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. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond annotations: it explains ownership enforcement (not in annotations), and clarifies that the operation deactivates rather than deletes (consistent with destructiveHint=false). This provides the agent with crucial behavioral information not available in annotations alone.
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?
The description is highly concise with three sentences covering action, constraint, and effect. Every sentence adds essential value with no redundant or filler content.
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 the simple input schema (one parameter) and no output schema, the description provides all necessary context: what the tool does, who can use it, and what the outcome is (deactivation, linking to 'recent_alerts'). It is complete for the tool's complexity level.
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 coverage is 100% (the only parameter 'id' is fully described in schema). The tool description does not add new semantic meaning beyond what's already in the schema, so 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?
The description clearly states the action ('Cancel a subscription by id') and the resource ('subscription'). The verb 'cancel' and title 'Unsubscribe from Alerts' specify the exact operation, distinguishing it from sibling tools 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?
The description provides usage context by noting ownership enforcement ('you can only cancel your own subscriptions') and the effect on data ('deactivated not deleted'). It implicitly guides against using this tool for other actions, but lacks explicit when-not-to-use or alternative tool references beyond mentioning 'recent_alerts'.
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.
Many tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve query/research; polymarket_edges, polymarket_arbitrage, bet_research, and polymarket_edge_tracker all analyze prediction markets; entity_profile, compare_entities, and recent_changes overlap on company research. An agent could easily select the wrong one.
All names are lowercase snake_case, but the verb-noun convention is inconsistent: some are verb-first (ask_pipeworx, generate_llms_txt, validate_claim), some noun-first (entity_profile, polymarket_edges, recent_changes), and some are bare nouns (query, metadata, datasets). The pattern is readable but not uniform.
34 tools is excessive for the apparent scope, especially given the server name suggests a Chicago city-data focus while the vast majority of tools are generic data-research, prediction-market, and memory utilities. This feels like a kitchen-sink bundle rather than a focused, well-scoped toolset.
The tool surface is a disjointed collection covering querying, research, memory, subscriptions, and prediction markets, but it lacks coherent lifecycle coverage for any single domain. For the named 'Cityofchicago' purpose, there is almost no city-specific functionality, and even as a general data tool, obvious gaps remain (e.g., no direct dataset management or update/delete operations).