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?
The annotations already establish this as a safe, read-only, idempotent operation, so the description does not need to disclose that. It adds value by specifying the response scope ('the caller's active subscriptions') and the exact return fields, which are useful behavioral details beyond the annotations. 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?
The description is two sentences: the first states the purpose and the return values, the second gives usage guidance. Every word earns its place, and the key information is front-loaded. This is a model of conciseness.
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 no output schema, the description fully covers the purpose, scope, return fields, and common use cases. It is complete and leaves no important gaps for an agent to invoke it correctly.
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 only parameter (include_inactive) is fully described in the schema with a clear description, and schema coverage is 100%. The tool description does not add any extra parameter semantics beyond what the schema provides, so the 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 ('List') and the resource ('the caller's active subscriptions'), and it enumerates the return fields (id, type, params, etc.), which distinguishes it from sibling tools like subscribe and unsubscribe. This is a specific and unambiguous statement of what the tool does.
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 second sentence provides explicit context for when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' It does not mention when not to use it or name alternatives, but the context is clear enough given the sibling tools (subscribe/unsubscribe). This earns a 4 rather than a 5 because it lacks explicit when-not/exclusion guidance.
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 like ask_pipeworx, ask_pipeworx_grounded, deep_research, and entity_profile have overlapping purposes, causing potential confusion. However, their descriptions provide some differentiation, so an agent can usually pick the right one with careful reading.
Naming is highly inconsistent: mixes verb_noun (ask_pipeworx), noun_verb (reverse_dns), single-word (geoip), and compound phrases (generate_llms_txt). No clear pattern, making it hard for an agent to predict tool names.
44 tools is overwhelmingly high for a single server. The set mixes unrelated domains (network tools, data APIs, memory, prediction markets), suggesting it's a grab bag rather than a focused toolkit.
The server lacks a coherent domain, so evaluating completeness is difficult. There are many lookup tools but few for updates or deletes (except memory). The HackerTarget subset is sparse, and the overall surface feels incomplete for any single purpose.