digital-human-library

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool serves a unique, clearly defined purpose (career exploration, curriculum alignment, impact reporting, mentor matching, program design, session planning). No two tools overlap in function.
Naming Consistency5/5
All tools follow a uniform 'dhl_verb_noun' pattern, using lowercase with underscores. This provides clear predictability and readability.
Tool Count5/5
Six tools is an appropriate scope for a mentoring platform, covering key workflows without being overwhelming or insufficient.
Completeness4/5
The tool set covers core mentoring operations (matching, planning, reporting, alignment). Minor gaps exist, such as direct mentor/student profile management, but the surface is largely comprehensive.
Average 3.4/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description carries full burden. It states the tool returns data, implying a read-only or generative operation, but does not disclose safety profile, side effects, or authorization needs. Insufficient details for safe invocation.
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 extremely concise: two sentences with no fluff. The first sentence defines the purpose, the second enumerates output types. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter tool with no output schema and no annotations, the description covers the core purpose and outputs but lacks constraints (e.g., regional support, outcome format) and does not explain how inputs map to outputs. Adequate but not thorough.
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 covers all 5 parameters with descriptions (100% coverage). The description adds no further parameter-level detail beyond what the schema provides, meeting the baseline but not exceeding it.
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 clearly states the tool aligns mentoring sessions with curriculum standards and learning outcomes, listing specific return values (aligned standards, cross-curricular connections, assessment rubrics, pre/post activities). This distinguishes it from sibling tools like session_plan or career_explorer.
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?
No explicit guidance on when to use this tool versus alternatives. The description does not mention prerequisites, context, or exclusions, leaving the agent to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It states the tool 'returns complete program structure...' but does not disclose any side effects (e.g., whether it creates/modifies data), authorization needs, or rate limits. For a design tool, it likely generates output without mutation, but this is not explicit.
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 concise with two sentences: the first states the action and target, the second lists key output components. It is front-loaded and contains no redundant information, making it efficient for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 6 required parameters and no output schema, the description adequately explains what the tool returns but does not provide context about prerequisites (e.g., need for existing school data) or how the output integrates with sibling tools. It is minimally complete for a complex tool.
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?
The input schema has 100% coverage with clear descriptions for each parameter. The tool description adds no additional meaning beyond listing output types, which does not enhance parameter understanding. The schema itself is sufficient, 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Design custom mentoring programs for schools and districts.' It specifies the resources (schools and districts) and outputs (program structure, plans, schedules, etc.). It distinguishes from siblings like dhl_session_plan (focuses on individual sessions) and dhl_mentor_match (focuses on matching), making it clear this tool handles holistic program design.
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 guidance on when to use this tool versus alternatives like dhl_curriculum_align or dhl_mentor_match. It lacks explicit context for best use, such as prerequisites or scenarios where this tool is appropriate, leaving the agent to infer without direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries full burden. It only states the tool 'creates' plans, but does not disclose behavioral traits like idempotency, side effects, authorization needs, or whether the plan is saved or returned directly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and front-loaded with the key action. It is concise, though it could benefit from a clearer structure separating function from customization.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description should explain what the tool returns (e.g., plan format, structure) or any constraints. It only mentions output components vaguely, leaving significant gaps for an agent to understand the full behavior.
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% with descriptions for each parameter. The description adds context for mentor_expertise, student_grade, and duration, but omits topic and group_size, which are required. Overall, description adds some value but not fully comprehensive.
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 clearly states the tool creates structured mentoring session plans with specific components (discussion prompts, activities, follow-up assignments) and customization factors. It distinguishes itself from sibling tools like dhl_career_explorer or dhl_mentor_match which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for creating session plans customized by expertise, grade, and duration, but lacks explicit guidance on when to use this tool versus alternatives 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as read-only nature, side effects, required permissions, or response behavior. The agent lacks crucial context for safe invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and informative, listing key outputs. However, it could be more structured (e.g., bullet points) for easier parsing, but no unnecessary content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, no output schema, and no annotations, the description provides a reasonable overview of what the tool returns. However, it omits details on output format, pagination, or how results are delivered, leaving some gaps for a complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are already well-documented. The description adds overall context but does not enhance individual parameter meaning beyond the schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a career exploration tool for K-12 students and lists specific outputs (career pathways, day-in-the-life, education requirements, salaries, mentors, growth trends). This distinguishes it well from sibling tools like dhl_curriculum_align or dhl_mentor_match.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for career exploration by students but does not provide explicit guidance on when to use this tool versus alternatives (e.g., dhl_mentor_match for mentors only) or any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It lists return values (engagement metrics, learning outcomes, etc.) but does not disclose whether the tool is read-only, any authentication requirements, rate limits, or potential side effects. The description is minimally adequate but lacks behavioral context beyond output.
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 a single, efficient sentence that quickly communicates the tool's purpose and output. No unnecessary words or repetition.
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 lack of an output schema, the description compensates by enumerating return types (student engagement, learning outcomes, satisfaction, mentor effectiveness, ROI). It is sufficiently complete for a report-generation tool, though it could mention any result format or size constraints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67% with all parameters having descriptions. The tool description does not add any additional meaning beyond the schema's documentation of school_id, date_range (start/end dates), and metrics_requested (enum). With high schema coverage, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates comprehensive mentoring impact and engagement reports for schools. It lists specific output metrics (engagement, outcomes, satisfaction, mentor effectiveness, ROI), clearly distinguishing it from sibling tools like dhl_career_explorer or dhl_session_plan.
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?
No guidance on when to use this tool versus alternatives. For example, when a user needs to explore career paths or align curriculum, they might need a different tool. The description does not provide any context for when to choose this report generator over other sibling tools.
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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