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). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and non-destructive nature. Description adds value by specifying that it returns active subscriptions by default and mentions the optional parameter for inactive ones. No contradiction.
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 concise sentences, front-loaded with the purpose and return fields, no 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?
With no output schema, the description lists key return fields. It lacks pagination details but is sufficient for a simple list tool. Given the context signals, it's nearly complete.
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 is described in the schema with 100% coverage. The description adds context by clarifying 'include_inactive' and its effect (include cancelled subscriptions).
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 uses a specific verb ('List') and resource ('subscriptions'), and lists return fields (id, type, etc.). It effectively distinguishes from siblings 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?
Explicitly states when to use: 'review what you're monitoring before adding more or to find an id to cancel.' Provides clear usage context and alternatives.
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.
Several tool families overlap heavily: ask_pipeworx and ask_pipeworx_beta are functionally identical today, and the polymarket_edges/arbitrage/fill_risk/kalshi_spread family plus entity_profile/recent_changes/compare_entities cover adjacent jobs. The descriptions are detailed enough to separate them with careful reading, but an agent could easily select the wrong one without deep inspection.
The set has recognizable prefixes like ecos_, ask_pipeworx, and polymarket_, but it also mixes verb_noun names (validate_claim, discover_tools), bare verbs (remember, forget, recall), reversed/gerund forms (bet_research, pipeworx_trending), and special tokens (generate_llms_txt). The naming is readable on a per-family basis but not predictable across the full surface.
35 tools is above the comfortable range for a coherent tool set, and several entries are near-duplicates or wrappers: ask_pipeworx_beta is currently identical to ask_pipeworx, and scan_competitor_ai_presence wraps ai_visibility_check. The prediction-market and company-research families could be consolidated without losing capability.
For the server's broad scope, lifecycle coverage is strong: ECOS has search/items/get/indicators, subscriptions have create/list/read/cancel, memory has save/read/delete, and the data-research surface covers lookup, grounded verification, comparison, profiles, changes, and discovery. Minor gaps exist, such as no direct tool to fetch a pipeworx:// record by URI or execute a single catalog tool directly, but these are workable.