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 indicate read-only, idempotent, non-destructive behavior. Description adds that it returns specific fields (id, type, params, etc.) and focuses on active subscriptions, providing useful behavioral context beyond 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 that are front-loaded with the core purpose and efficient in conveying all necessary information without waste.
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?
Despite no output schema, the description lists return fields and behaviors. Combined with rich annotations and low complexity (one optional param), it is sufficiently complete for an AI agent to use 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?
Schema coverage is 100% with a clear description for the only parameter 'include_inactive'. Description does not add extra parameter semantics beyond the schema, so baseline 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 verb 'List', the resource 'subscriptions', and the scope 'caller's active subscriptions', making it distinguishable from sibling tools like subscribe/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?
Provides explicit use cases: 'review what you're monitoring before adding more' and 'find an id to cancel'. Does not explicitly exclude other uses or mention alternatives, but context is 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.
The ask_pipeworx family (stable, beta, grounded) are nearly identical, with beta explicitly matching stable, creating clear misselection risk. The five polymarket_* tools and several research tools (deep_research, bet_research, entity_profile) also overlap in purpose despite detailed descriptions.
Tool names are mostly snake_case and readable, with consistent prefixes (ask_pipeworx_, polymarket_, easypost_), but mix verb-first (validate_claim, resolve_entity) and noun-first (entity_profile, ai_visibility_check) conventions. The server name 'Easypost' does not align with the overwhelmingly Pipeworx-focused tool set.
33 tools is a heavy count, especially with three near-duplicate ask_pipeworx variants and many meta-tools. The set is also unfocused: only two shipping tools under an 'Easypost' label while the rest are a broad data-research and prediction-market platform, making the count feel bloated for the apparent scope.
As an Easypost server, shipping coverage is severely incomplete (rates and tracking only, no label purchase, address verification, or refunds). Within the Pipeworx tools, the cited pipeworx:// URIs have no direct fetch-by-URI tool, leaving a notable dead end for agents trying to retrieve full records.