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). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. Description adds useful behavioral details like the default filtering to active subscriptions and the effect of the include_inactive parameter, plus the exact returned fields. No contradictions with 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?
Two concise sentences: first states purpose and output, second provides usage context. No redundant words, well-front-loaded, earns its place.
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?
Given the simple schema (one optional boolean, no required params), strong annotations covering safety, and no output schema needed, the description provides complete context for an AI agent to correctly invoke the tool and interpret results.
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 covers 100% of parameters with a clear description for include_inactive. The description does not add further semantic value beyond restating the parameter's effect indirectly ('List the caller's active subscriptions' implies default behavior). Baseline for high coverage is appropriate here.
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?
Description states clearly that the tool lists the caller's active subscriptions and specifies the returned fields (id, type, params, created_at, last_fired_at, fire_count). It distinguishes itself from sibling tools like subscribe and unsubscribe by explicitly noting use cases for reviewing or finding subscription ids.
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 advises using this tool 'to review what you're monitoring before adding more or to find an id to cancel,' providing clear context for when to use it. It could mention alternatives (e.g., subscribe/unsubscribe) but the guidance is sufficient for an AI agent.
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: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer queries, and the multiple polymarket_* tools scan for edges and arbitrage. The Drive tools are distinct, but the surrounding 31 unrelated tools create significant ambiguity about which tool to call for a given task.
Tool names are all snake_case, but the pattern is inconsistent: some start with verbs (drive_create_file, ask_pipeworx, validate_claim), some are noun phrases (entity_profile, recent_changes, polymarket_edges), and some are bare verbs (remember, forget, recall). There is no uniform verb_noun convention, and suffixes like _beta and _grounded add further irregularity.
36 tools is far too many for a server named Google_drive, especially since only 5 tools (drive_create_file, drive_get_content, drive_get_file, drive_list_files, drive_search) actually relate to Drive. The other 31 tools cover unrelated domains like Pipeworx data queries, Polymarket betting, and memory management, making the tool count wildly disproportionate to the server's apparent purpose.
For a Google Drive server, the tool set is incomplete: it covers create, get content, get metadata, list, and search, but lacks essential operations like updating, deleting, uploading, moving, copying files, creating folders, or managing permissions/sharing. These gaps would force agents to work around missing core Drive functionality.