opendata-campus-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool serves a clearly distinct purpose: browsing resources on a specific platform, discovering available platforms, and reading a single resource's details. There is no overlap.
Naming Consistency5/5All tools follow a consistent verb_noun pattern with snake_case: browse_education_source, discover_education_sources, read_education_resource.
Tool Count4/5Three tools is on the low side but appropriate for the focused scope of navigating educational open data sources from TWCampus. Could be slightly expanded, but current count is reasonable.
Completeness4/5The set covers the basic workflow: discover sources, browse resources within a source, and read details of a resource. Minor gaps like aggregating searches across sources or listing all resources from a source without query are absent but not critical.
Average 4.1/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 19 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that results are fetched from TWCampus directory, prioritizes local directory, and clarifies TWCampus is not a resource warehouse but a router. No side effects mentioned, but read-only nature is implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences with no redundancy. Front-loads purpose, then provides parameter details and behavioral notes efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters (simple types), no enums, and an output schema (not shown), the description covers core purpose, return fields, and parameter hints. Misses explanation for subject param and error handling, but overall adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must compensate. It explains education_stage possibilities, implies query is a search term, and mentions max_sources limit. However, subject parameter is not explained, leaving ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool discovers official education platforms from TWCampus directory, up to max_sources. It specifies the return fields (name, official_url, categories, directory_source) and distinguishes itself from siblings (browse_education_source, read_education_resource) by focusing on discovery and routing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides possible values for education_stage (國小, 國中, 高中, 大學, 技職, 全階段) and notes TWCampus is only a routing directory, implying when to use. However, it lacks explicit when-not-to-use guidance or alternatives beyond the sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses key behaviors: returns a results list with specific fields, max_results upper limit of 5, and explicitly states it will not attempt login or bypass access controls. This is comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, containing only relevant sentences without fluff. It uses a bullet-like format for clarity, making it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 3 parameters, 2 required, an output schema (so return values are covered), and no annotations. The description covers constraints (max_results limit, no auth) and output structure. It could be more complete by noting error handling or source validation, but overall sufficient for operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It explains 'source' with an example (platform name or URL) but does not clarify the 'query' parameter beyond its type. 'max_results' is mentioned as having an upper limit of 5, but no additional semantic detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (搜尋/search) and resource (學習資源/learning resources) on official education platforms. It distinguishes from siblings: discover_education_sources (discovering sources) and read_education_resource (reading a specific resource).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a constraint (no login or bypass access control) implying use only for public resources. However, it lacks explicit guidance on when to use this tool versus its siblings (discover_education_sources, read_education_resource).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It explicitly states the tool does not attempt to log in or bypass access controls, returns a summary of up to 500 characters, and does not store full text. This provides good behavioral insight, though lacks details like error handling or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with the main purpose, and uses bullet-like enumeration for parameters and behavior. Every sentence is substantive with no superfluous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown), the description appropriately covers the key aspects: input parameters, return object fields, and access behavior. It could mention potential error scenarios but is largely complete for a simple read tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add value. It explains the 'extract' parameter with allowed field values (title, summary, publisher, etc.) and notes summary length limit. This adds substantial meaning beyond the schema's bare definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool reads key information (summary, not full text) from a single public education resource page. It specifies that it is for reading, distinguishing it from browsing or discovering resources. The verb and resource are specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for reading a specific page's info but does not explicitly compare with sibling tools (browse_education_source, discover_education_sources) or provide when-not-to-use guidance. Usage context is inferred, not stated.
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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- Evaluate tool definition quality.
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