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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool is known to be a safe read operation. The description adds meaningful context by specifying it lists the caller's subscriptions (not all system-wide), returns only active ones by default, and enumerates the exact fields returned, going beyond the annotation-only picture.
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, with the first clearly stating the action and output fields and the second providing practical usage advice. Every sentence is informative with no redundancy or filler.
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 read-only list tool with no output schema, the description supplies the essential return fields, the default filtering behavior, and a clear use case. It is complete enough for an agent to select and invoke the tool correctly without further clarification.
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 has 100% coverage for the only parameter, include_inactive, with a description. The tool description does not add extra parameter semantics beyond what the schema already provides, 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 tool lists the caller's active subscriptions, which is a specific verb + resource. It distinguishes itself from the sibling tools subscribe and unsubscribe by focusing on listing, and it also explicitly lists the fields returned.
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 explicit usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This clearly indicates when to use the tool, though it does not explicitly mention alternatives or when not to use it, so it misses a small piece of the highest benchmark.
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.
Each tool has a clearly distinct purpose and description, with no overlap. For example, ask_pipeworx vs ask_pipeworx_grounded are differentiated by hallucination resistance, and all Polymarket tools have unique roles.
Tool names follow a mostly consistent snake_case pattern with descriptive verb_noun structures (e.g., ask_pipeworx, get_intensity, resolve_entity). Minor deviations like generate_llms_txt and pipeworx_feedback do not cause confusion.
33 tools is high for a single server, covering many domains (Pipeworx, Polymarket, electricity, memory, npm). While well-organized, the broad scope may feel bloated for a focused use case.
Within each subdomain, the tool surface is complete: Pipeworx has query, research, entities, subscriptions; Polymarket has arbitrage, edges, fill risk; electricity has mix and intensity. Only minor gaps exist (e.g., missing PyPI support in scan_dependency).