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. The description adds valuable context: returns specific fields, lists only active subscriptions by default, and is scoped to the caller. This exceeds what annotations provide without contradicting them.
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 concise sentences, front-loaded with the purpose, followed by return fields and usage guidance. Every sentence earns its place with zero filler.
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?
The tool is simple (one optional param, no output schema). The description covers purpose, use case, returned fields, and active/inactive behavior, making it fully complete for an agent to decide when and how to invoke it.
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 has 100% coverage with one self-explanatory parameter include_inactive. The description does not explicitly elaborate on the parameter but implies its meaning by contrasting active vs cancelled subscriptions. Baseline 3 is appropriate when schema handles parameter documentation.
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's function with a specific verb and resource: 'List the caller's active subscriptions.' It further distinguishes from sibling tools like subscribe and unsubscribe by mentioning 'before adding more' and 'to find an id to cancel.'
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?
Explicit usage guidance is provided: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent exactly when to use this tool relative to subscribe/unsubscribe.
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.
Most tools have clearly distinct purposes, but there are overlapping clusters: the three ask_pipeworx variants and multiple polymarket analysis tools can cause selection ambiguity. Descriptions help, yet boundaries between entity_profile, compare_entities, recent_changes, and ask_pipeworx require careful reading.
Tool names almost all follow snake_case with verb-noun or verb-phrase structure (ask_pipeworx, validate_isin, list_subscriptions), and family prefixes like ask_pipeworx_* and polymarket_* are consistent. Minor deviations include one-word verbs (remember, recall, forget) and adjective-noun names (recent_alerts, recent_changes, entity_profile).
With 34 tools, the surface is well above the 25-tool threshold that typically feels heavy, even though the Pipeworx platform is broad in scope. The server named 'Isin' exposes a large toolkit far beyond its apparent identifier-focused purpose, making the count feel excessive.
The broader data-access, research, subscription, and utility workflows are well covered, including discovery, grounded queries, entity profiles, comparisons, and claim validation. Minor gaps include the lack of direct pipeworx:// URI reading and ISIN issuer resolution, but agents can work around these.