Fortune MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have completely distinct purposes with no overlap. draw_tarot provides tarot card readings, while get_horoscope calculates astrological horoscopes. An agent would never confuse these tools as they serve different divination methods.
Naming Consistency5/5Both tools follow a consistent verb_noun pattern with clear, descriptive names. draw_tarot and get_horoscope use the same naming convention throughout, making them predictable and easy to understand.
Tool Count2/5With only 2 tools, this server feels quite thin for a fortune-telling domain. While tarot and horoscope are distinct, other common divination methods (like numerology, palm reading, or I Ching) are missing. The scope feels incomplete with just these two offerings.
Completeness2/5For a fortune-telling server, the surface is severely incomplete. While tarot and horoscope are covered, there are significant gaps in the domain - no general fortune-telling, no compatibility readings, no prediction tools, and no way to interpret or combine results from different methods.
Average 3.3/5 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
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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 the full burden of behavioral disclosure. It states the tool draws tarot cards and returns results, implying a read-only operation, but does not address potential behavioral traits such as randomness, rate limits, authentication needs, or side effects. This leaves significant gaps in understanding how the tool behaves beyond its basic function.
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 appropriately sized and front-loaded, with the core purpose stated first, followed by parameter and return details. It uses clear sections ('Args:', 'Returns:') for structure, though the return statement is vague. There is minimal waste, making it efficient for an agent to parse.
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 (one parameter) and the presence of an output schema, the description is somewhat complete but has gaps. It explains the parameter well and states the return purpose, but lacks behavioral context and usage guidelines. The output schema should cover return values, so the description's vagueness on returns is acceptable, but overall it could be more comprehensive for a tool with no annotations.
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 description adds meaningful semantics for the single parameter 'count', explaining it as '引くカードの枚数(1-10、デフォルト: 3)' (number of cards to draw, 1-10, default: 3). This provides context beyond the input schema, which only defines the parameter type and default without describing its purpose or constraints. Since schema description coverage is 0%, the description compensates well for the single parameter.
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: 'タロットカードを引いて占い結果を返します' (Draw tarot cards and return fortune-telling results). It specifies the verb ('draw') and resource ('tarot cards'), though it doesn't explicitly differentiate from the sibling tool 'get_horoscope', which suggests a different fortune-telling method. The purpose is clear but 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 Guidelines2/5Does 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 like 'get_horoscope'. It mentions the tool's function but does not indicate specific contexts, prerequisites, or exclusions for its use, leaving the agent without explicit usage instructions.
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 what the tool does (calculates horoscopes) and describes parameters, it doesn't disclose important behavioral traits like whether this is a read-only operation, what authentication might be needed, rate limits, error conditions, or what format the horoscope result takes. The description is minimal and lacks behavioral context.
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 well-structured and appropriately sized. It starts with the core purpose, then provides a clear Args section with parameter explanations, and ends with Returns information. Every sentence earns its place, though the Japanese-only text might limit accessibility in some contexts. The structure is logical and efficient.
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 that an output schema exists (though not shown), the description doesn't need to explain return values in detail. The description covers the purpose and all parameters comprehensively. For a calculation tool with 4 parameters and no annotations, this description provides good context, though it could benefit from more behavioral information about the calculation process itself.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage (titles only, no descriptions), the description provides excellent parameter semantics. It clearly explains all 4 parameters: datetime_str format and default behavior, random_time purpose, latitude/longitude defaults and reference location. This fully compensates for the schema's lack of descriptions and adds meaningful context beyond the bare schema.
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: 'ホロスコープを計算して返します' (calculates and returns a horoscope). This is a specific verb+resource combination. However, it doesn't explicitly differentiate from the sibling tool 'draw_tarot', which appears to be a different type of divination tool. The purpose is clear but 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 Guidelines2/5Does 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. There's no mention of the sibling tool 'draw_tarot' or any other context for choosing between horoscope calculation and tarot drawing. The only usage information is implicit in the parameter descriptions, but no explicit when/when-not guidance is provided.
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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