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 read-only, idempotent, non-destructive. Description adds return field details and context, building on annotations without contradiction.
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 efficient sentences with no extraneous content; first states function, second provides usage guidance.
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 tool with one optional parameter and annotations covering safety, the description fully explains purpose, output, and use case.
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 clear parameter description. Description does not add additional semantics beyond what the schema provides, so baseline score.
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?
Clearly states the tool lists active subscriptions and specifies return fields. Distinguishes 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?
Provides explicit usage guidance: review before subscribing or to get a cancellation ID. Does not explicitly state when not to use, but context is clear.
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.
ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, creating a true duplicate entry point, and the prediction-market cluster (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_kalshi_spread) all detect mispricings with heavily overlapping descriptions. The detailed docs help, but an agent choosing among these will frequently misselect.
The set mixes verb-first names (ask_pipeworx, compare_entities, discover_tools, subscribe) with noun-first names (polymarket_edges, entity_profile, ip_context, recent_changes) and bare verbs (remember, forget) without a unifying convention. Subfamilies are internally consistent (polymarket_*, ask_pipeworx_*, subscribe/unsubscribe), which keeps it readable, but there is no predictable server-wide pattern.
32 tools is well into the too-many band, and several tools duplicate or wrap others: ask_pipeworx_beta is a redundant copy of ask_pipeworx, scan_competitor_ai_presence wraps ai_visibility_check, and bet_research overlaps polymarket_edges/arbitrage. The broad scope justifies a large set, but it would be tighter and clearer around 20-24 tools.
For the domain the descriptions actually define (structured-data research, company intelligence, prediction markets, subscriptions, memory), coverage is strong with few dead ends: subscription and memory lifecycles are complete, and research has routing/grounded/deep modes. However, the server is named Greynoise while only ip_context serves that domain, and side tools like generate_llms_txt and scan_dependency sit outside any core workflow.