request_contactRelay a real person's request for human contact (sales question, demo, enterprise) to the Measure Tech PRO team. Nothing sends until a reply channel is given — an email or phone number the PERSON provided. Rate-limited.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Who is asking | |
| No | Their reply email — theirs, given by them | ||
| phone | No | Their phone, if they prefer a call | |
| message | Yes | What they want, in their words |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations, the description discloses a critical behavioral condition: nothing sends until a reply channel (email or phone) is provided by the person. It also mentions rate-limiting. This adds meaningful behavioral context that an agent could not infer from the schema or the all-false 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 compact sentences with no filler. The main action is front-loaded, followed by the key precondition and a short behavioral note. Every sentence 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?
For a simple four-parameter tool with no output schema, the description is nearly complete: it states purpose, required channel, and rate limit. It could add a brief note about the expected return behavior, but nothing in the current text leaves a caller without the essential information needed to invoke it successfully.
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?
With 100% schema description coverage, the baseline is 3, but the description adds extra meaning: the email/phone must be provided by the person, not invented by the AI. This clarifies the 'reply channel' condition that directly affects whether the tool succeeds, going beyond the schema's plain parameter descriptions.
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 uses a specific verb ('Relay') and names a concrete resource: a real person's request for human contact to the Measure Tech PRO team. It lists example intents (sales question, demo, enterprise) that clearly distinguish this from sibling tools like post_feature_request, join_waitlist, or heart_request.
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 conveys the intended context well: use this for a real person requesting human contact, especially sales/demo/enterprise follow-up. It does not explicitly name alternatives or state when not to use it, so it falls just short of full 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.
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.