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, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by listing the return fields (id, type, params, etc.) and specifying that it lists 'active subscriptions,' implying the default filter and the include_inactive parameter.
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, front-loaded with the action and output fields, then a concise usage hint. Every sentence earns its place with no redundancy.
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 operation with one optional parameter, the description fully covers purpose, output fields, and typical use cases. It also fits well among sibling tools like subscribe and unsubscribe, providing enough context for an agent to invoke it 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 description coverage is 100%: the only parameter, include_inactive, is fully documented in the schema. The description reinforces the default behavior ('active subscriptions') but adds no new meaning beyond the schema.
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') and resource ('subscriptions') with the scope 'the caller's active subscriptions.' It clearly distinguishes from sibling tools like subscribe, unsubscribe, and recent_alerts by indicating it lists existing subscriptions for review.
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 use cases: 'review what you're monitoring before adding more or to find an id to cancel.' It does not name alternative tools or exclusions, but the usage context is clear enough to guide tool selection.
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 serve overlapping purposes, especially ask_pipeworx and ask_pipeworx_beta (explicitly identical) and ask_pipeworx_grounded/deep_research/validate_claim for fact retrieval. Even with detailed descriptions, an agent could easily misselect among data-query tools or among the five Polymarket analysis tools.
Tool names mix verb-first (ask_pipeworx, compare_entities), noun-first (polymarket_edges, entity_profile), and single-word verbs (geocode, forget), with no strict verb_noun pattern. However, all names are snake_case and mostly descriptive, so the inconsistency is moderate.
36 tools is far beyond the typical well-scoped range of 3-15, and the feature set spans research, memory, subscriptions, prediction markets, and geo utilities. Many tools are meta-tools (discover_tools, suggest_questions) that could be consolidated, making the surface feel bloated.
For the apparently broad domain of data research and prediction markets, the toolset covers most needs with parallel research, grounding, claim verification, memory, and subscription lifecycle. Minor gaps exist—like a direct way to fetch arbitrary raw data or a unified list of all tools—but agents can generally work around them.