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dhl_mentor_match

Match students and classes with industry mentors using grade level, subject, learning objectives, location, and session type to find tailored expertise, availability, and curriculum alignment.

Instructions

Match students and classes with industry mentors based on grade level, subject area, learning objectives, location, and session type. Returns matched mentors with expertise profiles, availability, session format recommendations, and curriculum alignment insights.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locationYesGeographic region or province/state
grade_levelYesStudent grade level (K-12)
session_typeYesPreferred session format
subject_areaYesSubject area for mentoring
learning_objectivesYesSpecific learning objectives to achieve
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. However, it only describes what the tool returns and does not mention any side effects, authorization requirements, rate limits, or safety implications like read-only or destructive nature.

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 two sentences: the first states the action and criteria, the second describes the return value. It is front-loaded, every sentence adds value, and there is no redundancy.

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

Completeness4/5

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

Given the tool's complexity (5 required parameters, no output schema), the description covers the inputs and the nature of the outputs (mentor profiles, availability, etc.) adequately. It lacks details like result ordering, limits, or error cases, but provides a reasonable overall overview.

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 coverage is 100%, so the baseline is 3. The description lists the parameters but does not add new meaning beyond what is already in the schema descriptions (e.g., enum values). No deeper insights into how parameters affect matching are provided.

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 uses a specific verb 'Match' and clearly identifies the resources (students and classes with industry mentors) and the criteria. It distinguishes itself from sibling tools like dhl_career_explorer, which focuses on career exploration, and dhl_curriculum_align, which aligns curricula, making its purpose unique.

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

Usage Guidelines2/5

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

The description does not provide any guidance on when to use this tool versus alternatives, nor does it specify prerequisites or contexts where matching is appropriate. There is no mention of exclusions or when not to use it.

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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