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. The description adds the list of returned fields but does not disclose additional behavioral traits like pagination or rate limits. This is acceptable given annotations cover safety.
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: the first states purpose and return fields, the second provides usage guidance. Every sentence adds value 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?
The description lists all relevant return fields and provides usage context. Given the low complexity (no output schema, one optional parameter), it is sufficiently complete for an agent to select and invoke the tool 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%, so the baseline is 3. The description adds no further meaning to the 'include_inactive' parameter beyond what is already in 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 clearly states 'List the caller's active subscriptions', identifying the specific verb and resource. It also enumerates the fields returned, distinguishing 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?
The description 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.' While it doesn't list negative use cases, the guidance is direct and helpful.
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 detailed usage guidance, but there are several overlapping entry points: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, and deep_research; discover_tools vs suggest_questions; entity_profile vs recent_changes; and ai_visibility_check vs scan_competitor_ai_presence. The descriptions help, but the boundaries are not always crisp enough to prevent misselection.
Names are mostly lower_snake_case, but conventions vary widely: some are verb-first (list_subscriptions, resolve_entity), some are noun/adjective phrases (recent_changes, entity_profile), and several use brand prefixes (pipeworx_trending, polymarket_arbitrage). The naming is readable but lacks a single predictable pattern.
34 tools is well past the 25+ threshold and the scope sprawls beyond news/data into prediction-market arbitrage, npm dependency scanning, AI visibility audits, memory, subscriptions, and llms.txt generation. Each tool may be useful, but the set feels like several different servers merged into one, making it oversized and harder to navigate.
The research workflow is well covered: universal routing, grounded verification, deep research, entity resolution/profiles/comparisons, change feeds, discovery/onboarding, subscriptions, and memory all exist. Minor gaps include no direct pipeworx:// citation fetch tool and no broader account/profile management, but agents can work around these.