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justomsharma

github-resume-assistant

by justomsharma
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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: v0.1.1

  • Disambiguation5/5

    Each tool serves a distinct purpose: fetching repos, analyzing resume claims, and suggesting projects. No overlap or ambiguity.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern with snake_case (analyze_resume, fetch_github_repos, suggest_projects).

    Tool Count5/5

    With 3 tools, the server is well-scoped for its purpose—fetching data, diagnosing gaps, and prescribing projects. No unnecessary tools.

    Completeness5/5

    The tool set covers the full workflow: retrieving GitHub data, analyzing resume claims, and suggesting projects. No obvious missing functionality.

  • Average 4.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 27 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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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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

  • Behavior4/5

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

    The description discloses key behaviors: it extracts strong claims, cross-references against public repos, and returns a gap report. It also gracefully handles empty GitHub profiles, framing it as a gap. Since no annotations are provided, the description bears full burden and does so well.

    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 clear opening sentence, followed by a concise elaboration of the analysis process and edge case handling. No redundant information, and every sentence adds 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 the presence of an output schema (likely defining the gap report), the description provides sufficient context for typical use. It covers edge cases (empty GitHub) and the overall process. Missing details like input format requirements or size limits, but these are minor given the schema.

    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?

    The input schema has 0% description coverage, but the description explains both parameters: 'resume_text' as the full text of the resume, and 'username' as the GitHub login. This adds meaningful context beyond the bare schema property names.

    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: 'Find which resume claims a GitHub profile does and doesn't back up.' It specifies the action (find, cross-reference) and resource (resume claims vs. GitHub profile). This differentiates it from siblings like fetch_github_repos and suggest_projects, which have distinct functions.

    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 implies the tool is used to analyze resume claims against GitHub profiles but does not explicitly state when to use it versus alternatives. No exclusions or 'when not to use' guidance is provided, relying on implied context.

    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?

    Given no annotations, the description carries full burden. It explains the tool's process (builds gap report, prescribes projects) and edge case handling (empty GitHub). It does not mention side effects or limitations, but for a suggestion tool this is adequate.

    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, starting with the core purpose and then elaborating on process and edge cases. Every sentence adds value.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    With an output schema present (so return values are covered elsewhere), the description covers the tool's purpose, parameters, edge cases, and output nature (ranked plan with sized/scoped projects). It is complete for a 2-parameter 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?

    The description explains the role of each parameter ('resume_text' grounds suggestions, 'username' grounds analysis) beyond what the schema provides. Since schema coverage is 0%, this adds necessary meaning.

    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 that the tool prescribes a ranked 30-day plan of projects to make a resume credible. It distinguishes itself from sibling tools (analyze_resume and fetch_github_repos) by being the 'prescription, not just the diagnosis.'

    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 good usage context, indicating it should be used after diagnosis and highlighting that it handles empty GitHub profiles. However, it does not explicitly state when not to use it or name alternative tools directly.

    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?

    Discloses return data (profile + repos, stars, language, dates) and edge case handling, despite 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two sentences plus Args section, no extraneous words, front-loaded purpose.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    With output schema present, description adequately covers return values and edge cases; complete for a simple fetch tool.

    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?

    Schema has 0% coverage, but description includes detailed param documentation with example ('octocat').

    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?

    Clear verb ('Fetch'), resource ('GitHub user's public profile and repositories'), and no sibling ambiguity.

    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?

    Explicit use case ('ground resume advice') and graceful handling of empty repos, but no explicit when-not or alternatives (none needed).

    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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