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ZztIsolation

HDU Academic System MCP Server

by ZztIsolation
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TypeScript
Remote

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one handles authentication (hdu_login) and the other retrieves data (get_course_schedule). There is no overlap in functionality, and the dependency relationship (login required for schedule) is logically clear, preventing misselection.

    Naming Consistency4/5

    The tool names follow a consistent verb_noun pattern (hdu_login and get_course_schedule), with both using snake_case. The slight deviation is that 'hdu_login' includes a domain prefix, but this is minor and does not break readability or predictability.

    Tool Count2/5

    With only 2 tools, this server feels under-scoped for an academic system. While login and schedule retrieval are core functions, typical academic systems would include more operations (e.g., get_grades, register_courses, view_notices), making this set too thin for the apparent domain.

    Completeness2/5

    The tool surface is severely incomplete for an academic system. It covers authentication and one data retrieval function, but lacks essential operations like viewing grades, managing registrations, or accessing announcements. This will likely cause agent failures when trying to perform common academic tasks.

  • Average 3/5 across 2 of 2 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?

    With no annotations provided, the description carries full burden for behavioral disclosure. While '登录' (login) implies authentication and potential session creation, the description doesn't disclose what happens after login, whether sessions persist, what permissions are granted, or any rate limits/constraints. For an authentication tool with zero annotation coverage, this is a significant gap.

    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 extremely concise - a single phrase that directly states the tool's purpose. There's no wasted language or unnecessary elaboration. However, the extreme brevity borders on under-specification rather than optimal conciseness.

    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?

    For an authentication tool with no annotations and no output schema, the description is insufficient. It doesn't explain what successful login enables, what the return value contains, or what subsequent operations might be available. The combination of authentication complexity and lack of structured documentation requires more descriptive context than provided.

    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 schema has 100% description coverage with clear parameter documentation ('密码' for password, '学号' for student ID). The description adds no additional parameter semantics beyond what's already in the schema. With high schema coverage, the baseline score of 3 is appropriate as the schema does the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description '登录杭电教务处系统' clearly states the action (login) and target system (Hangzhou Dianzi University academic affairs system), providing a specific verb+resource. However, it doesn't differentiate from the sibling tool 'get_course_schedule' which appears to be a different operation entirely, so it lacks sibling differentiation.

    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 provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites, when authentication is needed, or relationship to the sibling tool. It simply states what the tool does without contextual usage information.

    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 the full burden of behavioral disclosure. While it mentions the login requirement, it doesn't describe what the tool returns (e.g., format, structure), error conditions, or other behavioral traits like rate limits or permissions needed beyond login. This is a significant gap for a tool with no annotation coverage.

    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 in Chinese that conveys the core purpose and key prerequisite without any wasted words. It is appropriately sized and front-loaded with essential information.

    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 the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., schedule format, data structure), which is critical for a 'get' operation. The login prerequisite is helpful, but more behavioral context is needed for adequate completeness.

    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 schema description coverage is 100%, with the parameter 'semester' documented as optional with a default. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline of 3 where the schema does the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose as '获取课程表' (get course schedule), which is a specific verb+resource combination. However, it doesn't differentiate from its sibling tool 'hdu_login' beyond mentioning the login prerequisite, which is more of a usage guideline than a distinction of purpose.

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

    Usage Guidelines4/5

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

    The description explicitly states '需要先登录' (requires login first), providing clear context about prerequisites. However, it doesn't specify when to use this tool versus alternatives or any exclusions, leaving some guidance gaps.

    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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