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 readOnly, idempotent, and non-destructive behavior. The description adds value by specifying the scope (caller's subscriptions), default filtering (active only), and return fields (id, type, params, etc.), which goes beyond the 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 three sentences long, front-loaded with the core purpose, and every sentence contributes value. There is no redundancy or fluff.
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?
Given the tool's simplicity (one optional parameter, no output schema), the description covers all essential aspects: what it lists, returned fields, default behavior, and usage context. It effectively differentiates from siblings and provides sufficient information for an agent to use 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?
Schema coverage is 100% for the single parameter 'include_inactive', which already includes a clear description. The tool description does not add further meaning beyond what the schema provides, so it meets the baseline of 3.
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 a specific verb+resource: 'List the caller's active subscriptions.' It clearly distinguishes from sibling tools like subscribe and unsubscribe by focusing on reading existing subscriptions.
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 explicitly advises using it 'to review what you're monitoring before adding more or to find an id to cancel,' providing clear when-to-use context. However, it does not explicitly state when not to use it or name alternatives, so it falls short of a 5.
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.
ask_pipeworx_beta is explicitly identical to ask_pipeworx today, and ask_pipeworx_grounded is another variant of the same router, so an agent can easily misselect. Additionally, bet_research, polymarket_edges, and polymarket_arbitrage all present as prediction-market opportunity finders with overlapping responsibilities.
Most tools follow snake_case verb_noun (ask_pipeworx, list_subscriptions, resolve_entity, unsubscribe, validate_claim), but several break the pattern with noun phrases like polymarket_edges and pipeworx_trending, plus oddities like startup_oracle_evaluate. The mix is readable but not predictable.
32 tools is well over the 25 threshold and the set is not tightly scoped: prediction markets alone account for six overlapping tools, and the server also bundles memory, subscriptions, npm dependency scanning, llms.txt generation, and AI visibility checks. ask_pipeworx_beta adds a duplicate that does not earn its slot.
As a read-heavy research gateway, the surface is broad: lookups, profiles, comparisons, verification, deep research, discovery, and citation handling are covered, and the subscription/memory helpers have their own lifecycle operations. But the 'Startup Oracle' mission is thin — startup-specific evaluation is a single joke tool, and there is no way to update subscriptions or act on research findings beyond saving memory.