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, covering safety. The description adds the list of returned fields, but does not disclose additional behavioral traits like pagination or default limits.
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 very concise: two sentences that front-load the action and resource, followed by return fields and usage guidance. No unnecessary information.
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 listing tool with one optional parameter and good annotations, the description is nearly complete. It could mention pagination or ordering, but those are minor omissions given the tool's simplicity.
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% (only one parameter with a description). The tool description does not add extra meaning beyond the schema's parameter description, so it meets the baseline for high coverage.
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 specifies the action ('List'), the resource ('the caller's active subscriptions'), and lists the returned fields, which distinguishes it from sibling tools like subscribe/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 provides explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This clarifies when to use the tool, though it does not explicitly state when not to use it.
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 currently identical, deep_research overlaps ask_pipeworx for multi-faceted questions, and the five Polymarket tools plus bet_research all target the same market-analysis space. Several tools appear to do the same thing, and even detailed descriptions can't fully separate them.
All names are lowercase snake_case and mostly readable, but the set mixes bare nouns (datasets, query, recall) with verb phrases (generate_llms_txt, validate_claim) and domain-prefixed compounds (polymarket_edges, pipeworx_trending). The ask_pipeworx family is internally consistent, but the overall pattern is not uniform.
34 tools is well above the 25-tool threshold for a coherent set, especially since many are meta-tools (suggest_questions, discover_tools, pipeworx_feedback, pipeworx_trending) or one-off utilities (generate_llms_txt, scan_dependency). The server tries to cover data lookup, prediction markets, memory, subscriptions, and AI visibility in a single surface.
Within each subdomain (data lookup, memory, subscriptions, Polymarket) the main workflows are covered, and the memory/subscription clusters have full CRUD. But the server name promises Norfolk Open Data, which is barely represented by three read-only tools, and odd one-offs like generate_llms_txt and scan_dependency have no supporting ecosystem.