MCP Lottery Demo
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: draw_lottery selects from a list of options, flip_coin returns heads or tails, and roll_dice handles dice with customizable faces and counts. There is no overlap or ambiguity between these random selection mechanisms.
Naming Consistency5/5All tools follow a consistent verb_noun pattern (draw_lottery, flip_coin, roll_dice) with clear, descriptive names that accurately reflect their functions. The naming is uniform and predictable throughout the set.
Tool Count5/5With 3 tools, the server is well-scoped for a lottery/gambling demo, covering common random selection scenarios (lottery draws, coin flips, dice rolls). Each tool earns its place without feeling excessive or insufficient for the domain.
Completeness5/5The tool set provides complete coverage for basic random selection operations in a gambling context: drawing from options, binary coin flips, and customizable dice rolls. There are no obvious gaps for the stated demo purpose, and agents can handle typical use cases without dead ends.
Average 3/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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
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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. It states the tool rolls dice with customizable parameters but doesn't describe output format, randomness characteristics, error conditions, or any side effects. For a tool with no annotation coverage, this leaves significant behavioral gaps.
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 extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place with no redundant information or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (individual rolls, sum, distribution), error handling, or practical usage context. For a randomization tool with two parameters, more behavioral context would be helpful.
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 has 100% description coverage with clear parameter documentation, so the baseline is 3. The description adds minimal value beyond the schema by mentioning customizability of sides and count, but doesn't provide additional semantic context about parameter usage or constraints.
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 action ('投掷骰子' - roll dice) and specifies key capabilities ('支持自定义面数和数量' - supports custom number of sides and quantity). It distinguishes from siblings by focusing on dice rather than lottery or coin operations, though it doesn't explicitly contrast with them.
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 the sibling tools (draw_lottery, flip_coin). It mentions what the tool does but offers no context about appropriate use cases, alternatives, or 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 full burden for behavioral disclosure. While it mentions the random drawing behavior, it doesn't address important aspects like whether this is a read-only operation, if it has side effects, error conditions, or performance characteristics. For a tool with no annotations, this leaves significant behavioral gaps.
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 extremely concise - a single sentence that directly states the tool's core functionality. There's no wasted language, repetition, or unnecessary elaboration. It's front-loaded with the essential information.
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 moderate complexity (random selection with configurable parameters) and no output schema, the description is minimally adequate but incomplete. It covers the basic purpose but lacks information about return values, error conditions, and behavioral context. With no annotations and no output schema, the description should provide more complete guidance for effective tool use.
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?
With 100% schema description coverage, the schema already documents all three parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions '选项列表' (option list) which corresponds to the 'options' parameter, but provides no additional context about parameter usage, constraints, or interactions.
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: '从给定的选项列表中随机抽取一个或多个结果' (randomly draw one or more results from a given list of options). It specifies the verb ('抽取' - draw) and resource ('选项列表' - list of options), but doesn't explicitly distinguish it from sibling tools like flip_coin or roll_dice, which prevents a perfect score.
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 its siblings (flip_coin, roll_dice). It doesn't mention alternatives, prerequisites, or specific contexts where this tool is appropriate versus others. The description only states what the tool does, not when 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 the full burden. It states the basic behavior (flip coin, return heads/tails) but lacks details like whether it's deterministic, if there are rate limits, error conditions, or how multiple flips (with count parameter) are handled. For a tool with no annotations, this is minimal disclosure.
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 extremely concise and front-loaded: '抛硬币,返回正面或反面' (flip a coin, return heads or tails). It uses minimal words to convey the core purpose without any wasted sentences, making it efficient and easy to understand.
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 random generation), no annotations, no output schema, and a single parameter with full schema coverage, the description is adequate but basic. It covers the main action and outcome, but lacks details on behavior for multiple flips or error handling, which could be useful for completeness in this context.
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 input schema has 100% description coverage, with the 'count' parameter documented as '抛硬币次数,默认为1' (number of coin flips, default is 1). The description doesn't add any parameter semantics beyond this, as it doesn't mention parameters at all. With high schema coverage, the baseline score of 3 is appropriate.
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: '抛硬币,返回正面或反面' (flip a coin, return heads or tails). It specifies the action (flip coin) and the outcome (return heads/tails), which is straightforward. However, it doesn't explicitly differentiate from sibling tools like 'draw_lottery' or 'roll_dice', though the action is distinct enough to imply difference.
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 sibling tools or any context for choosing flip_coin over draw_lottery or roll_dice, such as for binary outcomes or simple random selection. This lack of comparative guidance limits its utility in a multi-tool environment.
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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