elc-partnership-builder

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

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds genuinely useful behavioral context beyond that: 'No contact details collected here — the booking page handles everything' and that passing preset_id/item_ids causes 'the response then carries a paste-ready booking note.' This clarifies the tool's side-effect-light nature without contradicting the 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?

The description is dense but well-organized: purpose first, then usage conditions, then parameter behavior. Every sentence earns its place, and there is no filler or repetition of schema content.

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 three parameters, no output schema, and a set of sibling alternatives, the description covers what the tool does, when to use it, how the optional parameters change the response, and what the user-facing outcome is. An agent has enough information to invoke it correctly.

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 coverage is 100%, but the description adds real semantic value for the optional parameters: 'Pass preset_id and item_ids if a package was composed: the response then carries a paste-ready booking note, so the call starts from their numbers instead of from scratch.' This goes beyond the schema by explaining why and how these parameters affect the outcome.

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 states a specific verb and resource: 'book' a '1:1 intro meeting with Marian Kamenistak, ELC's founder.' It also positions itself as 'the second legitimate ending besides request_offer,' sharply distinguishing it from siblings and making the tool's purpose unmistakable.

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 names the alternative ('besides request_offer') and gives concrete trigger conditions: 'whenever the visitor hesitates, wants a human, or the package needs tailoring beyond the catalog.' This gives an agent clear decision criteria for when to select this tool over siblings.

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

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

Beyond the readOnlyHint/idempotentHint annotations, the description adds critical behavioral context: the server is authoritative for arithmetic, the tool never forecasts hires, and it returns a forwardable plain-text memo meant for an approver rather than the current user. This goes well beyond what annotations alone convey and prevents misuse.

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?

Three sentences with no filler: the first states the core output, the second gives usage timing and audience context, the third explains the authoritative-computation boundary. Every sentence carries actionable weight and is front-loaded.

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 tool with 8 parameters, no output schema, and a consequential business-decision purpose, the description explains what it returns, when to use it, what to avoid, and how it fits the conversation flow. Nothing an agent needs to select and invoke it correctly is missing.

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 description coverage is 100%, so the baseline applies. The description does not add parameter-specific guidance beyond mentioning the 'basket' (item_ids) and 'package' (preset_id), but the schema already documents each parameter fully. It neither improves nor harms parameter understanding.

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 opens with a specific verb+resource mapping: 'Turns a composed basket into the argument that gets it approved,' then enumerates concrete outputs (recruiter-fee equivalence, break-even hire count, cost per room, approval_memo). It clearly distinguishes itself from siblings by positioning itself before request_offer and disclaiming hire forecasting.

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 trigger conditions ('whenever money, ROI, justification or "I need to convince my CFO/CTO" comes up'), an explicit alternative ('before request_offer'), and a strong exclusion ('Never compute this arithmetic yourself'). This leaves no doubt about when to select this tool over its siblings.

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

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

Beyond the readOnly/idempotent annotations, the description discloses that the tool is the authoritative price source and warns against doing own arithmetic, explains the return contents (total, selected items with prices, addable items), and gives guidance about foundation items. It also says to call again after every change, reinforcing idempotent stateless behavior.

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?

Four tight sentences with no filler. The purpose is front-loaded, and each sentence adds value: invocation, authoritative output, foundation-item guidance, and call frequency/next step.

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?

With no output schema, the description adequately explains what will be returned and how to interpret it. It also covers when to call, what to pass, and what to advise about foundation items, making it complete for an agent to invoke correctly.

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 description coverage is 100% and each parameter already has substantial detail, including the seats headcount warning and item_ids provenance. The description only restates 'pass the preset and the item ids currently ON' without adding new parameter-level meaning, so the baseline 3 applies.

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?

States a specific verb and resource: customize a package by toggling line items and recomputing the authoritative total. It clearly distinguishes itself from related tools by describing this as the conversational toggle board that operates on currently-ON item IDs and hands off to request_offer next.

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 clear context: call it after every visitor change, pass the preset and currently-ON item IDs, and use its computed total as authoritative. It also routes the workflow forward with 'Next: request_offer.' It does not explicitly name alternatives or exclusions, but the intended usage is well implied.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds meaningful behavioral context beyond those flags: the plan is deterministic, computed from the basket's scheduling metadata, and returns unplaceable items with reasons. It also reinforces that the tool does not mutate the basket, which matches and augments the 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?

Three sentences carry a lot of information: the core output, the computation source, the return payload, the scope restriction, and the pipeline order. There is a slight stylistic flourish in 'The moment the package becomes a year,' but it is not wasted. The description is front-loaded with the result and does not repeat annotations.

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?

Despite having no output schema, the description tells the agent what the tool returns: placed months, the recurring-every-month layer, and unplaceable items with reasons. It also states the scope constraint and pipeline position. It could say more about how start_month or anchors influence output, but the essentials for correct invocation are covered.

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 schema already documents all four parameters, and context description is unusually detailed. The description adds the concept of the 'basket' mapping to item_ids and mentions scheduling metadata as the computation basis, but it does not provide deeper per-parameter guidance. With schema coverage at 75%, the description is adequate but does not go beyond what the schema supplies.

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 states a precise verb and resource: it produces a deterministic month-by-month plan of what lands when for a basket. It clearly distinguishes itself from siblings by explicitly saying the plan contains ONLY items in the basket and never adds or moves an event, which is different from customize_package and request_offer.

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 gives explicit pipeline positioning: 'Call after customize_package, before request_offer.' It also implies when not to use the tool by warning against adding or moving events. It does not list every sibling alternative, but the sequencing is unambiguous and actionable.

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

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

Even with annotations covering readOnly and idempotent, the description adds rich behavioral detail: foundation items kept first, remaining items chosen cheapest-first, default AI-channel pricing, and exact response contents including unspent budget and cheapest excluded item. This goes far beyond the structured 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?

Each sentence earns its place: trigger, return value, selection algorithm, pricing basis, and authority are all covered in dense, well-ordered prose. The most important usage condition is front-loaded with concrete examples.

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?

With no output schema, the description compensates by clearly stating what the response contains: what fits, what was left out and why, unspent budget, and cost of the cheapest excluded item. Combined with full parameter schema coverage, no critical call-time decision is left unexplained.

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 coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema: budget is a ceiling, must-have items are kept first if they fit, and the 'against' parameter defaults to the AI-channel figure. This is useful supplemental semantics without needing to re-document every parameter.

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 names a specific verb and resource: composing the best package for an exact numeric budget, returning a deterministic best-value composition under a ceiling. It also differentiates itself from customize_package by framing it as the authoritative composition tool versus the authoritative total.

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 trigger is explicit: use when the visitor names a NUMBER rather than a band, with concrete examples ('8,000 approved', 'five thousand'). It also gives a clear when-not: never hand-pick a basket when a budget is stated, making this tool the authoritative composition.

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

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

Annotations already cover the safety profile with readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds useful context about when to call it, but it does not disclose what the tool returns or how the 'additional tools' are delivered, though this is partially implied by the tool name and openWorldHint.

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 a single sentence with no filler. It front-loads the primary action ('Check for additional tools') and packs the key usage condition and fallback guidance into a concise, readable statement.

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 one-parameter meta-tool, the description, annotations, and schema provide sufficient guidance to invoke it correctly. The only minor gap is that it does not explicitly describe the response format or what 'getting more tools' entails operationally, but this is largely inferable and not critical to calling the tool.

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 description coverage is 100%, and the schema already describes the 'context' parameter well: 'A description of your goal and what kind of tool would help accomplish it.' The tool description adds the nuance of 'specialized capabilities,' but it does not add significant semantic detail beyond the schema.

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's function: 'Check for additional tools.' It also identifies the purpose ('whenever your task might benefit from specialized capabilities') and implicitly distinguishes itself from the sibling tools, which are domain-specific actions rather than a meta/discovery 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?

The description gives explicit guidance on when to use the tool: 'whenever your task might benefit from specialized capabilities.' It even states the preferred behavior over existing alternatives with 'even if existing tools could work as a fallback,' making the decision boundary clear.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds behavioral context beyond annotations: it is an informational starting point, it routes non-qualifying users elsewhere, and it defines a follow-up action. It does not detail return format or pagination, but that is a minor gap for a simple read-only tool.

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 compact and well-structured: it front-loads the primary usage ('START HERE'), then states what is returned, who is excluded, and what to do next. Every sentence provides distinct value with no filler or redundant restating of the tool name or title.

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 one-parameter, read-only informational tool, the description is complete. It covers the target audience, the content returned, the exclusion case, and the follow-up tool. No output schema exists, but the description sufficiently explains what the agent can expect to deliver to the visitor.

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 schema already provides a thorough description of the only parameter, context, including word-count requirements, perspective rules, and sensitive-data restrictions. The tool description does not add parameter-level detail, but with 100% schema description coverage, the baseline of 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 opens with 'START HERE' and states exactly what the tool returns: how company membership works, community reach figures, and the two qualifying questions with valid answers. It clearly targets companies considering an ELC membership and distinguishes itself by routing individual mentor-seekers to /mentor/ and pointing to match_package as the next step, so it does not blur with siblings.

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?

Usage guidance is explicit: 'START HERE for any company considering an ELC membership' with personas listed. It also states when not to use it ('Companies only — individuals seeking a mentor for themselves get pointed to /mentor/ instead') and names the follow-up tool ('After the visitor answers both questions, call match_package'). This gives clear decision rules and routing.

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

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

Annotations already mark the tool read-only, idempotent, and non-destructive, so the description adds value beyond that by disclosing the routing-matrix provenance, the real-price guarantee, default line-item inclusion, and the precise error behavior (listing valid ids). These details materially shape how an agent should handle failures and set expectations.

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 three sentences, each carrying a distinct purpose: core behavior/output, input handling and error protocol, and next-step routing. It is front-loaded with the most important resolution logic and contains no filler or redundant restatement of the tool name.

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 absence of an output schema, the description tells the agent exactly what to expect (matched package(s), real prices, default line items) and how to respond to bad input. It also supplies the relationship to neighboring tools, making the tool's position in the overall flow clear without needing extra documentation.

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 input schema fully describes all three parameters and references get_partnership_options for goal and budget ids, so baseline is 3. The description adds a crucial normalization rule—map free-text answers to the closest valid id—and clarifies what happens on invalid input, which goes beyond static schema types and helps the agent invoke the tool correctly.

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 names a specific operation ('Resolves goal + budget'), the underlying resource ('ELC's own routing matrix'), and a concrete return value ('matched package(s) with real prices and their default line items'). It also differentiates from siblings by pointing to customize_package and request_offer as downstream actions rather than competing alternatives.

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 gives clear operational guidance: map free-text answers to valid ids, and if input is bad re-ask rather than guessing. It also states which sibling to use next depending on whether line items need toggling or the offer should be sent as-is. It lacks an explicit 'when not to use this tool' statement, but the context is strong enough to make routing decisions.

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

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.

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.

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.

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.

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.

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.