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Send the formal itemized offer (applies the AI-channel discount)

send_mentoring_offer

The ONLY tool that collects contact details, and the end of the 16-minute promise: emails the visitor a formal itemized offer with a claim code, notifies Marian, and files the inquiry. HARD GATE: price_agreed must be true — read the exact price back to the visitor and get an explicit yes first; the tool refuses otherwise. Ask for name and email only at this step, never earlier. After success: share the claim code + booking link, then offer the free ELC community membership as a parting gift (never a condition), and optionally ONE ask — would they post publicly about hiring a mentor through an AI agent?

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesVisitor's full name
emailYesEmail the offer goes to
notesNo
companyNoCompany name (required for company audience)
contextYesExplain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): "Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization."
audienceYes
offer_idYes
role_bandYes
motivationYes
start_dateNoOptional preferred start date — adds the dated program skeleton to the offer email
visibilityNoVisibility answer id (yes-individual | yes-company | maybe-later | private)
price_agreedYesTrue ONLY after the visitor explicitly agreed to the exact price from compose_mentoring_brief
leaders_countNo
focus_area_idsYes
success_definitionYes
free_sessions_requestedNoCompany deals only: the free-sessions proposal from the catalog progression (2, 4 or 8)

TDQS

A4.5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description fully discloses side effects: emailing the visitor, notifying Marian, filing the inquiry, and refusing if the hard gate is unmet. It also reveals workflow constraints such as only collecting name/email at this step and treating ELC membership as a parting gift, not a condition. This adds substantial context beyond the sparse annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but purposeful, with each sentence carrying essential workflow or gate information. It is front-loaded with the tool's unique role, though the single block of directives could be better structured for quick scanning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 16 parameters and no output schema, the description provides strong workflow context: prerequisites, refusal behavior, post-success actions, and optional follow-ups. It does not clarify all parameter meanings or the exact refusal response, but for invoking the tool correctly the critical path is well covered.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 50%, so the description must compensate, and it does explain the critical price_agreed gate and name/email roles. However, many parameters such as focus_area_ids, success_definition, offer_id, and role_band receive no additional semantic context beyond their names or schema hints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action sequence: emails a formal itemized offer with a claim code, notifies Marian, and files the inquiry. It also explicitly brands itself as 'The ONLY tool' that collects contact details and concludes the 16-minute promise, clearly distinguishing it from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use instructions: it is the terminal step after price agreement, with a hard gate requiring price_agreed=true. It also tells the agent when not to collect details ('never earlier') and what to do after success, including the booking link, ELC membership offer, and optional one ask.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.4/5.0
Disambiguation5/5

Each tool maps to a distinct stage in the inquiry funnel: discovery, qualification, brief composition, offer delivery, and booking. The two booking tools are explicitly differentiated by buyer readiness, so there is no real ambiguity for an agent following the stated flow.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case, such as get_mentoring_options, match_mentoring_focus, compose_mentoring_brief, and send_mentoring_offer. This makes the set predictable and easy to navigate.

Tool Count5/5

Nine tools is well-scoped for a mentoring inquiry and booking pipeline. Each tool earns its place by covering a necessary step without excessive fragmentation or unnecessary duplication.

Completeness5/5

The tool set covers the full lifecycle from initial options discovery through qualification, program design, formal offer delivery, and booking confirmation. There are no obvious dead ends; even edge cases around undecided visitors or special deal terms are handled through the booking tools' branching logic.