dangsaju-mcp
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly distinct purposes: one computes a full Dangsaju reading from birth date/time, while the other looks up static star information by branch or name. No overlap in intended use cases, making misselection unlikely.
Naming Consistency5/5Both tool names share the consistent prefix 'dangsaju_' and use snake_case with descriptive suffixes ('reading', 'star_lookup'). This creates a predictable and uniform naming pattern.
Tool Count3/5With only two tools, the server is minimal, but the specialized domain of Dangsaju fortune-telling makes this count reasonable. It falls on the borderline of being too thin for broader use.
Completeness4/5The core workflow of generating a Dangsaju reading is fully covered, and the lookup tool supports verification and exploration. A minor gap is the lack of a way to list all stars or batch queries, but the existing surface is functional.
Average 4.4/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 8 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.
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, the description carries the full burden. It discloses key behaviors: inclusive counting, default forward direction for both sexes, the variant mode, and that it returns original text without generated interpretation. This is strong coverage. It could be improved by noting that omitting birth_time yields only year/month/day stars, but that detail is present in the schema.
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 and densely packed, with three sentences covering purpose, output, counting/direction rules, and tool type. There is no redundant or filler content, and it is front-loaded with the primary 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 that no output schema exists, the description should clearly explain return values and edge cases. It mentions the four periods and what is returned, but it omits the behavior when birth_time is not provided (hour star excluded, only year/month/day returned), which is an important nuance. This leaves a gap in understanding the tool's full behavior.
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 100%, so the baseline is 3. The description adds meaningful context beyond the schema by explaining the direction parameter's two modes, specifying that sex is required only for the variant, and clarifying the tool's data-only nature. This helps the agent understand how parameters interact.
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 tool's function: it calculates the Dangsaju 12 stars from birth date/time and returns the branches, stars, and original explanation texts for the year, month, day, and hour periods. It also distinguishes itself from the sibling tool by emphasizing it is a data-only tool that does not generate interpretations, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context for use: it specifies the default forward direction and the male-forward/female-backward variant, and warns that it does not produce interpretations or advice, implying it is for raw data retrieval. However, it does not explicitly name the sibling tool or state when to prefer this over it, so it falls short of explicit exclusion guidance.
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 provided, the description discloses key behavioral traits: it returns the full original text '그대로' (as-is), including symbolic, overall, and period-based sections. This gives the agent a clear expectation of the output's scope and nature, though edge cases like invalid inputs or both-parameter behavior are not addressed.
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, well-structured sentence that front-loads the main action and output, then adds context. Every phrase earns its place with no filler or repetition.
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 lack of an output schema, the description lists the output sections (상징, 총평, 시기별 해설) and the intended use case, providing sufficient context for an agent to decide when to invoke. Minor omissions such as the exact return type or error behavior prevent a perfect score, but it is complete for a simple lookup 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 coverage is 100%, so the baseline is 3. The description adds meaningful semantics beyond the schema: it clarifies the 'or' relationship between jiji and star, provides example formats (Hangul and Hanja), and gives a specific example value, which helps correct usage.
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 uses a specific verb ('조회한다') and a specific resource ('별 원문 전체') to clearly state what the tool does. It also differentiates itself as a browsing/testing auxiliary tool, which implies a distinct role from the sibling dangsaju_reading.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly identifies usage contexts: browsing, testing, and SKILL development. It does not list exclusions or directly contrast with the sibling tool, but the auxiliary-tool framing gives clear guidance on when this lookup should be used instead of a full reading.
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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