mcp-toolkittest
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have completely distinct purposes: get_current_time retrieves temporal data, while number_add performs arithmetic addition. There is no overlap in functionality, making it impossible for an agent to confuse them.
Naming Consistency4/5Both tools follow a verb_noun pattern (get_current_time, number_add), which is consistent and readable. However, the verb styles differ slightly ('get' vs. 'number'), but this minor deviation does not significantly impact clarity.
Tool Count2/5With only 2 tools, this server feels thin for a 'toolkit' purpose implied by the name 'mcp-toolkittest'. It lacks breadth and depth, making it borderline for practical use, though not extreme like a single tool.
Completeness2/5The server's domain is unclear from the tool set, but as a basic utility toolkit, it has significant gaps. It only covers time retrieval and addition, missing common operations like subtraction, multiplication, or other utility functions, which limits agent effectiveness.
Average 3.2/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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 are provided, so the description carries the full burden. It states the tool returns the current date and time, which implies it's a read-only operation with no side effects, but it doesn't explicitly confirm this or disclose other behavioral traits like timezone handling, format, or potential delays. For a 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in Chinese that directly states the tool's purpose. It's front-loaded with no wasted words, making it highly concise and well-structured for its simple function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on return format or behavioral context. Without annotations or output schema, the description should ideally provide more completeness, but it meets the bare minimum for such a simple 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?
The tool has 0 parameters, and the schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so a baseline of 4 is appropriate as it doesn't introduce confusion or redundancy.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description '返回当前的日期和时间' (returns the current date and time) clearly states the tool's purpose with a specific verb ('返回') and resource ('当前的日期和时间'). It's not tautological with the name 'get_current_time' since it provides the Chinese translation. However, it doesn't explicitly distinguish from the sibling tool 'number_add', though that distinction is obvious given their different domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. It doesn't mention any context, prerequisites, or exclusions. While the tool's purpose is simple and the sibling 'number_add' is unrelated, the description itself offers no usage guidelines.
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 for behavioral disclosure. It states the tool performs addition but doesn't mention error handling (e.g., for non-numeric inputs), performance characteristics, rate limits, or output format. For a simple tool with no annotations, this leaves gaps in understanding how it behaves beyond the basic operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a single sentence that directly states the tool's purpose and usage. It's front-loaded with the core function. However, it could be slightly more structured by separating purpose from usage, but it efficiently communicates the essential information without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (simple addition with two parameters), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and usage but lacks details on output format, error cases, or behavioral traits. For such a simple tool, this might be sufficient, but it doesn't provide a complete picture for an AI agent to handle edge cases confidently.
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?
The schema description coverage is 100%, with clear descriptions for both parameters ('第一个数字' and '第二个数字'). The description adds no additional parameter semantics beyond what the schema provides, such as constraints or examples. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as '简单的求和工具' (simple summation tool) and specifies it calculates the sum of two numbers. It distinguishes from the sibling tool 'get_current_time' by focusing on arithmetic calculation rather than time retrieval. However, it doesn't explicitly differentiate from potential other mathematical tools beyond the sibling context.
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 implied usage guidance with '当想要计算两个数字相加后的结果时调用' (call when you want to calculate the sum of two numbers), which gives basic context. However, it doesn't offer explicit alternatives, exclusions, or when-not-to-use guidance. The sibling tool 'get_current_time' is unrelated, so no comparison is needed, but the description lacks broader usage context.
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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