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=false, so the safety profile is covered. The description adds useful behavioral context by specifying the return fields (id, type, params, created_at, last_fired_at, fire_count) and the default scope of 'active' subscriptions. No contradiction found.
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, front-loaded with the purpose and return fields, followed by usage guidance. Every sentence earns its place with no redundancy or 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?
Given the tool has only one optional parameter, rich annotations, and no output schema, the description compensates by listing the return fields and providing usage context. It is fully adequate for an agent to select and invoke the tool 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?
The input schema has 100% coverage for the single parameter include_inactive, with a clear description ('Include cancelled subscriptions in the response (default false)'). The tool description does not add further parameter semantics, so the baseline of 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 the tool lists the caller's active subscriptions, with a specific verb (list), resource (subscriptions), and scope (caller's active). It also enumerates the returned fields, and the context of sibling tools (subscribe, unsubscribe) makes its purpose distinct.
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 tells when to use the tool: 'review what you're monitoring before adding more or to find an id to cancel.' This provides clear use cases and implicitly contrasts with the sibling subscribe and unsubscribe tools.
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.
Multiple tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same 5,529 tools, and deep_research overlaps for broad questions. The six prediction-market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also have heavily overlapping purposes, making misselection likely.
All names use snake_case, which is consistent, and most are verb-first (search, extract, remember, resolve_entity, validate_claim). However, several are noun-phrases (entity_profile, polymarket_arbitrage, recent_alerts, pipeworx_feedback), breaking the verb_noun pattern. The deviations are minor but present.
At 33 tools, the set is well above the 25-tool threshold for 'too many'. The server also spans several unrelated domains—web search, Pipeworx structured data, prediction markets, memory, subscriptions, AI visibility—making the count feel excessive for a coherent purpose.
Each sub-domain is well covered: search has search/extract/search_within, prediction markets have research/arbitrage/edges/fill-risk/tracking, subscriptions have subscribe/unsubscribe/list/recent_alerts, and memory has remember/recall/forget. Minor gaps exist (e.g., no way to edit a subscription's parameters, no direct SEC filing content viewer), but agents can work around them via ask_pipeworx.