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 mark this as read-only, idempotent, and non-destructive. The description adds behavioral context by clarifying that it lists 'active' subscriptions by default (matching the include_inactive parameter default) and enumerates the exact return fields, which goes beyond the annotation baseline. It does not contradict any annotation.
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, front-loaded with the primary action, then return fields, then usage guidance. Every sentence adds value, and there is no redundant or filler content.
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 strong annotations/schema coverage, the description fully covers purpose, output, and usage scenarios. No output schema is needed, and the description is sufficient 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 schema fully describes the sole parameter 'include_inactive' with its default behavior, so schema coverage is 100%. The description's mention of 'active subscriptions' implicitly reinforces the default but does not add additional semantic detail beyond the schema, warranting the baseline score 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 begins with 'List the caller's active subscriptions,' which uses a specific verb and resource, clearly stating what the tool does. It also distinguishes itself from siblings like subscribe/unsubscribe by focusing on read-only enumeration of existing subscriptions and lists the exact fields returned.
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 provides explicit usage guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This directly tells when to use the tool relative to subscription management actions, referencing adding (subscribe) and canceling (unsubscribe) without naming them explicitly.
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, ask_pipeworx_beta, and ask_pipeworx_grounded form a confusing cluster—beta currently behaves identically to the stable router. The six polymarket_* tools also share overlapping boundaries (edges vs. arbitrage vs. research vs. fill risk), requiring deep reading to select correctly.
There are some coherent families (ask_pipeworx_*, polymarket_*, subscribe/unsubscribe/list_subscriptions, remember/recall/forget), but the overall style is mixed: verb-first names like compare_entities sit next to noun-first names like entity_profile and recent_changes. Everything is snake_case, so it is readable, just not driven by a single consistent convention.
31 tools is too many for a single MCP surface, and each carries a very dense description. The server is essentially several different products bundled together: data access, deep research, prediction markets, AI visibility, memory, subscriptions, and utilities.
The data/research workflows are deeply covered: simple queries, grounded answers, deep research, entity resolution, comparisons, company profiles, recent changes, claim validation, and discovery. Minor gaps exist—no actual order placement for prediction-market trades, no subscription editing, and limited full-catalog browsing for the 5,596 underlying tools.