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?
With annotations declaring readOnlyHint, idempotentHint, and openWorldHint, the description adds value by specifying it returns only the caller's subscriptions and that include_inactive defaults to false. It also lists the exact fields returned, which compensates for the lack of an output schema. 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 only two sentences: the first states the action and output fields, the second gives usage guidance. All content is relevant and there is no fluff, earning a top score.
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 read-only list tool with one optional parameter and rich annotations, the description is complete. It covers purpose, returned fields, and usage context; the lack of an output schema is mitigated by listing fields inline. Some might want pagination info, but the low complexity makes this acceptable.
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 schema fully documents include_inactive with its default value (100% coverage), so the baseline is 3. The description does not elaborate on the parameter, but it does hint that cancelled subscriptions might be relevant when finding an id to cancel. Since the schema is explicit, no additional semantic burden is placed on the description.
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 enumerates the returned fields (id, type, params, created_at, etc.). This distinguishes it from mutation siblings like subscribe/unsubscribe and other monitoring tools.
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 says 'Use this to review what you're monitoring before adding more or to find an id to cancel,' giving clear when-to-use context. It doesn't explicitly name alternatives, but it aligns with subscribe/unsubscribe siblings, making the usage intention 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.
Several tools have heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ask_pipeworx_grounded and deep_research blur the query/research boundary, and the polymarket_* family plus bet_research all target prediction-market analysis. An agent would frequently struggle to pick the correct tool among these near-duplicates.
All names are snake_case, but the verb/noun style is inconsistent: get_* for flight lookups, ask_* for queries, noun-style names like entity_profile and bet_research, and the polymarket_* prefix group. Some subgroups are internally consistent, but there is no single predictable pattern across the set.
36 tools is far too many for a server named 'flights' — only 5 tools actually relate to aviation, while the rest span prediction markets, SEC/FDA data, npm packages, memory, and feedback. Even viewed as a general data platform, the count is heavy and the scope is unfocused.
For a flights server, the surface is severely incomplete: no scheduled flight status, delays, cancellations, or airport schedules — only live ADS-B snapshots. For the broader data-research domain the tools imply, coverage is better but still scattered, with no coherent lifecycle and several one-off utilities that don't connect to the rest.