MCP Project Guard
Server Quality Checklist
Latest release: v1.1.1
- Disambiguation5/5
The two tools address distinct concerns: one analyzes project architecture and language, the other searches for similar code patterns. There is no overlap in purpose, making selection unambiguous.
Naming Consistency5/5Both tools follow a consistent verb_noun pattern (analyze_architecture, find_similar_code) with descriptive, clear names. The naming style is uniform and predictable.
Tool Count3/5With only two tools, the set feels thin for a 'Project Guard' server. According to the calibration, 1-2 tools is borderline; each tool is substantive, but the overall scope appears limited.
Completeness2/5The server lacks core guard functionality such as validation, rule checking, or issue reporting. It only offers architecture analysis and code similarity search, which are helper operations rather than a complete project-guard surface.
Average 3.2/5 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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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It fails to state that the tool is read-only, how 'similar' is determined, or whether there are any side effects. The description is minimal and does not add meaningful behavioral context beyond the basic action.
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 sentence that front-loads the action ('Find existing similar') and clearly states the scope ('in the project'). It contains no extraneous information and is appropriately sized for the tool's simplicity. Every word earns its place.
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?
The tool is relatively simple with three optional parameters and no output schema. The description explains the core purpose but omits details about what the results look like, how the search behaves, or any limitations. For a basic search tool, this is adequate but not comprehensive, leaving the agent without expectations for return format or edge cases.
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 input schema has 100% coverage: each parameter has a description (path, search_term, component_type). The tool description itself adds little beyond the schema, but it does align with the 'component_type' parameter by mentioning 'components, functions, or patterns'. Since the schema already explains parameters, a baseline of 3 is appropriate.
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 function: 'Find existing similar components, functions, or patterns in the project'. It uses a specific verb and resource, effectively differentiating it from the sibling tool 'analyze_architecture' by focusing on finding similar code rather than analyzing architecture. However, it does not explicitly name alternatives or contrasts, so it stops short of a 5.
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 explicit guidance on when to use this tool versus alternatives. It implies usage for locating reusable code but omits any context about scenarios where it would be preferred over 'analyze_architecture' or other approaches. There are no exclusions or prerequisites mentioned.
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 burden of behavioral disclosure. It only states the basic function (analyze, detect, return) without explaining side effects, read-only status, error handling, or what exactly 'validation rules' means. This leaves significant ambiguity for a tool with no annotations.
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 sentence of 12 words, front-loaded with the core action ('Analyze project architecture'). Every word contributes meaning, and there is no redundancy or filler.
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?
The tool has one simple parameter and no output schema or annotations, so the description must carry the full context. It covers the main function but leaves key details unexplained, such as what 'validation rules' are, how they are returned, and any prerequisites or limitations. This makes it adequate but incomplete.
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 already provides a description for the 'path' parameter (100% coverage). Since there is only one parameter and its meaning is fully documented in the schema, the description adds no additional param semantic value. Baseline 3 is appropriate.
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 states a clear verb ('analyze') and resource ('project architecture'), and adds expected outputs (detect language, return validation rules). It does not explicitly distinguish from find_similar_code, but the purpose is unambiguous enough that a 4 is appropriate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies its usage by stating what it does, but it provides no explicit guidance on when to use this tool versus find_similar_code, nor any exclusions or alternative recommendations. This is a case of implied usage.
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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