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, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that it returns the caller's subscriptions and specific fields, but does not mention pagination, rate limits, or other behavioral details beyond what annotations imply.
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 three sentences: first states the action and scope, second lists return fields, third gives usage guidance. There is no redundancy or filler; each sentence 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 no output schema, the description covers purpose, return fields, and usage context. Combined with rich annotations, it provides sufficient information for an agent to select and invoke the tool 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 single parameter include_inactive is fully documented in the schema (100% coverage), with description 'Include cancelled subscriptions in the response (default false).' The tool description adds no additional parameter syntax or meaning 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 opens with 'List the caller's active subscriptions,' a specific verb+resource+scope that clearly states what the tool does. It distinguishes from sibling subscribe/unsubscribe tools by focusing on the read-only listing of existing subscriptions, and the list of return fields reinforces its purpose.
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?
It provides clear usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' However, it does not explicitly name alternatives or state when-not-to-use, so it lacks the explicit exclusion found in top-tier descriptions.
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 are distinctly named and the descriptions are unusually specific, but the set contains overlapping families: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying data, and the beta variant is currently an exact duplicate. The entity/company lookup, AI-visibility, and Polymarket clusters also require reading the long descriptions to choose correctly.
All names are lowercase snake_case and readable, with useful prefixes like ask_pipeworx, polymarket_, and pipeworx_. However the macro pattern is mixed: many are verb-first (compare_entities, resolve_entity), many are noun phrases (entity_profile, polymarket_edge_tracker, recent_alerts), and one puts the verb last (linkedin_humblebrag_generate).
32 tools is far too many for a server whose apparent name and stated LinkedIn-humblebrag purpose are served by exactly one tool. Even viewed as a general Pipeworx/research utility, the surface is bloated: duplicate ask variants, multiple meta-tools, and a sprawling prediction-market family push the count well past the 25-tool threshold.
For the domain implied by the server name, the surface is severely incomplete: only generation exists, with no way to list, edit, delete, publish, or manage LinkedIn-humblebrag posts. The de facto Pipeworx research domain is much better covered, but the overall set has serious dead ends because the one LinkedIn tool is isolated and the core tools are oriented toward a different, unrelated workflow.