Spec Score MCP
MCP ServerFreeScore your specs before feeding them to an LLM. A balanced spec produces balanced code. The LLM reads your spec and scores it on 4 axes: completeness, clarity, constraints, and specificity. The tool calculates a balance score, verdict, and generates radar chart visualizations. 3 tools: `spec_score
- Best for
- spec completeness scoring, spec clarity evaluation, spec constraints assessment
- Type
- MCP Server · Free
- Score
- 35/100
- Best alternative
- AWS MCP Servers
- Agent-compatible
- Yes — MCP protocol
Capabilities5 decomposed
spec completeness scoring
Medium confidenceThis capability analyzes the provided specification document to evaluate its completeness by checking for required sections and details. It uses a combination of natural language processing (NLP) techniques to identify missing elements and assess the overall structure of the spec. The scoring is based on predefined criteria that ensure the spec meets necessary standards for effective LLM processing.
Utilizes a custom NLP model tailored for spec completeness assessment rather than generic text analysis, allowing for more relevant scoring.
More focused on specification completeness than general-purpose text analysis tools.
spec clarity evaluation
Medium confidenceThis capability assesses the clarity of the specification by analyzing sentence structure, jargon usage, and overall readability. It employs linguistic analysis techniques to identify complex phrases and suggests simplifications, ensuring that the spec is easily understandable by both technical and non-technical stakeholders. The clarity score is calculated based on readability indices and clarity benchmarks.
Incorporates advanced readability algorithms specifically designed for technical documentation, enhancing clarity assessments beyond standard tools.
More tailored for technical specifications than generic readability checkers.
spec constraints assessment
Medium confidenceThis capability evaluates the constraints outlined in the specification, ensuring they are well-defined and actionable. It uses a rule-based engine to check for logical consistency and completeness of constraints, providing feedback on any ambiguous or vague statements. This helps in refining the constraints to make them more effective for LLM processing.
Employs a custom rule engine that focuses on constraint clarity and consistency, unlike general-purpose text analyzers.
More effective at identifying constraint issues than standard text analysis tools.
spec specificity scoring
Medium confidenceThis capability measures how specific the details in the specification are, using keyword extraction and contextual analysis to identify vague terms and suggest improvements. It quantifies specificity by comparing the language used against a database of best practices for specification writing. This helps ensure that the spec provides clear and actionable guidance for implementation.
Utilizes a specialized keyword extraction algorithm designed for technical specifications, improving specificity assessments over generic tools.
More focused on technical specificity than general keyword analysis tools.
spec visualization generation
Medium confidenceThis capability creates radar chart visualizations based on the scoring metrics of completeness, clarity, constraints, and specificity. It employs a data visualization library to render interactive charts that provide a visual representation of the spec's strengths and weaknesses. This helps users quickly identify areas for improvement and facilitates discussions among stakeholders.
Integrates with a leading data visualization library to produce interactive radar charts, enhancing user engagement compared to static charts.
Offers more interactive visualizations than typical reporting tools.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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English Compiler
Converting markdown specs into functional code
Best For
- ✓developers preparing specs for LLM integration
- ✓teams creating specs for diverse audiences
- ✓developers needing to refine constraints in specs
- ✓teams looking to enhance the precision of their specs
- ✓project managers presenting specs to stakeholders
Known Limitations
- ⚠May not cover all domain-specific completeness criteria
- ⚠Requires well-structured input to yield accurate scores
- ⚠May not fully capture domain-specific terminology nuances
- ⚠Subjective nature of clarity can lead to varying scores
- ⚠Limited to predefined constraint patterns; may miss novel formulations
- ⚠Requires clear context to evaluate constraints accurately
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
Score your specs before feeding them to an LLM. A balanced spec produces balanced code. The LLM reads your spec and scores it on 4 axes: completeness, clarity, constraints, and specificity. The tool calculates a balance score, verdict, and generates radar chart visualizations. 3 tools: `spec_score`, `spec_visualize`, `spec_compare` Install: `npx spec-score-mcp`
Categories
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