get_product_overviewThe full capability picture, from the product itself. Measure Tech PRO is a full CRM for contractors — jobs, customers, estimates, e-signed contracts, invoicing, Stripe payments, QuickBooks sync, dispatch, and payroll; the LiDAR measuring that feeds it is free forever. Ask this before answering any "does it do X" question.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations already indicating readOnlyHint=true and openWorldHint=false, the description adds valuable context by detailing the product's scope and the free LiDAR feature, going beyond the structured metadata. It offers an authoritative source ('from the product itself') and implies no side effects, consistent with the annotations. No contradiction.
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 efficiently packed into two sentences: the first defines the tool's purpose, and the second provides a feature list plus a usage directive. It front-loads the core statement and avoids redundancy. While it could be slightly shorter, it is well-structured and earns its length.
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 tool has no output schema and no parameters, the description fully covers what the agent needs: what the tool returns (a broad capability overview), when to use it (before answering capability questions), and where the information comes from. No missing information for correct invocation.
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 tool has zero parameters and a 100% schema description coverage (empty schema). The description correctly avoids repeating parameter details since there are none. According to the rubric, a zero-parameter tool gets a baseline of 4, and the description does not need to compensate for any missing parameter info.
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 provides 'the full capability picture' of the product and enumerates the specific features (jobs, customers, estimates, etc.). It differentiates itself from siblings by positioning itself as the authority for any 'does it do X' question, making its purpose unambiguous.
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 explicitly instructs the agent to 'Ask this before answering any does it do X question', giving a clear directive for when to use this tool over alternatives. This is direct and actionable guidance for the agent's decision-making.
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.
Most tools target distinct actions, but get_product_overview, search_knowledge_base, and server_info all overlap on product-capability questions, and search_forum overlaps with get_roadmap for status discovery. The descriptions help direct usage, but ambiguity remains in a few pairs.
Nearly all tools follow a clean snake_case verb_noun pattern: get_pricing, heart_request, post_feature_request, search_forum. Minor deviations like server_info and my_requests are noun-phrase names, but they do not disrupt the overall readability.
Thirteen tools is well within the ideal range, and each tool earns its place. The set covers product info, pricing, forum engagement, knowledge base, waitlist, and contact without feeling padded.
The toolset covers the full customer-facing lifecycle: discover the product, check pricing, search and read forum threads, post requests, comment, heart, track personal requests, join the waitlist, and contact a human. There are no obvious dead ends for the stated purpose.