case, but the pattern is inconsistent: some are verb_noun (ask_pipeworx, search_datasets, resolve_entity), some are noun_noun (layer_info, entity_profile, pipeworx_feedback), and some are adjective_noun (recent_alerts, recent_changes). Verbs are also inconsistent across similar actions (scan_ vs check_ vs compare_, and three different polymarket_ verbs plus a bare bet_research).
34 tools is already heavy, but the server is named Arcgis Pittsburgh and only 3 of the 34 tools relate to Pittsburgh GIS data; the other 31 belong to unrelated domains (Pipeworx data lookup, prediction markets, memory, npm auditing). This is an extreme scope mismatch — the tool count is far too high for the stated purpose and mostly irrelevant noise.
For the ArcGIS Pittsburgh domain, the surface has basic read coverage (search datasets, inspect layer schema, query records) but no update/delete/write operations and no geospatial analysis tools, which are significant gaps for a GIS server. The broader tool set is a grab bag of research, prediction-market, and memory features that don't form a coherent lifecycle for any single domain, so completeness cannot be assessed as a unified surface.