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 indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations by specifying it returns the caller's own subscriptions (only active by default), and listing the exact return fields, which is useful for downstream actions like cancellation. No contradiction with annotations.
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 with no filler. The first sentence states the core purpose and return fields; the second provides concrete usage guidance. Every word adds value, and the structure is front-loaded with the most important information.
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's simplicity (1 optional parameter, no output schema), the description is complete. It covers the tool's scope (caller's active subscriptions), return fields (compensating for lack of output schema), and typical use cases. There are no obvious gaps for an agent to invoke or interpret the results 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: the only parameter, include_inactive, is fully described as including cancelled subscriptions and defaulting to false. The description's mention of 'active subscriptions' implicitly aligns with this parameter, but it does not explicitly explain the parameter. With schema coverage this high, a baseline of 3 is appropriate.
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 verb 'List' with the specific resource 'the caller's active subscriptions', and enumerates the exact return fields (id, type, params, created_at, last_fired_at, fire_count). This distinguishes it from sibling mutation tools like subscribe and unsubscribe, and from other list tools by focusing on subscriptions.
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 provides explicit usage context: use it to review active monitoring before adding more subscriptions and to find an ID to cancel. While it does not explicitly name alternatives, the context clearly implies this is the read-only counterpart to subscribe/unsubscribe, making the usage intent clear.
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 overlapping or near-identical purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions with varying degrees of grounding. The polymarket_* family (edges, arbitrage, fill_risk, edge_tracker) and bet_research also create boundary confusion. An agent would struggle to consistently select the correct tool without deep reading of each description.
Most tools follow a verb_noun snake_case pattern (search_markets, get_market, compare_entities, resolve_entity), but there are clear outliers like pipeworx_trending, recent_alerts, top_markets, and pipeworx_feedback which use adjective_noun or noun_adjective forms. The pattern is mostly consistent but has enough deviations to feel mixed.
With 34 tools, the surface is decidedly heavy. Many tools serve entirely different domains (memory, subscriptions, AI visibility, npm dependency scanning, cross-venue arbitrage) rather than a unified purpose. This feels like several server concepts merged into one, making the count inappropriate for a single coherent server.
The data-research side is fairly comprehensive: querying, grounding, comparisons, profiles, claim validation, entity resolution, change tracking, and memory are all covered. However, for the nominal Futuur prediction-market domain, only read-only market lookup exists — no trading, account management, or order placement. Subscription CRUD is also missing an update operation, and several research tools only cover US public companies and specific data sources.