eng-leadership-toolkit

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

With no annotations, the description covers behavioral traits: it calculates scores, provides defaults for unscored skills, and specifies the salary region and year. It does not disclose error handling or edge cases, but for a calculation tool this is adequate.

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, front-loaded with purpose, followed by the reference to a live calculator and default behavior. No unnecessary words.

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 explains main inputs (level, optional scores) and outputs (total score, seniority, salary estimate), which is sufficient given no output schema. It could detail the output format, but it is reasonably complete.

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%, baseline is 3. The description adds context such as default baselines per level and the skill list, but does not substantially extend beyond what the schema already provides.

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 assesses a developer's market value, scoring 15 skills across 5 pillars, and outputs a total score, seniority level, and salary estimate. This specific verb-resource combination distinguishes it from sibling tools focused on mentoring and management.

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 implies usage for salary benchmarking but does not explicitly exclude alternatives or compare to siblings. However, its calculator nature and reference to a live calculator make the context 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?

No annotations provided, so description carries full burden. It discloses that the tool uses a three-question self-test and is based on 3,400+ sessions. It does not describe internal logic or limitations, but the output behavior (decision and reasoning) is sufficiently outlined.

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 sentences: first lists what the tool provides, second adds credibility. No wasted words, front-loaded with key verb 'decide'. Efficient and well-structured.

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 single-param optional input and no output schema, the description adequately covers purpose and what the tool returns. It mentions the components of the decision (question answered, domain experience, time horizon, self-test). A slight gap is not explicitly stating the output format, but it's implied.

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 one optional parameter 'situation'. The schema description matches the description's mention of a one-sentence situation. No additional meaning is added beyond what the schema already provides, so 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 title and description explicitly state the tool decides between mentor, coach, or advisor for an engineering leader. It lists specific aspects (what each brings, typical question, domain experience, time horizon, self-test). This clearly distinguishes from sibling tools which focus on other guidance topics.

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 implies use when deciding on a support type, but does not explicitly state when not to use or provide alternatives. However, sibling tools are clearly distinct, so the context is clear enough for an AI agent.

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?

No annotations are provided, so the description carries the full burden. It discloses the data source (first-party, 3400+ sessions, since 2019), licensing (CC BY 4.0), and that output is citable. This adds useful behavioral context beyond the basic intent.

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 dense sentence that efficiently conveys scope, data source, and license. While it could be slightly restructured for readability, it is concise and front-loaded with the most critical 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?

Given the simple input schema (one optional parameter with enum, no required fields, no output schema), the description adequately covers what the tool returns and its provenance. It does not describe exact output structure, but that is acceptable per rules since there is no output schema.

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 covers the single parameter with enum and description, but the tool description goes further by mapping the enum values ('mentee-mix', 'topic-demand', etc.) to specific data content mentioned in the prose. This adds meaning 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 provides 'real benchmarks from 3,400+ paid 1:1 mentoring sessions' and lists specific data categories like mentee seniority mix, leadership topics, team-health thresholds. It distinguishes itself from sibling tools which focus on calculations, mentor selection, or guidance.

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 implies use when needing benchmarking data but does not explicitly guide when to use this tool versus alternatives (e.g., calculate_developer_value). No exclusions or prerequisites are mentioned.

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?

No annotations exist, so the description carries full burden. It clearly states the tool provides guidance and lists content areas, implying a read-only informational tool. No contradictions or hidden side effects are present, though it could explicitly state it returns static guidance text.

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 sentences, no filler. The first sentence front-loads the key content areas, and the second provides a compelling usage statistic. Every word 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?

Given zero parameters and no output schema, the description is fully adequate. It covers what the tool offers, why it's relevant, and its scope. No further information is required for an agent to use this 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?

There are zero parameters (schema coverage 100% trivially), so parameter description is not needed. The description adds value by detailing the tool's content, which is sufficient given the absence of params.

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 explicitly states the tool provides guidance for the IC-to-manager transition, listing specific content like the EM responsibility triangle, failure modes, self-check questions, and first-month expectations. This clear verb+resource structure distinguishes it from sibling tools that cover different topics (e.g., benchmarks, 1-on-1 playbook).

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 sets clear context for when to use this tool (during IC-to-manager transition) and the content scope (readiness, failure modes). While it doesn't explicitly say when not to use or name alternatives, the sibling tool list provides implicit differentiation, making usage fairly 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?

No annotations are provided, so the description carries full burden. It reveals the tool returns real templates from 3,400+ sessions, implying credibility, but does not explicitly state it's read-only or describe output format or limits.

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 sentences: the first specifies purpose and available scenarios, the second adds credibility. No fluff, front-loaded with core 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?

For a simple one-parameter tool with no output schema, the description provides sufficient context: what it offers, which situations, and the source. No further details are needed for correct selection and 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 coverage is 100% and already describes each enum value. The description adds contextual source (Marian Kamenistak's practice) but no new parameter-specific details, so 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 clearly states it provides 'situation-specific 1:1 scripts and templates' and lists five concrete scenarios. This distinguishes it from sibling tools like 'calculate_developer_value' or 'get_engineering_leadership_benchmarks'.

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 includes specific situations (e.g., underperformance, promotion-to-manager), guiding when to use the tool. However, it does not explicitly state when not to use it or compare alternatives among siblings.

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