ardhi-mcp
Server Quality Checklist
Latest release: v0.1.4
- Disambiguation5/5
With only one tool, there is no possibility of confusion between tools. The single tool is clearly distinguishable.
Naming Consistency5/5There is only one tool, so naming conventions are trivially consistent. The name 'subdivision_process' follows a clear noun_noun pattern.
Tool Count2/5A single tool for a land subdivision process is too few, even for a demo. The server's scope likely requires multiple tools for a complete workflow, but only one is provided, making it feel incomplete.
Completeness1/5The tool set is severely incomplete. A land subdivision application process typically involves multiple steps and operations (e.g., submitting documents, checking status, approvals), but only one tool covering a vague 'process' is provided, leaving obvious gaps.
Average 2.9/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 39 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so this is known to be a safe read operation. The description adds 'DEMO,' hinting at limited or non-production data, which provides some extra context beyond annotations. No other behavioral traits (e.g., data scope, return format) are disclosed, but the annotations carry most of the burden here.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (just 8 words), which is efficient for a simple tool. However, it lacks structure (no sentences, just phrases). For a tool with only 2 parameters and clear schema, this brevity is acceptable and earns a high score.
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 the simple schema (2 optional string params) and output schema existence (though not shown), the description is minimal but covers the core topic. The 'DEMO' note adds context about reliability. For a simple read-only tool, this is just adequate, but it could mention what the output represents.
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?
Schema coverage is 100%; both parameters (county, purpose) have descriptions in the schema itself. The tool description adds no additional meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Land subdivision application process in Kenya. DEMO.' This gives a general topic but lacks an action verb (e.g., 'get' or 'search') and doesn't specify what the tool returns (e.g., applications, statuses). It barely distinguishes itself from potential siblings, but since there are none, it's somewhat acceptable for a vague purpose.
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?
No usage guidelines are provided. The description does not mention when to use this tool, prerequisites, or alternatives. With zero guidance, an AI agent must infer usage solely from the name and vague description, which is insufficient for clear decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
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- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
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