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ikrigel

mcp-server-cv-modify

by ikrigel
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool addresses a distinct stage in the CV optimization workflow: extracting job data, modifying the CV, and analyzing match. No two tools perform overlapping functions, so there is no ambiguity in selection.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with underscore separators (extract_job_description, modify_cv, analyze_cv_job_match). The naming is clear and predictable, making it easy for an agent to infer each tool's purpose.

    Tool Count5/5

    The server has 3 tools, which is within the typical well-scoped range. Each tool is essential to the CV modification workflow, and the small count reflects a focused design without unnecessary bloat.

    Completeness4/5

    The tools cover the core pipeline from job description extraction to CV modification and match analysis. However, there are minor gaps such as no explicit tool for reverting changes or handling multiple CV formats, which agents might need to work around.

  • Average 3.8/5 across 3 of 3 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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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 must disclose behavioral traits, but it only describes the process ('Parses the CV, extracts keywords... enhances the CV') without revealing side effects, such as whether the original CV is overwritten or a new file is returned. It also lacks details on permissions or irreversibility, which is a significant gap for a mutation tool.

    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, front-loaded sentence with the main action first. It is mostly concise, but the second sentence partially repeats the first ('enhances the CV with relevant keywords') and could be trimmed for even greater clarity.

    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's complexity (6 parameters, no output schema), the description is incomplete. It does not explain what the tool returns (e.g., formatted file, path) or how the outputFormat parameter relates to the result. This leaves the agent uncertain about the tool's end-to-end behavior.

    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 6 parameters are described in the schema, so the description doesn't need to add basic parameter meanings. It does mention keyword extraction and enhancement, which loosely relates to jobKeywords/jobUrl, but doesn't provide additional syntax or semantics beyond the schema.

    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 ('Modifies a CV to better match a job description') with a specific resource (CV) and purpose (strategically emphasizing relevant keywords). This distinguishes it from sibling tools like extract_job_description and analyze_cv_job_match, which perform 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 Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description gives clear context for when to use the tool: to tailor a CV to a job description by emphasizing relevant keywords. However, it does not explicitly name alternatives or state when not to use it, which would earn a 5.

    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 disclose read-only behavior, which it does via 'without modifying it.' It also mentions scoring and suggestion generation. However, it does not describe output format, potential failures, or any security/privacy aspects related to sending CV data.

    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 sentence that is direct and packs essential information (purpose, non-modifying behavior, output type) without filler. It is well-structured and easy to parse.

    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?

    There is no output schema, so the description must explain return values; it mentions scoring and suggestions but not their structure or format. It also does not address potential prerequisites (e.g., job description extraction) or edge cases, making it adequate but incomplete for a tool with no output schema.

    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 described clearly, so the description adds little beyond confirming the analysis outcome. The description does not provide additional detail on parameter formats, constraints, or interdependencies that are not already in the schema.

    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 uses a specific verb ('Analyzes') with a clear resource ('CV') and target ('job description'), and explicitly states it does not modify anything. This distinguishes it from sibling tools like modify_cv, which would alter the CV.

    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 context: it is for analysis only, contrasting with modification. However, it does not explicitly name sibling tools or provide exclusion criteria, though the 'without modifying it' phrase provides clear differentiation.

    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 provided, the description carries the behavioral disclosure burden. It mentions scraping and extracting keywords, which adds context, but it does not disclose potential side effects, such as external network calls, rate limits, or error behavior. The description 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?

    The description is two sentences, front-loaded with the primary action, and contains no unnecessary information. Every word earns its place, making it highly concise and structured.

    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?

    The description is sufficient for a simple extraction tool, but there is no output schema, so it would benefit from outlining return values. It also does not differentiate between sibling tools beyond the basic purpose, leaving some contextual gaps.

    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 provides full descriptions for both parameters (url and extractKeywords), giving a baseline of 3. The description adds context about keywords and skills but does not provide additional meaning beyond what the schema already states.

    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 extracts job descriptions and keywords from a LinkedIn or job posting URL, with a specific verb and resource. It distinguishes itself from sibling tools like modify_cv and analyze_cv_job_match by focusing on extraction from external URLs.

    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 the tool should be used when given a job posting URL to extract information, providing clear context. However, it does not explicitly mention when to use alternatives or when not to use this tool, so it lacks explicit exclusions.

    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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