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, and destructiveHint. The description adds value by specifying returned fields and default filtering to active subscriptions, enhancing transparency 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 with no wasted words. The purpose and return fields are front-loaded, followed by usage guidance. Extremely concise and well-structured.
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 adequately. Missing details like pagination or rate limits, but acceptable given the tool's simplicity.
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 description coverage is 100% for the one parameter. The description reiterates the parameter's effect ('include_inactive' for cancelled subscriptions) but does not add significant new meaning beyond the schema.
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 'List the caller's active subscriptions', specifying the verb 'list' and resource 'subscriptions' with scope. It distinguishes from sibling tools 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?
The description advises using this tool 'to review what you're monitoring before adding more or to find an id to cancel', providing clear context for when to use it. No explicit when-not or alternatives, but sufficient guidance.
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 cleanly separated, but real overlap exists: ask_pipeworx_beta is currently an identical twin of ask_pipeworx, ask_pipeworx_grounded shares the same router, ai_visibility_check is subsumed by scan_competitor_ai_presence, and several polymarket tools scan adjacent opportunity spaces. The long descriptions help an agent choose, but the boundaries between several query, research, and edge-scanning tools are not always crisp.
Names are uniformly lowercase and mostly snake_case, but they mix verb_noun forms like search_datasets and validate_claim with noun-first/brand-prefixed forms like entity_profile, pipeworx_trending, polymarket_arbitrage, and recent_alerts, plus bare verbs like remember and forget. There is no single predictable pattern, though the names remain readable.
35 tools is well past the 25+ threshold for a coherent MCP surface, and the count is padded by near-duplicate query gateways (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research), multiple overlapping polymarket scanners, and memory/subscription utilities that could easily live in separate servers. The broad scope justifies some multiplicity, but the set feels bloated rather than curated.
For a read/research data platform, the surface is largely complete: entity resolution, profiles, comparisons, change feeds, grounded lookup, deep research, claim validation, dataset discovery, memory, and subscription lifecycles are all covered. Minor gaps exist—no direct dataset-resource download, no prediction-market execution, and no data write/update operations—but these are workaround-able or outside the server's apparent purpose.