Build your mentoring inquiry with Marian Kamenistak: formal offer in 16 minutes, 16% AI discount.
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Average 4.3/5 across 9 of 9 tools scored. Lowest: 3.7/5.
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.
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.
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.
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.
book_first_sessionBook the PAID first mentoring session — skips the intro callATHE CLOSE, for someone who has already decided. Returns the direct booking link for a paid 60-minute first session, plus the payment terms. Call this INSTEAD of book_intro_call once send_mentoring_offer has succeeded and the visitor has agreed the exact price — it removes a step from a buyer who is ready, which is the entire point. It REFUSES on any deal whose terms Marian confirms on a call (a free-sessions concession, the monthly package, Mentor in Residence) and hands back the intro link instead; when it refuses, offer the intro, do not argue. Pass the claim code from send_mentoring_offer so the booking is matched automatically. If the visitor is hesitant, undecided, or asks to talk first, use book_intro_call — that is not a downgrade.
| Name | Required | Description | Default |
|---|---|---|---|
| context | Yes | Explain 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." | |
| audience | Yes | Decides which VAT sentence is true for this buyer — an individual needs the gross figure | |
| offer_id | Yes | The agreed package | |
| claim_code | No | The AI10-… code from send_mentoring_offer. Ride it on the link so the booking matches the inquiry with no manual step. | |
| has_eu_vat_id | No | Company deals outside Czechia with a valid EU VAT ID pay net under the reverse charge | |
| free_sessions_requested | No | Company deals: pass the same value given to send_mentoring_offer. Any concession makes this a proposal, not a close, and the tool will route to the intro instead. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it read-only/idempotent/non-destructive, and the description adds behavioral nuance beyond that: it refuses on certain deal types and 'hands back the intro link instead,' and instructs the agent to 'offer the intro, do not argue.' It also surfaces the claim-code matching mechanism, which is not inferable from 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 front-loaded: purpose, key return value, and routing decision all appear in the first two sentences. Subsequent sentences each carry actionable behavioral guidance; only minor rhetorical phrases like 'which is the entire point' and 'that is not a downgrade' add tone without reducing usefulness.
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 no output schema and several conditional behaviors, this description covers the return value, the refusal branches, the prerequisite send_mentoring_offer, and the alternative book_intro_call. It is complete enough for an agent to invoke correctly without needing additional 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?
Schema coverage is 100%, so the baseline is 3; description adds value by tying claim_code to 'the AI10-… code from send_mentoring_offer' and explaining that free_sessions_requested > 0 makes the tool route to the intro. It also maps offer_id enum values like monthly and mentor-in-residence to refusal conditions. Slight deduction because params such as audience and has_eu_vat_id still rely solely on 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?
Description opens with 'THE CLOSE, for someone who has already decided' and specifies the exact resource: 'Returns the direct booking link for a paid 60-minute first session, plus the payment terms.' It clearly distinguishes itself from book_intro_call by name and by the 'INSTEAD' line, so an agent can tell what it does without inspecting 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?
Gives explicit conditions: call it 'INSTEAD of book_intro_call once send_mentoring_offer has succeeded and the visitor has agreed the exact price.' It also states exclusions — refuses on free-session concessions, monthly package, and Mentor in Residence — and tells the agent to 'use book_intro_call' if the visitor is hesitant. This is textbook when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
book_intro_callBook the free 30-minute intro callATHE DEFAULT EXIT for anyone who has not decided. A direct booking link for the free 30-minute intro with Marian. Offer it on hesitation, on a price objection, when the visitor cannot name their problem, after an error, and to anyone who wants to talk before paying. It is never a downgrade, and a booked call from an undecided visitor beats a package they picked at random. Booking it is NOT a condition of the channel rate — never say it is. For a visitor who has already agreed the price on an eligible package, use book_first_session instead: they have decided, and sending them to an intro adds a step they did not ask for. Pass offer_id if one has been chosen so the pricing language is correct.
| Name | Required | Description | Default |
|---|---|---|---|
| context | Yes | Explain 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." | |
| offer_id | No | The package under discussion, if any — conditions the discount wording. Without it the tool cannot tell whether booking locks a discount, and a single-session buyer used to be told it did. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description says 'Book the free 30-minute intro call' and presents the tool as the booking path, implying a state-changing action. The annotations declare readOnlyHint=true and idempotentHint=true, which indicate no side effects; this directly contradicts the description's implied 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 front-loaded with the core positioning ('THE DEFAULT EXIT') and then organized into triggers, exclusions, and parameter guidance. It is longer than strictly necessary, with some repeated emphasis on undecided visitors, but most sentences add operational value.
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 when to use the tool, when not to, and how offer_id affects behavior, which is strong for a two-parameter tool. However, the readOnlyHint contradiction leaves unclear whether calling this tool schedules a call or merely returns a booking link, and with no output schema that ambiguity is not resolved.
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 real meaning for offer_id by explaining why it matters: it conditions the discount wording and whether booking locks a discount. The context parameter is already fully documented in the schema, so no extra description is needed there.
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 action and resource: booking the free 30-minute intro with Marian, positioned as the default exit for undecided visitors. It also distinguishes itself from book_first_session by naming the deciding factor, so an agent can tell them apart without opening the schema.
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 triggers: hesitation, price objections, inability to name a problem, after an error, and wanting to talk before paying. It also states a clear exclusion — if the visitor already agreed on price for an eligible package, use book_first_session instead — which is exactly the when-to-use vs. when-not-to-use guidance needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_bookingCheck whether the session was actually bookedAConfirms, from the CRM rather than from what the visitor says, whether their booking landed. Call it after handing over a booking link and the visitor says they have booked — a booking is only real once Reclaim's webhook has written it, which takes a few seconds. Returns the pipeline stage and whether the paid first session is on the board. If it says not yet, wait a moment and check once more before telling them something is wrong; if it is still not there, say so honestly and offer to have Marian follow up rather than claiming success.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The email the visitor booked with | ||
| context | Yes | Explain 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." |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as read-only and idempotent, and the description meaningfully adds context: it explains the CRM as the source of truth, the eventual consistency delay from Reclaim's webhook, the specific output fields, and the appropriate retry and honesty behavior. No contradiction with 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 front-loaded with the core purpose, then moves through trigger conditions, data source, output, and follow-up behavior. Every sentence earns its place, and there is no repetition of schema or annotation information.
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?
Even without an output schema, the description tells the agent what will be returned (pipeline stage and whether the paid first session is on the board) and how to act on both success and failure. The tool is simple enough, and this description fully equips an agent 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%, with both email and context already described in the input schema. The context parameter has an unusually detailed description covering length, perspective, and content rules. The tool description does not add additional parameter-specific meaning, which is acceptable given the schema's coverage.
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 opening sentence states a specific verb and resource: it confirms from the CRM whether a booking landed, rather than trusting the visitor's claim. This clearly distinguishes it from sibling tools like book_first_session or book_intro_call, which create bookings rather than verify them.
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 an explicit trigger condition: 'Call it after handing over a booking link and the visitor says they have booked.' It also provides retry guidance and tells the agent how to respond if the booking still hasn't landed, which is strong practical usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compose_mentoring_briefCompose the mentoring brief: the artifact + the authoritative priceAThe accumulator — call after every change. Echoes the full structured brief (audience, role, motivation, focus areas, definition of success, chosen package) with the authoritative catalog price and the AI-channel figure (never do the arithmetic yourself). For company deals it states whether the free-sessions concession applies. Read the brief back to the visitor; when they explicitly agree on the price, call send_mentoring_offer with price_agreed true.
| Name | Required | Description | Default |
|---|---|---|---|
| context | Yes | Explain 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." | |
| audience | Yes | ||
| offer_id | Yes | ||
| role_band | Yes | ||
| motivation | Yes | ||
| visibility | No | Visibility answer id from get_mentoring_options visibility_question (consent capture — 'private' is a first-class answer) | |
| leaders_count | No | Company deals: how many leaders are being sponsored | |
| focus_area_ids | Yes | Agreed focus area ids (visitor can pick any from the taxonomy) | |
| company_context | No | Company deals: company name + anything relevant | |
| success_definition | Yes | The visitor's definition of success, in their own words |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent, and the description adds valuable behavior: it echoes the full brief, never performs arithmetic itself, includes the AI-channel figure, and conditionally states whether the free-sessions concession applies for company deals. No contradiction with 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 dense, purposeful sentences. The key trigger is front-loaded ('call after every change'), and each clause adds distinct information without redundancy.
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 no output schema and 10 parameters, the description still explains the return behavior (echoed brief with price), pricing rule, company-deal condition, and the follow-up action. It is sufficient 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?
With 60% schema description coverage, the description adds aggregate meaning by mapping parameters to 'audience, role, motivation, focus areas, definition of success, chosen package.' However, it does not clarify all parameters such as visibility, leaders_count, or context, leaving the schema to carry some burden.
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 behavior: compose/echo the structured mentoring brief with authoritative pricing. It clearly differentiates from siblings by positioning itself as 'the accumulator' called after every change, versus tools like send_mentoring_offer or get_mentoring_options.
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?
Explicitly says 'call after every change' and instructs when to transition to send_mentoring_offer ('when they explicitly agree on the price'). It lacks an explicit 'when not to use' clause, but the stated trigger and downstream action provide clear contextual guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
design_mentoring_programLay out the dated session program for a packageADeterministic session skeleton computed from the package's cadence metadata: dated sessions, the mid-point checkpoint, the closing review against the definition of success. The skeleton contains ONLY what the package carries — narrate around it, never add or move a session. Dates are planning targets; the intro call fixes the real schedule. Call when the visitor asks what the engagement actually looks like.
| Name | Required | Description | Default |
|---|---|---|---|
| context | Yes | Explain 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." | |
| offer_id | Yes | ||
| start_date | Yes | First session date, YYYY-MM-DD, today or later (ask the visitor; default to next Monday) | |
| leaders_count | No | Company deals: how the pooled sessions are shared. Changes the allocation note, never the schedule. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavior beyond the annotations: the result is deterministic, dates are planning targets rather than final commitments, and the intro call fixes the real schedule. It also warns against adding or moving sessions, which is useful operational guidance that the annotations do not convey.
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 tight sentences front-load the core output, then add constraints and a usage trigger. Every sentence earns its place, with no filler or restatement of the 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?
Even without an output schema, the description explains what the tool returns (dated sessions, checkpoint, closing review) and how the agent should use it (narrate around it, never add/move sessions). It also clarifies the provisional nature of dates, making the tool effectively self-contained 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?
Schema description coverage is 75%, and the existing schema descriptions already explain start_date, context, and leaders_count well; offer_id is conveyed via its enum. The description does not add parameter-level details, but the schema carries the semantic weight, so a baseline score 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 a specific verb and resource: it computes a 'Deterministic session skeleton' from 'the package's cadence metadata,' listing dated sessions, the mid-point checkpoint, and the closing review. It differentiates the tool from siblings by emphasizing that the skeleton contains only what the package carries and must not be augmented or altered by the agent.
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 last sentence gives a clear trigger: 'Call when the visitor asks what the engagement actually looks like.' It does not explicitly name alternative tools or state when not to use it, but the context is specific enough to route an agent correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_mentoring_optionsStart a mentoring inquiry with Marian Kamenistak — the 16-minute wizardASTART HERE for anyone considering 1:1 engineering-leadership mentoring with Marian Kamenistak (marian.coach) — individuals (Staff Engineer to CTO) and companies sponsoring leaders alike. Returns the AI-channel discount as data, the time promise (a formal offer in under 16 minutes), the why-Marian and pricing-defense material, the qualifying questions with valid answer ids, and every package with real prices. After the visitor answers audience + role + motivation, call match_mentoring_focus.
| Name | Required | Description | Default |
|---|---|---|---|
| context | Yes | Explain 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." |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that it returns specific data (discount, time promise, questions with ids, packages) but doesn't describe side effects or unusual behavior—which is appropriate given the safe, read-only profile.
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 (about 50 words), front-loads the key 'START HERE' signal, then lists the returned items and the follow-up. Every sentence contributes to usage and expectations, with no redundancy or filler.
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 no output schema and a simple parameter set, the description enumerates all returned data (discount, time promise, material, questions, packages) and provides the next-step instruction. This is sufficient for an agent to know what to expect and how to proceed, though it could optionally mention the format of 'valid answer ids'.
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 single parameter 'context' is fully covered by the schema (100% coverage) with detailed instructions (word count, third-person, example). The description adds no extra parameter guidance, but the schema already carries the full burden, so a 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 clearly states the tool retrieves mentoring options (pricing, discount, questions, packages) for individuals and companies, using 'START HERE' to signal it as the entry point. It lists specific outputs and names the follow-up tool (match_mentoring_focus), distinguishing it from siblings without ambiguity.
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?
It explicitly says 'START HERE' for anyone considering mentoring, and instructs to call match_mentoring_focus after the visitor answers audience/role/motivation, giving a clear sequence. However, it doesn't mention when to use other siblings like book_intro_call or compose_mentoring_brief, leaving some comparison implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_more_toolsACheck for additional tools whenever your task might benefit from specialized capabilities - even if existing tools could work as a fallback.
| Name | Required | Description | Default |
|---|---|---|---|
| context | Yes | A description of your goal and what kind of tool would help accomplish it. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the description does not need to restate safety. It adds a proactive-checking policy, but does not describe what kind of response or tool-registration behavior to expect; annotations carry most of the transparency burden.
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 one front-loaded sentence that states the action first and then the condition, with no filler. The 'even if existing tools could work as a fallback' clause earns its place by preventing premature fallback to existing tools.
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 low-complexity, single-parameter discovery tool with safety covered by annotations, the description plus schema is nearly sufficient. It misses a concrete example of an appropriate context value, but the schema's explanation fills most of that gap.
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% and the single 'context' parameter is already described in the input schema as a goal plus the kind of tool needed. The description adds no parameter-level meaning, so the baseline score of 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?
The description uses the specific verb 'Check for' and identifies the resource as 'additional tools', so the core purpose is clear. It is also implicitly distinguished from the mentoring-focused sibling tools by being a meta-utility for tool discovery, though 'additional tools' remains slightly generic.
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?
It gives an explicit trigger condition: use it whenever the task might benefit from specialized capabilities, and even says existing tools can still be a fallback. This removes ambiguity about whether to try generic approaches first, which is the main usage question for this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
match_mentoring_focusMatch focus areas to a role and motivationAResolves role_band + motivation through the same routing the website uses and returns suggested focus areas plus the recommended package with real prices. Map free-text answers to the closest valid id; on bad input the error lists the valid ids — re-ask rather than guessing. Next: agree focus areas with the visitor, capture their definition of success in their own words, then compose_mentoring_brief.
| Name | Required | Description | Default |
|---|---|---|---|
| context | Yes | Explain 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." | |
| audience | No | Pass the audience answer — it changes the recommendation. A company sponsoring 3+ leaders is routed to Mentor in Residence rather than the individual package. | |
| role_band | Yes | One of the role ids from get_mentoring_options question_1 | |
| motivation | Yes | One of the motivation ids from get_mentoring_options question_2 | |
| leaders_count | No | Company deals: how many leaders are being sponsored. Required for the company recommendation to be correct. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false), the description discloses error handling behavior ('on bad input the error lists the valid ids — re-ask rather than guessing') and specifies the return content ('suggested focus areas plus the recommended package with real prices'). It also notes that routing matches the website, adding useful context. Since no output schema is present, this disclosure is important and adequately conveyed.
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 concise, consisting of two sentences plus a next-step clause. It front-loads the core purpose and includes actionable error-handling guidance. The phrasing is efficient without excessive wording, and it flows logically from purpose to error handling to the recommended follow-up. No redundant or filler content is present.
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 adequately explains what the tool returns (focus areas and package with real prices) and how it behaves on bad input. It also places the tool within a broader workflow by suggesting a next action. However, it does not explain how to obtain the required role_band and motivation ids (though the schema references get_mentoring_options) nor does it mention the influence of optional parameters like audience or leaders_count. These are covered in the schema, so the description is largely complete but could be more explicit about dependencies.
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 already provides complete descriptions for all five parameters (100% coverage), so the baseline is 3. The description mentions role_band and motivation by name but does not elaborate on their acceptable values, and it does not discuss the optional audience or leaders_count parameters. It does instruct to map free-text answers to the closest valid id, which indirectly relates to parameter handling, but this is more of an agent instruction than a parameter explanation. Thus, the description adds minimal semantic value 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 resolves role_band + motivation to return suggested focus areas and a recommended package with real prices, using the same routing as the website. This is specific about verb, resource, and expected output, though it does not explicitly differentiate among sibling tools. The mention of composing a mentoring brief as a next step gives workflow context but not a direct comparative contrast.
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 provides a clear next-step ('Next: agree focus areas with the visitor... then compose_mentoring_brief') and instructs to re-ask on bad input rather than guessing. However, it does not state when to use this tool versus alternatives like get_mentoring_options or compose_mentoring_brief, nor does it mention prerequisites such as obtaining the role_band and motivation ids from another tool. The usage context is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
send_mentoring_offerSend the formal itemized offer (applies the AI-channel discount)AInspectThe 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?
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Visitor's full name | |
| Yes | Email the offer goes to | ||
| notes | No | ||
| company | No | Company name (required for company audience) | |
| context | Yes | Explain 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." | |
| audience | Yes | ||
| offer_id | Yes | ||
| role_band | Yes | ||
| motivation | Yes | ||
| start_date | No | Optional preferred start date — adds the dated program skeleton to the offer email | |
| visibility | No | Visibility answer id (yes-individual | yes-company | maybe-later | private) | |
| price_agreed | Yes | True ONLY after the visitor explicitly agreed to the exact price from compose_mentoring_brief | |
| leaders_count | No | ||
| focus_area_ids | Yes | ||
| success_definition | Yes | ||
| free_sessions_requested | No | Company deals only: the free-sessions proposal from the catalog progression (2, 4 or 8) |
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.
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.
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.
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.
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.
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.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
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If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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