list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
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?
Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds value beyond annotations by specifying the return fields (id, type, params, etc.) and the default active-only scope. No contradictions with annotations.
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, no fluff. The first sentence states the action and return fields, the second gives usage guidance. Information is front-loaded and every word earns its place.
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 list tool with one optional parameter and rich annotations, the description covers purpose, usage, and return fields. The lack of an output schema is compensated by listing return fields. Complexities like pagination or errors are not relevant for this straightforward read operation.
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 coverage is 100% for the single parameter include_inactive, which has a clear description. The tool description does not elaborate further on the parameter, so the schema carries the semantic load, warranting the baseline score of 3.
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 'List the caller's active subscriptions' with a specific verb and resource, and lists the exact return fields. It also differentiates from sibling tools like subscribe and unsubscribe by framing the use case for reviewing monitoring and finding ids to cancel.
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 gives explicit usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' While it doesn't name alternatives directly, the context implies that subscribe/unsubscribe are the complementary tools, making the when-to-use clear.
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.
Tools are diverse across GIS, brand audits, prediction markets, and subscription management. While each tool has detailed descriptions, the broad domain mix confuses which tool to use for a given task. Some overlap exists among Pipeworx tools (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research), making disambiguation moderate.
Tool names follow snake_case but verbs vary (e.g., query_layer vs. ask_pipeworx vs. generate_llms_txt). Subgroups like 'polymarket_*' are consistent, but the overall set lacks a uniform naming pattern, reducing coherence.
33 tools is excessive for a server named after Lorain County GIS. Only three tools (layer_info, query_layer, search_datasets) relate to that purpose; the rest are unrelated APIs. The tool count is mismatched to the server's implied scope.
For a GIS server, the tool surface is incomplete—missing editing, upload, and administrative tools. Moreover, the inclusion of many irrelevant tools (e.g., prediction markets, npm package checks) dilutes focus and leaves the core domain under-served.