MCP MeasureMCP Measure

index observed

CoverCraft AI by Ambuj Kumar Tripathi

Live Hugging Face model proofing, ATS resume scoring and career tools by Ambuj Kumar Tripathi

Should I use this

Quality & Safety

A
Description quality
96%
Schema completeness
100%
Naming quality
80%
Poisoning risk
100%
Permission match
100%
Protocol compliance
100%

Findings (1)

  • LOWTool 'cover_letter_generator' description lacks action verbin cover_letter_generator

Based on automated analysis of tool definitions and protocol compliance.

Context Cost

~2,023Tokens (tool definitions)
~2.6 KBTypical response size
Moderate attention impact (1.58% of 128k context)

This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.

Install

One-Click Install

Add this to your `claude_desktop_config.json` file:

{
  "mcpServers": {
    "covercraft-mcp": {
      "url": "https://career-workspace-ambujsystems.vercel.app/api/mcp"
    }
  }
}

Remote endpoints

https://career-workspace-ambujsystems.vercel.app/api/mcpstreamable-http

What it can do

Tool inventory

Tools (8)

🟢 Read-only🟡 Write🔴 Delete⚪ Unknown
🟢company_research(company_name, focus_areas)

Search web for verified company information, tech stack, and engineering culture using Tavily API and grounded synthesis.

Input Schema

{
  "type": "object",
  "properties": {
    "company_name": {
      "type": "string",
      "description": "Target company name (e.g. Stripe, Razorpay)"
    },
    "focus_areas": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Optional focus areas like tech_stack, culture, recent_news"
    }
  },
  "required": [
    "company_name"
  ]
}

Output Schema

{
  "type": "object",
  "properties": {
    "company": {
      "type": "string",
      "description": "Target company name"
    },
    "confidence": {
      "type": "string",
      "description": "Confidence level: HIGH, MEDIUM, LOW"
    },
    "summary": {
      "type": "string",
      "description": "Grounded company summary with source citations"
    },
    "tech_stack": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Verified tech stack"
    },
    "recent_news": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Recent developments"
    },
    "sources_count": {
      "type": "number",
      "description": "Count of verified sources evaluated"
    },
    "sources": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Verified source citations with URLs and titles"
    }
  },
  "required": [
    "company",
    "confidence",
    "summary",
    "sources"
  ]
}
🟢evidence_validator(claims, sources)

Validate factual resume claims against verified web source citations using deterministic Claim Ledger verification. Rejects unsupported claims.

Input Schema

{
  "type": "object",
  "properties": {
    "claims": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "List of candidate claims"
    },
    "sources": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "List of source citations with url and content"
    }
  },
  "required": [
    "claims",
    "sources"
  ]
}

Output Schema

{
  "type": "object",
  "properties": {
    "claim_ledger": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Detailed ledger of verified, partial, and unsupported claims"
    },
    "summary": {
      "type": "object",
      "description": "Summary counts of verified, partial, and unsupported claims"
    },
    "confidence": {
      "type": "number",
      "description": "Mathematical confidence score between 0.0 and 1.0"
    },
    "audit_verdict": {
      "type": "string",
      "description": "Audit conclusion: ALL_CLAIMS_VERIFIED, PARTIAL_EVIDENCE_GAPS, or UNSUPPORTED_CLAIMS_DETECTED"
    }
  },
  "required": [
    "claim_ledger",
    "summary",
    "confidence",
    "audit_verdict"
  ]
}
🟢jd_analyzer(jd_text, resume_text)

Parse target Job Description and match required skills against candidate experience with evidence classification and gap detection.

Input Schema

{
  "type": "object",
  "properties": {
    "jd_text": {
      "type": "string",
      "description": "Job description text"
    },
    "resume_text": {
      "type": "string",
      "description": "Candidate resume text"
    }
  },
  "required": [
    "jd_text",
    "resume_text"
  ]
}

Output Schema

{
  "type": "object",
  "properties": {
    "role_title": {
      "type": "string",
      "description": "Identified target job title"
    },
    "overall_match": {
      "type": "number",
      "description": "Deterministic overall match percentage (0-100)"
    },
    "required_skills": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Extracted required skills with evidence match"
    },
    "chart_data": {
      "type": "object",
      "description": "Categorical breakdown of strong, partial, transferable, and missing skills"
    }
  },
  "required": [
    "role_title",
    "overall_match",
    "required_skills",
    "chart_data"
  ]
}
🟢ats_readiness(letter_text, jd_text)

Perform heuristic ATS compliance checks (keyword coverage, word count, metrics density, parseable formatting, contact info).

Input Schema

{
  "type": "object",
  "properties": {
    "letter_text": {
      "type": "string",
      "description": "Generated cover letter text"
    },
    "jd_text": {
      "type": "string",
      "description": "Target job description text (optional for keyword matching)"
    }
  },
  "required": [
    "letter_text"
  ]
}

Output Schema

{
  "type": "object",
  "properties": {
    "readiness_score": {
      "type": "number",
      "description": "Calculated readiness percentage (0-100)"
    },
    "readiness_level": {
      "type": "string",
      "description": "ATS Readiness Tier: HIGH, MEDIUM, LOW"
    },
    "checks": {
      "type": "object",
      "description": "Individual heuristic evaluation criteria"
    },
    "suggestions": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Specific formatting recommendations"
    }
  },
  "required": [
    "readiness_level",
    "checks",
    "suggestions"
  ]
}
🟢source_filter(sources, company_domain)

Audit and categorize research source domains into authoritative tiers (Tier 1: Official/SEC, Tier 2: Reputable tech press, Tier 3: Aggregators) and discard promotional spam.

Input Schema

{
  "type": "object",
  "properties": {
    "sources": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "List of research sources with url and content"
    },
    "company_domain": {
      "type": "string",
      "description": "Target company primary domain (e.g. stripe.com)"
    }
  },
  "required": [
    "sources"
  ]
}

Output Schema

{
  "type": "object",
  "properties": {
    "filtered_sources": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Categorized source citations"
    },
    "tier_1_authoritative_count": {
      "type": "number",
      "description": "Count of official primary sources"
    },
    "tier_2_reputable_count": {
      "type": "number",
      "description": "Count of reputable secondary sources"
    },
    "audit_verdict": {
      "type": "string",
      "description": "Source quality verdict"
    }
  },
  "required": [
    "filtered_sources",
    "tier_1_authoritative_count",
    "audit_verdict"
  ]
}
🟢cover_letter_generator(candidate_name, role, company, matched_skills, approved_company_facts, ...)

Synthesize structured, evidence-grounded cover letter paragraphs with in-line citations and tone alignment.

Input Schema

{
  "type": "object",
  "properties": {
    "candidate_name": {
      "type": "string",
      "description": "Candidate name"
    },
    "role": {
      "type": "string",
      "description": "Target role title"
    },
    "company": {
      "type": "string",
      "description": "Target company name"
    },
    "matched_skills": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Verified matched skills with evidence"
    },
    "approved_company_facts": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Verified company research facts"
    },
    "tone": {
      "type": "string",
      "description": "Tone style: professional, confident, technical, humble"
    }
  },
  "required": [
    "candidate_name",
    "role",
    "company",
    "matched_skills"
  ]
}

Output Schema

{
  "type": "object",
  "properties": {
    "subject_line": {
      "type": "string",
      "description": "Recommended email subject line"
    },
    "greeting": {
      "type": "string",
      "description": "Salutation"
    },
    "paragraphs": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Structured body paragraphs"
    }
  },
  "required": [
    "subject_line",
    "greeting",
    "paragraphs"
  ]
}
🟢github_proofer(candidate_github_or_resume, technical_claims)

Inspect public GitHub repositories, code languages, commit activity, stars, and forks to fact-check candidate technical claims. Validates evidence-backed claims and flags uncorroborated technical claims.

Input Schema

{
  "type": "object",
  "properties": {
    "candidate_github_or_resume": {
      "type": "string",
      "description": "GitHub username, URL, or resume text containing GitHub link"
    },
    "technical_claims": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "List of technical claims to cross-reference against code repositories"
    }
  },
  "required": [
    "candidate_github_or_resume"
  ]
}

Output Schema

{
  "type": "object",
  "properties": {
    "username": {
      "type": "string",
      "description": "Inspected GitHub handle"
    },
    "profile_url": {
      "type": "string",
      "description": "Public GitHub profile URL"
    },
    "verified_repositories": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Verified repositories matching skills"
    },
    "claim_verifications": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Per-claim verification breakdown"
    },
    "audit_verdict": {
      "type": "string",
      "description": "EVIDENCE_SUPPORTED, PARTIALLY_SUPPORTED, or UNSUPPORTED_CLAIMS_DETECTED"
    }
  },
  "required": [
    "username",
    "profile_url",
    "claim_verifications",
    "audit_verdict"
  ]
}
🟢huggingface_proofer(candidate_hf_or_resume, ai_technical_claims)

Inspect public Hugging Face assets (models, spaces, datasets, lifetime and rolling 30-day downloads) to verify GenAI, LoRA, GGUF, and LLM fine-tuning claims.

Input Schema

{
  "type": "object",
  "properties": {
    "candidate_hf_or_resume": {
      "type": "string",
      "description": "Hugging Face username or resume text"
    },
    "ai_technical_claims": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "AI/ML claims to verify"
    }
  },
  "required": [
    "candidate_hf_or_resume"
  ]
}

Output Schema

{
  "type": "object",
  "properties": {
    "username": {
      "type": "string",
      "description": "Inspected Hugging Face handle"
    },
    "profile_url": {
      "type": "string",
      "description": "Public Hugging Face profile URL"
    },
    "total_models_published": {
      "type": "number",
      "description": "Number of public models published"
    },
    "aggregate_downloads_last_30_days": {
      "type": "number",
      "description": "Rolling 30-day downloads"
    },
    "aggregate_downloads_all_time": {
      "type": "number",
      "description": "Lifetime downloads across all models"
    },
    "verified_models": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Published models with weights and metrics"
    },
    "claim_verifications": {
      "type": "array",
      "items": {
        "type": "object"
      },
      "description": "Per-claim verification breakdown"
    },
    "audit_verdict": {
      "type": "string",
      "description": "GenAI competency audit verdict"
    }
  },
  "required": [
    "username",
    "profile_url",
    "aggregate_downloads_all_time",
    "audit_verdict"
  ]
}

Community

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Evidence

Recent observations

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