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, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds return field details but no additional behavioral traits beyond what annotations provide.
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 wasted words. Front-loaded with the main action and return details. Highly 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?
For a simple list tool with clear annotations and no output schema, the description is complete: it states what it does, what it returns, and gives usage context. 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?
Schema covers 100% of parameters with a clear description for 'include_inactive'. The tool description does not add extra meaning beyond what the schema provides, so baseline 3 is appropriate.
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 and specifies return fields (id, type, params, etc.). It distinguishes from siblings by referencing use for finding an id to cancel, which relates to 'unsubscribe' tool.
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 context for use: 'review what you're monitoring before adding more or to find an id to cancel.' This implies appropriate timing relative to 'subscribe' and 'unsubscribe' tools, but does not explicitly state when not to use.
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 distinct purposes, but there is notable overlap between ask_pipeworx and ask_pipeworx_grounded (same underlying data query, different answer modes), and multiple polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges) can confuse agents about which to use for a given betting query. Memory tools (remember/recall/forget) are clear, but the mix of museum, financial, and prediction market tools under one server increases ambiguity.
All tool names use snake_case consistently, but the naming pattern is inconsistent: some start with a verb (search_objects, list_subscriptions, remember) while others start with a noun or modifier (ai_visibility_check, entity_profile, polymarket_arbitrage). The 'ask_' prefix is used twice, but overall there is no single predictable convention like verb_noun across the set.
29 tools is high but not excessive for a general-purpose data server. However, the server is named 'Va Museum' which implies a narrow domain, making the count seem bloated. The set includes many tools unrelated to a museum (e.g., prediction markets, SEC filings), so the count is appropriate only if the server's actual scope is broad and multi-domain.
For a museum-focused server, the tool surface is severely incomplete, with only two museum-specific tools (search_objects, get_object) out of 29. Even as a general-purpose server, it lacks tools for common operations like updating or deleting resources, and the coverage of domains (e.g., no tool for creating or managing user data) feels ad hoc rather than systematically complete.