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, idempotentHint, etc. Description adds the list of return fields but no additional behavioral traits. With annotations present, this is adequate.
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 wasted words, front-loaded with the primary action and return fields.
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 provides purpose, return fields, and usage guidance, complemented by thorough annotations.
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 covers the one parameter (include_inactive) with 100% description coverage. Description does not add further meaning beyond schema, so baseline score applies.
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 and enumerates the returned fields. It distinguishes from siblings like subscribe and unsubscribe.
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?
Explicitly advises using it to review monitoring before adding more or to find an id to cancel, providing clear context. No explicit when-not-to-use but context is strong.
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.
Most tools have distinct purposes, but the ask_pipeworx family (including beta and grounded) and deep_research overlap in functionality, causing some ambiguity. The OONI tools are well-differentiated, and other tools are distinct.
Naming predominantly follows a verb_noun pattern with underscores, but some tools like generate_llms_txt and pipeworx_feedback deviate slightly. Overall consistent with minor inconsistencies.
35 tools is on the higher side but justified by the broad domain coverage (data lookup, censorship monitoring, prediction markets, memory, etc.). Each tool serves a specific purpose, making the count reasonable.
The tool surface covers core workflows comprehensively, including data retrieval, censorship analysis, prediction market evaluation, and memory management. Minor gaps exist (e.g., no subscription modification tool), but overall it's well-scoped.