product-spec-mcp

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: acceptance generation, architecture decision, debugging, unified routing, spec compilation, idea interrogation, and UI translation. No two tools overlap in functionality.
Naming Consistency5/5
All tool names follow a consistent noun_verb pattern (e.g., acceptance_generate, architecture_decide, ui_translate), making it easy to predict tool behavior from the name.
Tool Count5/5
With 7 tools, the set is well-scoped for a product specification assistant. Each tool serves a specific, necessary role without unnecessary bloat or duplication.
Completeness4/5
The tools cover the core workflow from idea analysis (spec_interrogate) to spec compilation, acceptance criteria generation, architecture decisions, and UI translation. Missing are update/delete operations for specs, but the set is still comprehensive for an assistant.
Average 3.4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 commits in the last 12 weeks
- Last stable release on June 23, 2026
- 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?
The description does not disclose behavioral traits beyond its purpose. It does not mention whether the decision is deterministic, what the output format is, or any side effects. With no annotations provided, the description carries the full burden but adds minimal behavioral insight.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently conveys the core purpose. It is front-loaded and contains no redundant information, though it could be slightly more informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description does not need to cover return values. However, it omits context about optional parameters and enum options, which would help an agent understand the decision basis better. It is minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage for all parameters. The description simply lists parameter names without adding extra semantics or constraints, so it aligns with the baseline score of 3 for high-coverage schemas.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool determines an architecture plan based on inputs like product type, platform, features, and commercial intent. It uses a specific verb ('judge') and resource ('architecture plan'), but does not explicitly differentiate from sibling tools, though the domain is distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description only states what it does, lacking context for choosing it over siblings like 'acceptance_generate' or 'debug_guide'.
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, description carries full burden. It mentions output type (questions/draft/formal) based on completeness, but does not disclose side effects, authentication needs, or other behaviors beyond generation. No annotation contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is efficient and front-loaded. Could benefit from slightly more structure, but no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description lacks details on decision logic for output type (e.g., what completeness level triggers questions vs draft vs formal). Does not explain parameter roles or readiness score thresholds. Output schema may cover return format, but the core logic is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description adds no extra meaning beyond schema; does not explain how 'answers', 'allow_assumptions', or 'min_readiness_score' affect compilation outcome.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool compiles complete product specs and dev prompt, and outputs questions/draft/formal spec based on completeness. It's specific verb+resource, and distinguishes from siblings like spec_interrogate (which may drill down) and product_spec_assist (which may assist). However, explicit differentiation from siblings is missing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives like product_spec_assist or spec_interrogate. Description only states its function, not context of use.
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. It only states the tool's output ('generate acceptance criteria') without mentioning any side effects, authorization needs, rate limits, or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the main purpose. It is appropriately sized for a simple tool, though it could include more detail without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that the tool has 6 parameters and an output schema, the description is too brief. It does not explain the output format or constraints, leaving the agent with incomplete information for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema adequately documents all parameters. The description adds no additional meaning beyond what is already in the schema, warranting a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: generating acceptance criteria based on product type and features. It distinguishes itself from sibling tools like product_spec_assist or spec_compile by focusing specifically on acceptance criteria.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does 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 does not mention any prerequisites or exclusions, leaving the agent without context for appropriate invocation.
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 responsibility for behavioral transparency. It does not disclose whether the tool modifies state, logs interactions, or what side effects may occur. For an interactive tool, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one sentence with no wasted words. However, it could benefit from a more structured format, such as bullet points for the two key actions (guidance and generation).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's interactive nature and the presence of an output schema (not shown), the description is adequate but not comprehensive. It explains the main goal but lacks detail on how the guidance works or what the structured steps look like.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters have descriptions in the input schema (100% coverage), so the schema already handles parameter semantics. The description adds minimal additional meaning beyond the overall purpose, but does not elaborate on parameter usage or provide examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: it guides users to provide correct error information and generates structured troubleshooting steps. This distinguishes it from sibling tools which focus on acceptance, architecture, product specification, compilation, interrogation, and translation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a user reports an error, but does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. The usage context is clear but lacks depth.
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 only states high-level behavior (scenario detection and routing) without detailing side effects, prerequisites, or what happens in ambiguous cases. This is insufficient for a dispatcher tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the core purpose. Every word is meaningful, and there is no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters, nested objects, and an output schema, the description is too minimal. It does not explain the role of parameters like known_context, preferred_platform, strictness, or auto_execute, nor the nature of the output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, so the parameters are documented. The description adds no additional meaning beyond the schema, which is acceptable here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool as a unified entry point that automatically determines the scenario (product development, UI modification, Debug, launch) and calls the corresponding capability. This distinguishes it from sibling tools, which are specific to individual tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates it is the first contact point for user requests, implying when to use it. However, it does not explicitly state when not to use it or mention alternatives for known scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It states the translation produces front-end terminology and executable prompts but does not disclose behavior with invalid input, completeness guarantees, or processing details. It is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence front-loads the action and resource without any fluff. Every word contributes to conveying the purpose, achieving high conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given only 3 simple string parameters with full schema descriptions and an output schema present, the description provides sufficient high-level context. A minor gap is lack of examples or edge case notes, but overall complete for the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with each parameter having a description (e.g., '用户原始描述' for description). The tool description adds no extra semantic meaning beyond what the schema already provides, meeting baseline for full coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool translates user page modification descriptions into front-end terminology and executable prompts. It uses specific verb 'translate' and specifies resources, distinguishing it from siblings like acceptance_generate or debug_guide which handle different tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for translation tasks but provides no explicit guidance on when to use or not use this tool compared to siblings. No alternatives or exclusions are mentioned, relying on user inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explains the core behavior (analyze, judge sufficiency, output questions) but doesn't specify the output format when info is sufficient, whether it modifies any state, or any authentication or rate limit requirements. With no annotations, the description carries the full burden, and more details would improve transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, directly stating the purpose and output. It is front-loaded and every sentence earns its place with no waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema and 5 params (with 3 enums), the description covers the main behavior. However, it could mention the output structure more explicitly (though output schema exists) and discuss error handling or edge cases for missing info.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds little beyond the schema; it mentions the raw_idea is required but doesn't elaborate on parameter meanings or usage nuances. The schema already provides sufficient descriptions for each parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it analyzes raw ideas or page modifications to determine if info is sufficient for development, and outputs follow-up questions if needed. This distinguishes it from siblings like spec_compile (which likely compiles to a spec) or product_spec_assist (which assists in writing specs).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when user input is vague and needs clarification before development, and notes when info is missing. However, it doesn't explicitly differentiate from siblings or state when not to use this tool versus alternatives.
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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