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, and non-destructive behavior. The description adds useful context beyond annotations by specifying the return fields (id, type, params, created_at, last_fired_at, fire_count) and the scope (caller's active subscriptions). This enriches the behavioral understanding without contradicting the 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, front-loaded with the purpose, followed by return fields and usage guidance. Every sentence earns its place with no redundancy or 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?
For a simple list tool with one optional parameter and no output schema, the description is complete. It states what it returns and when to use it, and the annotations cover safety aspects. No gaps remain.
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 provides 100% coverage for the single parameter include_inactive, with a clear description. The tool description does not add further parameter-specific semantics, so the 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 the tool lists the caller's active subscriptions, with a specific verb and resource. It distinguishes itself from related tools like subscribe/unsubscribe by mentioning reviewing existing subscriptions and finding 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?
Explicitly states when to use the tool: to review what you're monitoring before adding more or to find an id to cancel. This provides clear context and implies alternatives (subscribe/unsubscribe) without naming them directly.
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.
Tools span multiple unrelated domains (NZ open data, prediction markets, npm scanning, AI visibility, etc.). Within each domain, some tools are similar (e.g., multiple polymarket tools, multiple ask_pipeworx variants). The wide scope makes it hard for an agent to know which tool to use.
Naming conventions are inconsistent: some use snake_case (ask_pipeworx, deep_research), some use underscore verbs (group_list, package_search), some are descriptive phrases (generate_llms_txt, scan_competitor_ai_presence). No clear pattern.
41 tools is high for a server named 'Data Govt Nz' but includes many unrelated tools (Polymarket, npm, AI visibility). The scope is sprawling; many tools seem extraneous to the core purpose.
The NZ open data portion is fairly complete (CRUD for groups, organizations, packages, tags), but other areas have only one or two tools (e.g., npm scanning, AI visibility). The overall surface is incomplete for a coherent domain.