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, destructiveHint=false, idempotentHint=true. The description adds value by listing the exact return fields: id, type, params, created_at, last_fired_at, fire_count. No contradictions.
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 sentences with no waste: first sentence states purpose and return fields, second sentence gives usage guidance. Front-loaded and efficient.
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?
With only one optional parameter and no output schema, the description covers the tool's purpose, return format, and usage context adequately. No missing elements.
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 one optional parameter (include_inactive) fully described in schema. The description does not add parameter details, meeting 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 states it lists the caller's active subscriptions, with specific verb 'list' and resource 'subscriptions'. It distinguishes from siblings by mentioning using it for review before adding or to find an id for cancellation.
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: 'review what you're monitoring before adding more or to find an id to cancel.' While it doesn't mention when not to use, the context is clear and provides practical guidance.
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 have overlapping purposes, such as multiple 'ask_pipeworx' variants, 'deep_research', and various search tools (search_hash, search_ioc, search_malware, search_within). The similarities in descriptions confuse an agent's ability to select the correct tool.
Tool names mix conventions: some use underscores (ask_pipeworx, deep_research), some use hyphenated or compound names (generate_llms_txt, pipeworx_feedback), and verbs are inconsistent (search vs. ask vs. validate). No clear pattern.
With 35 tools, the server feels over-scoped, integrating many domains (financials, drugs, prediction markets, threat intel) into a single surface. This leads to redundancy and makes it hard for agents to navigate.
The tool set covers a wide range of data lookups and threat intel operations, but there are noticeable gaps: few update/delete/management tools (only subscribe/unsubscribe/forget) and no clear lifecycle for many resource types. Some domains appear incomplete.