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girik-chadha

Interviewer MCP

by girik-chadha
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Server Quality Checklist

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

  • Latest release: v0.4.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one for storing job description context, the other for retrieving user progress. No overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun snake_case pattern (set_job_description, get_progress), making them predictable and easy to understand.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for its advertised 'interview' and 'teach' modes. Many expected operations (e.g., conducting an interview, scoring answers) are missing.

    Completeness1/5

    The tool surface lacks any tools for actual interview or teaching interactions. The descriptions refer to modes that would require additional tools not present, leaving critical gaps.

  • Average 4.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 3 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.

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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?

    Annotations declare readOnlyHint=true, so no safety concerns. Description adds value by detailing the returned data components (coverage %, session history, weaknesses). No contradictions, but could mention any additional behavioral aspects.

    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?

    Three sentences, front-loaded with purpose, then usage cases. No wasted words; 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?

    For a read-only tool with one parameter and no output schema, the description adequately covers the returned data and usage contexts. Slightly more detail on return format would be beneficial but not necessary.

    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 description for 'repo' as 'Repo id in owner/repo form'. The tool description does not add further parameter semantics beyond what the schema already provides.

    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?

    Description clearly states the tool retrieves the user's full learning state for a repo, listing specific elements (coverage %, session history, top weaknesses, notes, performance). This is a specific verb+resource combination and distinguishes from sibling set_job_description.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

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

    Explicitly tells when to call it first (at start of returning sessions with trigger phrases) and for generating summaries after interviews. Provides clear context for usage and emphasizes resuming from weak spots.

    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?

    Annotations already indicate idempotent, non-destructive writes. Description adds that data is tied to a repo and persists across modes. Could mention overwrite behavior but idempotent hint covers it. No contradictions.

    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?

    Three sentences: core purpose, when to call, and usage in modes. Front-loaded with key action. Every sentence adds unique value without redundancy.

    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?

    Given full schema descriptions, annotations, and no output schema needed, the description covers storage, usage, and retrieval via get_progress. Adequately complete.

    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 covers all 4 parameters with descriptions. The tool description adds meaningful context about how each parameter is used (e.g., cv_text becomes bootcamp + interview material), going 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 tool stores job description, company, and CV text tied to a repo. It uses specific verbs and distinguishes from sibling get_progress, which retrieves this data.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

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

    Explicitly says to call when the user pastes or describes any of these items. Provides detailed guidance on how the stored data is used in teach and interview modes, and notes that get_progress returns the JD for session awareness.

    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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Glama performs regular codebase and documentation scans to:

  • 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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