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 readOnlyHint, idempotentHint, and non-destructive. The description adds valuable behavioral information such as the returned fields (id, type, params, etc.) and the default of active subscriptions, enhancing agent understanding beyond structured data.
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?
Two sentences, no redundancy, front-loaded with the core action. Every sentence adds value.
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?
Despite lacking an output schema, the description lists the returned fields. Combined with usage guidance and annotations, the tool is fully understandable for an AI agent.
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 a single boolean parameter fully described. The description implicitly references it ('active subscriptions') but adds no new semantics beyond the schema. 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 'List the caller's active subscriptions', specifying the verb (list) and resource (subscriptions). This distinguishes 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?
Explicitly advises when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This provides clear context and exclusions relative to siblings.
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 overlap heavily: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all route natural-language data questions, while entity_profile, recent_changes, compare_entities, and resolve_entity overlap around company data. An agent cannot reliably distinguish which retrieval entry point to choose.
Tool names are almost uniformly lowercase snake_case and mostly follow a recognizable verb_noun or domain-prefixed pattern (ask_pipeworx*, polymarket_*, pipeworx_*, scan_*, recent_*, subscribe/unsubscribe). A few names like deep_research, entity_profile, and bet_research break the verb-first style, but there is no chaotic convention mixing.
32 tools is too many for the apparent scope, and more importantly only one tool (mo_dmv_license_offices) matches the server name 'Missouri License Offices.' The other 31 tools form a general data-research, prediction-market, memory, and subscription platform that has little to do with the stated purpose.
Judged as the broad Pipeworx-style research platform the descriptions reveal, the surface is quite complete: simple and grounded querying, deep multi-source research, claim verification, entity resolution, profiles, comparisons, change feeds, discovery, subscriptions, alerts, and memory. For the literal Missouri license-office purpose, however, only the single lookup tool is present, which drags down completeness despite that tool being reasonably thorough.