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=true, idempotentHint=true, destructiveHint=false. The description adds value by detailing the return fields (id, type, params, created_at, last_fired_at, fire_count) and the default scope (active subscriptions). It does not contradict 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 long, front-loaded with the purpose, and every sentence adds value. No unnecessary words.
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 has low complexity with 1 optional parameter. The description covers purpose, return fields, and usage guidance. It lacks mention of pagination or ordering, but for a standard list tool this is acceptable. Output schema is absent but return fields are described.
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 1 parameter (include_inactive) with a full description. Schema coverage is 100%. The tool description does not add additional meaning beyond the schema, so baseline score of 3 applies.
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.' It uses a specific verb and resource, and distinguishes from sibling tools like subscribe and unsubscribe by being a read-only list operation.
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 advises when to use this tool: 'to review what you're monitoring before adding more or to find an id to cancel.' This provides clear guidance and implies alternatives (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.
Many tools are distinct in purpose, but there is significant overlap among the data-query tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all route to the same underlying 5,756 tools. The descriptions help differentiate them, but an agent could easily select the wrong router for a given question.
The naming is a mix of verb_noun (ask_pipeworx, resolve_entity, validate_claim), noun-only/product names (bet_research, compare_entities, entity_profile, deep_research), and generic terms (remember, recall, forget, subscribe, unsubscribe). Some tools have prefixes like polymarket_* and pipeworx_*, but ask_pipeworx variants deviate from the pattern. No consistent convention across the set.
33 tools is on the heavy side but arguably justified by the broad domain (SEC data, FDA, prediction markets, subscriptions, memory, AI visibility, npm scanning, IMEI validation). However, several tools feel like they belong to separate concerns (IMEI check, npm dependency scanning, llms.txt generation), making the set feel unfocused.
The core Pipeworx query surface is well-covered: routing, grounded answers, deep research, entity profiles, comparisons, claim verification, and tool discovery. Subscription CRUD is complete (subscribe, recent_alerts, unsubscribe, list_subscriptions), and memory tools are complete (remember, recall, forget). Minor gaps: no direct tool for editing a stored memory, and IMEI tools are minimal (validate + check digit only).