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 readOnly, idempotent, non-destructive. The description adds details about returned fields, but lacks info on pagination limits. Score high as annotations carry the burden.
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?
Three sentences, no redundancy. Every sentence adds value: purpose, return fields, and usage guidance.
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 single optional parameter, rich annotations, and no output schema, the description fully covers what an agent needs: purpose, return fields, and usage context.
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% with description for the only parameter. The tool description adds no further parameter detail, meeting baseline but not exceeding.
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 the action 'List the caller's active subscriptions' and lists specific return fields (id, type, params, etc.). This differentiates it from sibling tools like subscribe/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 advises when to use: 'review what you're monitoring before adding more or to find an id to cancel.' This helps the agent decide between this 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 clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve nearly the same routing purpose, with the beta variant currently identical to the stable version. Similarly, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all target prediction-market analysis with fuzzy boundaries, and ai_visibility_check is effectively wrapped by scan_competitor_ai_presence. The detailed descriptions help, but an agent would frequently need to read large descriptions to pick correctly.
All tool names use snake_case and many are readable verb_noun constructions (list_feeds, read_feed, fetch_feed, resolve_entity, validate_claim). However, conventions are mixed: some are bare verbs (remember, recall, forget), some are adjective_noun (recent_alerts, recent_changes), and some use domain prefixes or generic labels (ask_pipeworx, polymarket_edges, entity_profile). The naming is not chaotic, but it does not follow a single predictable pattern.
With 34 tools, the server is overstuffed for its stated 'Design Feeds' name, which only accounts for list_feeds, read_feed, and fetch_feed. The vast majority of tools belong to unrelated domains like investment research, prediction markets, entity resolution, and memory management, making the overall surface feel like a general-purpose toolkit rather than a focused design feed server.
For the design-feed domain, only list/read/fetch operations exist; there are no create, update, or delete feed-management tools, and the subscribe tool does not support design feeds. For the broader Pipeworx/research surface, coverage is quite strong with memory, subscriptions, discovery, grounded lookup, research, comparison, and claim verification. The mix of two very different domains leaves notable gaps in each.