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elc-partnership-builder

by io.engineeringleaders

Send the composed offer to ELC (applies the AI-channel discount)

request_offer

The ONLY tool that collects contact details, and the step that makes the AI-channel discount real. Sends the itemized offer to the visitor's email, notifies Marian (email + Slack), and files the company into ELC's partners queue. Ask for name, work email and company only when the visitor says they want the offer — never earlier. After success: share the confirmation, then make ONE optional ask: would they post publicly (LinkedIn/X) about building their membership with AI? Optional means optional — the discount is already theirs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kpisNoOptional: what they need to move this year, in their words
nameYesVisitor's full name
emailYesWork email the offer goes to
companyYesCompany name
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."
item_idsYesThe final basket: item ids toggled ON
preset_idYes
visibility_interestNoFrom the discovery question: do they want to invest in their visibility through the cooperation — as a company, through individual leaders, or stay quiet?
final_price_confirmedYesREQUIRED TRUE: set only after the visitor has seen and explicitly confirmed the exact final total (the discounted figure if the discount applies). Sending without this confirmation is refused.

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations, the description discloses concrete side effects: it sends an email, notifies Marian through two channels, files the company into a partners queue, and applies the AI-channel discount. It also clarifies that the discount is already granted even if the visitor declines the optional public post, reducing risk of coercive behavior.

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

Conciseness5/5

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

The description is three sentences with no filler. It front-loads the tool's uniqueness and core effect, then provides the exact interaction flow and an important ethical guardrail. Every clause earns its place.

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?

The description covers the full conversation flow from when to collect details through post-success behavior, which is essential for correct invocation. With no output schema, it does not describe return values or error responses, but the schema and annotations fill most other gaps. It is complete enough for an agent to select and use the tool correctly.

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 high at 89%, so the schema already explains most parameters. The description adds workflow-level meaning for name, email, and company by specifying when they should be collected, but it does not substantially clarify kpis, visibility_interest, preset_id, or context beyond what the schema provides. A baseline 3 is appropriate.

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 specific actions: sends the itemized offer to the visitor's email, notifies Marian via email and Slack, and files the company into ELC's partners queue. It also explicitly claims to be the ONLY tool that collects contact details, 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 Guidelines4/5

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

The description gives a clear trigger condition: collect contact details only when the visitor says they want the offer, never earlier. It also describes the post-success flow with the optional public-post ask. It does not name specific sibling alternatives, but the usage context is unambiguous.

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

A4.3/5.0
Disambiguation4/5

Most tools map cleanly to distinct funnel stages: qualify, match, customize, fit to budget, timeline, business case, offer, and booking. The only mild ambiguities are between match_package and fit_to_budget (both compose packages from budget input) and between request_offer and book_intro_call (both are conversion endpoints), though the descriptions do draw clear boundaries.

Naming Consistency5/5

All nine tool names follow the same verb_noun snake_case convention (get_partnership_options, customize_package, build_business_case, request_offer). get_more_tools is semantically a meta-tool, but it still follows the naming pattern, so there is no stylistic inconsistency.

Tool Count4/5

Nine tools is a well-scoped size for a guided sales-funnel server, and each domain tool corresponds to a meaningful workflow step. get_more_tools is a generic meta-tool that doesn't fully earn its place, which keeps this from a perfect score.

Completeness4/5

The toolset covers the full partnership lifecycle from qualification and package matching through customization, budgeting, timeline design, business-case generation, and final offer/booking. Minor gaps exist, such as no explicit package-catalog listing tool outside the matching flow and no direct handling of the individual-mentor path, but these are workaroundable or out of scope.

Resources