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xiangwang331-boop

resume-mcp

by xiangwang331-boop
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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: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one optimizes an existing resume, the other generates a new one from scratch. There is no overlap or ambiguity in their scopes.

    Naming Consistency5/5

    Both tool names follow the same verb_noun pattern (optimize_resume, generate_resume), making them predictable and consistent.

    Tool Count4/5

    With only two tools, the server is on the low end of the typical range, but the tools cover the two primary resume workflows (create and optimize). The count is slightly under but reasonable for the focused purpose.

    Completeness5/5

    The tool set covers the full lifecycle from generating a resume from raw background to optimizing an existing one. There are no obvious missing operations for the stated domain.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • 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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden. It discloses the return format (generation explanation, structured JSON, absolute paths) and the default behavior of output_dir, but it does not mention whether files are overwritten, if directories are created, or any required permissions. There is room for more transparency, but the core behavior is reasonably clear.

    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 concise and well-structured: a lead sentence stating the purpose, a bulleted list of parameters with clear semantics, and a final line describing the return values. It is front-loaded with the purpose, and every sentence contributes meaningful information without unnecessary verbosity.

    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?

    The tool has an output schema, but the description still provides adequate context about the generation process and return values. It covers the two required parameters and their defaults, and explains the output_dir behavior. Minor gaps exist (e.g., file overwriting behavior), but for a resume-generation tool, the description is sufficiently complete for an agent to understand the expected flow.

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

    Parameters5/5

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

    Since schema description coverage is 0%, the description fully compensates by explaining each parameter's meaning, including the type of content expected for background, the purpose of job_position, and the optional nature of requirements and output_dir with its default behavior. This adds significant value beyond the raw 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 tool's purpose: to generate a resume from scratch (从零生成) targeting a specific job position, based on scattered background info. This verb+resource+scope phrasing distinguishes it from the sibling tool optimize_resume, which presumably enhances an existing resume.

    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 when to use this tool: when you have scattered background info and need a new resume for a target position. It does not explicitly name optimize_resume as an alternative for existing resumes, so the differentiation is slightly implicit, but the context is clear enough for an AI agent to infer the appropriate use case.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries the transparency burden. It discloses the input (resume file path), processing (optimization for job position), and outputs (optimization description, structured JSON, generated .docx/.pdf paths). It does not explicitly state whether the original file is modified, but the mention of 'generated' files suggests non-destructive behavior.

    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 efficiently structured: a one-sentence purpose, a bulleted parameter list, and a one-sentence return summary. Every line adds value, and the structure makes it easy to scan. No redundant or filler content.

    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?

    The description is complete for a tool with 4 parameters and an output schema: it covers purpose, all parameters, and return values. It could add explicit notes about side-effects (e.g., whether the input file is overwritten) or error conditions, but these are likely covered by the output schema and the tool's non-destructive design.

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

    Parameters5/5

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

    The input schema has no parameter descriptions (0% coverage), so the description must compensate. It does so comprehensively: each of the 4 parameters is explained with types, defaults, and an example for job_position. This fully covers the semantic meaning beyond the schema's bare titles.

    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's function: 'Optimize an existing resume based on target job position' (translated). The verb 'optimize' with the resource 'resume' is specific, and it distinguishes itself from the sibling tool 'generate_resume' by explicitly targeting existing resumes.

    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 provides clear context by noting the tool works on an existing resume (PDF/.docx/.doc) and includes parameter details for target position and optional requirements. However, it does not explicitly mention when not to use it or name the sibling tool 'generate_resume' as the alternative for creating new resumes, though this is strongly implied by the wording.

    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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