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, so the read-only nature is established. The description adds behavioral context by specifying the exact return fields (id, type, params, created_at, last_fired_at, fire_count) and scoping to the caller's subscriptions. This goes beyond the annotations by describing the response structure, which is especially valuable given there is no output schema.
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: the first states the function and return fields, the second gives a concrete use case. There is no redundant information, and the most important information is front-loaded.
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 one optional parameter, no output schema, and clear annotations, the description adequately covers the tool's behavior and return values. It explicitly lists the returned fields, explains the caller-scoped nature, and provides usage context, making it complete for an AI agent to invoke 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?
Schema coverage is 100%: the only parameter include_inactive is fully described in the schema. The description does not mention this parameter, but the schema carries the meaning, so the baseline of 3 applies. No additional parameter semantics are needed.
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') with a clear resource ('the caller's active subscriptions') and explicitly distinguishes its purpose from adding or cancelling subscriptions by stating 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This clearly separates it 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 explicitly states when to use the tool: 'before adding more or to find an id to cancel.' This gives direct contextual guidance and implies that subscribe/unsubscribe are the alternatives for those actions. No exclusions are needed because the usage context is unambiguous.
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_beta is explicitly identical to ask_pipeworx right now, creating a true duplicate. The six-tool Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) plus discover_tools vs suggest_questions give agents overlapping entry points that require deep reading to disambiguate.
Sub-families are internally consistent (ask_pipeworx_*, polymarket_*, remember/recall/forget), but the server mixes verb_noun, domain_noun, and bare-verb styles across tools. bet_research breaks the polymarket_ prefix pattern, and ai_visibility_check vs scan_competitor_ai_presence use different words for the same underlying concept.
33 tools is heavy and spans at least six unrelated domains: a data-gateway, prediction markets, key-value memory, subscription management, PRIDE proteomics, and standalone utilities (generate_llms_txt, scan_dependency). The scope is so broad that it feels like multiple servers merged into one, making the surface hard to navigate.
The dominant data-query domain is well covered: query, grounded query, deep research, profiles, comparison, change feeds, validation, entity resolution, and discovery are all present. Subscription and memory lifecycles are complete, and the prediction-market research surface is thorough; minor gaps exist only in peripheral areas like PRIDE project download/file details.