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Shelpuk-AI-Technology-Consulting

Lad MCP Server

by Shelpuk-AI-Technology-Consulting
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Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.4.4

  • Disambiguation5/5

    The two tools, code_review and system_design_review, target distinctly different domains: code snippets/diffs vs. system design proposals. There is no overlap in their purpose, making disambiguation straightforward.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern using snake_case (code_review, system_design_review). The naming is predictable and clear.

    Tool Count3/5

    With only two tools, the server is quite minimal. While the tools are specific and well-defined, the narrow scope may feel thin for a general development assistant, but it is not excessively small for a focused purpose.

    Completeness3/5

    The server covers two common review types (code and system design) but lacks additional reviews like architecture, security, or documentation. The tools accept file paths, suggesting some flexibility, but the surface is notably incomplete for comprehensive review workflows.

  • Average 3.9/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 2 of 2 community issues answered or closed in the last 6 months
    • 25 commits in the last 12 weeks
    • No stable releases found
    • 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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?

    No annotations are provided, so the description must fully disclose behavioral traits. It mentions parallel reviewers but lacks details on determinism, error handling, rate limits, or what happens to existing data. This is a significant gap for a tool running multiple LLM calls.

    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 consists of two concise sentences, with the purpose front-loaded in the first sentence. No redundant information is present; every sentence earns its place.

    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 is somewhat complete but could mention that the output includes review comments from both reviewers. It lacks guidance on when to use this tool and missing behavioral details, but overall it is adequate for a simple tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, and each parameter has detailed descriptions explaining formats and constraints. The description adds value by explicitly stating the mutual exclusivity condition (at least one required), which is already implied but reinforced.

    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 action ('Review'), the resource ('a code snippet or diff'), and the method ('using two LLM reviewers in parallel'). It distinguishes from the sibling tool 'system_design_review', which focuses on a different domain.

    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 mentions the prerequisite that at least one of 'code' or 'paths' must be provided, but it does not explicitly state when to use this tool versus alternatives, nor does it provide any exclusion criteria. The implied usage is clear, but guidance is minimal.

    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?

    No annotations are provided, so the description must carry the burden. It discloses the use of two parallel reviewers, which is a behavioral trait. It does not explicitly state that the tool is read-only or non-destructive, but the context of a review implies that.

    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, front-loaded with the core purpose, and contains no fluff. Every word provides value.

    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 4 parameters, an output schema exists, and a sibling tool, the description is fairly complete. It explains the parallel review mechanism and the requirement for input. Minor gap: no guidance on when to use this versus 'code_review'.

    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 minimal parameter information beyond the schema, only reiterating the condition for 'proposal' and 'paths'. The schema descriptions themselves are thorough.

    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 verb 'review' and the resource 'system design proposal and constraints', with a distinctive behavioral detail 'using two LLM reviewers in parallel'. It implicitly differentiates from sibling 'code_review' by focusing on system design.

    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 explicitly states the prerequisite that at least one of 'proposal' or 'paths' must be provided. However, it does not provide guidance on when to use this tool versus the sibling 'code_review', though the tool name suggests the domain.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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