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 and destructiveHint=false. The description adds that it returns specific fields (id, type, params, etc.) and defaults to active subscriptions, giving useful behavioral context beyond the annotations. No contradictions.
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, front-loaded with the core action, followed by return fields and use case. No wasted words.
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, the description covers purpose, return fields, and usage context. It lacks pagination/limits info but is adequate 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 include_inactive. The description's mention of 'active subscriptions' implicitly relates to the parameter but doesn't add new semantic detail beyond the schema, so baseline 3 applies.
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' with a specific verb and resource, and differentiates from sibling subscribe/unsubscribe by noting it's for reviewing before adding or cancelling.
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 explicit usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This indicates when to use it but doesn't explicitly name alternative tools or when-not scenarios, so it's clear context without exclusions.
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 organized into clearly differentiated families (ask_pipeworx vs ask_pipeworx_grounded, polymarket_edges vs polymarket_arbitrage), but there are some genuinely ambiguous pairs: ask_pipeworx_beta is currently identical to ask_pipeworx, and search_recalls/recent_recalls, ai_visibility_check/scan_competitor_ai_presence, and bet_research/polymarket_edges all require careful reading to avoid misselection.
The set is consistently lowercase snake_case and contains strong families like ask_pipeworx*, polymarket_*, recent_*, and search_*. However, the naming pattern is mixed: imperative verbs (recall, forget, subscribe), noun phrases (entity_profile, recent_changes, pipeworx_trending), and action prefixes (scan_, generate_, validate_) all coexist, making the overall convention less predictable than a uniform verb_noun scheme.
With 33 tools, the server exceeds the healthy range and spreads across many side domains: data research, prediction markets, memory, subscriptions, npm dependency checks, llms.txt generation, and AI visibility audits. No individual tool feels pointless, but the overall surface is sprawling rather than tightly curated for a single purpose.
The core research workflow is well covered: querying, grounded verification, entity resolution, profiles, comparisons, recent changes, claim validation, deep research, memory, and subscriptions. Minor gaps exist—there is no direct reader for pipeworx:// citation URIs, no tool to update or edit a stored memory, and subscriptions can be created/cancelled but not modified—but agents can work around these.