Boundary MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation1/5
The two tools are essentially identical in purpose, both performing structured decision analysis on real-world problems. The only difference is output format (formatted text vs. JSON), which is insufficient to distinguish their core functionality. An agent would likely struggle to choose between them without arbitrary preference for output type.
Naming Consistency5/5Both tools follow a consistent verb_noun naming pattern with clear, descriptive names. 'analyze_decision' and 'get_analysis_json' maintain the same structure and style, making them predictable and easy to understand within the set.
Tool Count2/5With only two tools that essentially duplicate functionality, the count feels too thin for a decision analysis engine. A more complete surface might include tools for different analysis phases (e.g., problem framing, constraint identification, strategy evaluation) rather than just output format variants.
Completeness2/5The server claims to be a 'decision thinking engine' but offers only a single analysis operation in two output formats. There are obvious gaps: no tools for modifying analyses, comparing multiple decisions, tracking decision outcomes, or integrating external data—all expected in a comprehensive decision support system.
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
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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
- Behavior3/5
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. The description states it returns '原始 JSON 数据结构' (raw JSON data structure), which implies a read-only operation that doesn't modify data. However, it doesn't disclose other behavioral traits like error conditions, rate limits, authentication requirements, or what the JSON structure contains. The description adds some context about the return format but lacks comprehensive behavioral details.
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 well-structured with just two sentences. The first sentence states the core purpose, and the second sentence provides differentiation from the sibling tool and usage context. Every sentence earns its place with no wasted words.
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's simple nature (single parameter, no output schema, no annotations), the description is reasonably complete. It clearly explains what the tool does, how it differs from its sibling, and when to use it. However, without an output schema, the description could better explain what the returned JSON structure contains. The lack of annotations means the description should ideally cover more behavioral aspects.
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 one parameter ('problem_text') fully documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in the description.
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 purpose: '获取决策分析的原始 JSON 数据结构' (get the raw JSON data structure for decision analysis). It specifies the verb ('获取' - get) and resource ('决策分析的原始 JSON 数据结构' - raw JSON data structure for decision analysis), and explicitly distinguishes it from its sibling tool 'analyze_decision' by noting it returns structured JSON data instead of formatted text.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus its alternative: '与 analyze_decision 功能相同,但返回结构化的 JSON 数据而非格式化文本。适用于需要程序化处理分析结果的场景。' (Same functionality as analyze_decision, but returns structured JSON data instead of formatted text. Suitable for scenarios requiring programmatic processing of analysis results). This clearly defines the use case and names the alternative tool.
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 carries the full burden of behavioral disclosure. It does an excellent job describing what the tool does (5 specific functions), what it doesn't do (won't provide recommendations), and its philosophical approach ('expose reality structure'). However, it doesn't mention technical behaviors like response format, processing time, or error conditions that would be helpful for an AI agent.
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 perfectly structured and front-loaded with the core purpose, followed by specific functions in a numbered list, and concluding with important limitations. Every sentence earns its place by adding distinct value, with no redundancy or wasted words. The bilingual presentation (Chinese with English translation) is efficiently handled.
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's complexity (5 distinct functions) and absence of both annotations and output schema, the description does an excellent job explaining what the tool does and doesn't do. However, without an output schema, the description doesn't specify what format the analysis results will be returned in, which is a gap for an AI agent trying to use this tool effectively.
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 schema has 100% description coverage for its single parameter, so the baseline is 3. The description adds meaningful context by explaining that the problem_text parameter should contain 'emotional, positional, anxious natural expressions' that will be 'de-emotionalized and restructured.' This provides valuable semantic context beyond the schema's technical description.
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 explicitly states the tool's purpose as 'structured strategy analysis for real-world problems' and clearly distinguishes it from a suggestion generator. It provides a specific verb ('analyze') and resource ('decision problems') while differentiating from the sibling tool get_analysis_json by emphasizing its unique 'expose reality structure' approach rather than generating recommendations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('for structured strategy analysis of real-world problems') and when not to use it ('will not tell you whether to do something'). It clearly distinguishes this tool's purpose from alternatives by stating it's 'not a suggestion generator' and only helps 'see the structure and boundaries of decisions'.
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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