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 goes beyond the annotations by disclosing the deactivation (not deletion) behavior, ownership enforcement, and the impact on historical events. This adds meaningful context that the annotations alone (readOnlyHint false, destructiveHint false) do not fully convey.
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 two sentences long and immediately front-loads the core action. Every clause adds value: the action, the ownership constraint, and the deactivation side effect. No filler or 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 simple one-parameter cancellation tool, the description covers the essential behavioral outcomes (deactivation, visibility of historical events). It does not describe the response format, but given the absence of an output schema and the low complexity, this is a minor gap.
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 schema already provides 100% coverage for the single parameter 'id', including its type and source ('returned by subscribe'). The description only repeats 'id' without adding new semantic details, so it does not meaningfully supplement the 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 opens with a specific verb+resource structure: 'Cancel a subscription by id.' It clearly distinguishes this tool from sibling tools like subscribe and list_subscriptions by focusing on the cancellation action and the unique ownership constraint.
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 implies when to use this tool (when you want to cancel a subscription) and provides a key prerequisite: ownership enforcement means you can only cancel your own subscriptions. It also hints at a non-alternative by noting that deactivation keeps history accessible via recent_alerts, though it does not explicitly name alternatives or exclusion cases.
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 tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same underlying engine and differ mainly in confidence/extraction mode. Similarly, bet_research, polymarket_edges, and polymarket_arbitrage all surface prediction-market opportunities, and scan_competitor_ai_presence is just a wrapper around ai_visibility_check. The detailed descriptions help, but an agent could easily pick the wrong one without reading very carefully.
Naming is a mix of verb-first snake_case (list_groups, search_within, resolve_entity, validate_claim) and noun-first patterns (pipeworx_trending, polymarket_arbitrage, dataset_details, entity_profile). There are also inconsistent verb styles (ask_, search_, list_, scan_, get_/fetch_ are absent). Within families like polymarket_* the pattern is consistent, but overall the set does not follow one clear convention.
At 36 tools, the server exceeds the 25-tool threshold for 'too many.' Even though the underlying Pipeworx catalog is vast, the MCP surface exposes far more than a typical agent needs for any single task, and the server name 'Datagov Il' implies a much narrower scope than the prediction-market, memory, and AI-visibility tools suggest.
For the apparent scope — authoritative data lookup, entity research, prediction-market analysis, subscription monitoring, and CKAN dataset access — the surface is fairly complete. It covers read/query, grounded verification, deep research, profile building, comparison, entity resolution, memory, and subscription lifecycle. Minor gaps exist (e.g., no CKAN write operations, no batch dataset export tool), but they do not block core workflows.