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mr-sanjai-offl

Placement Analyzer MCP

by mr-sanjai-offl
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
LOG_LEVELNoLogging levelINFO
LOG_FORMATNoLogging format: 'json' or 'console'console
DATABASE_URLYesAsync database URLpostgresql+asyncpg://...
GITHUB_TOKENNoGitHub API token (optional)
DATABASE_POOL_SIZENoConnection pool size5
MAX_UPLOAD_SIZE_MBNoMax upload file size50

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
analyze_studentA

Run the full analysis pipeline for a student.

Analyzes resume, GitHub, LeetCode, and portfolio in parallel
where possible. Records analysis runs for auditing.

Args:
    student_id: The student's UUID.

Returns:
    Analysis results summary with status for each component.
analyze_resumeA

Analyze a student's resume (PDF text extraction and structuring).

Args:
    student_id: The student's UUID.

Returns:
    Resume analysis results including extracted skills and experience.
analyze_githubA

Analyze a student's public GitHub profile.

Args:
    student_id: The student's UUID.

Returns:
    GitHub analysis including repositories, languages, and activity.
analyze_leetcodeA

Analyze a student's public LeetCode profile.

Args:
    student_id: The student's UUID.

Returns:
    LeetCode analysis including problems solved and topic distribution.
analyze_portfolioA

Analyze a student's portfolio website.

Args:
    student_id: The student's UUID.

Returns:
    Portfolio analysis including projects and technologies found.
get_profile_statisticsA

Get aggregate statistics across all student profiles.

Includes CGPA distribution, skill coverage, project counts,
internship rates, and certification coverage.

Returns:
    Formatted statistics report.
get_skill_distributionA

Get the distribution of skills across the student cohort.

Shows which skills are most common, least common, and
how skills cluster by category.

Returns:
    Skill distribution report.
get_data_quality_reportA

Get a data quality report across all imported student data.

Reports on missing fields, invalid URLs, format issues,
and overall data completeness.

Returns:
    Data quality metrics and recommendations.
validate_excelA

Validate an Excel (.xlsx) file structure without importing data.

Checks file format, detects columns, maps to schema fields,
and reports any ambiguous or missing columns.

Args:
    file_path: Absolute path to the .xlsx file on the server filesystem.

Returns:
    Validation result including detected columns, mapping, and warnings.
import_excelA

Import student data from an Excel (.xlsx) file.

Runs the full pipeline: validate → map columns → normalize →
detect duplicates → persist to database → report statistics.

Args:
    file_path: Absolute path to the .xlsx file on the server filesystem.

Returns:
    Import result with statistics (total, successful, failed, duplicate rows).
get_import_statusA

Get the status and statistics of a data import operation.

Args:
    import_id: The UUID of the import to check.

Returns:
    Import status including row counts and any error details.
get_skill_evidenceA

Get the evidence map for a student's skills.

Shows each skill with its evidence sources (resume, GitHub, projects, etc.)
and evidence strength (declared_only, weak_evidence, strong_evidence).

Args:
    student_id: The student's UUID.

Returns:
    Structured skill evidence report.
get_skill_gapsA

Get skill gaps identified for a student.

Shows missing, weakly evidenced, and declared-but-unverified skills.

Args:
    student_id: The student's UUID.

Returns:
    Skill gap analysis with recommendations.
get_profile_completenessA

Get the profile completeness score for a student.

Returns a percentage score and breakdown by section
(education, skills, projects, resume, GitHub, etc.).

Args:
    student_id: The student's UUID.

Returns:
    Completeness score and section-by-section breakdown.
get_studentA

Retrieve a student's profile by their UUID.

Args:
    student_id: The student's UUID.

Returns:
    Student profile information including name, email, roll number.
search_studentsA

Search for students by name (partial, case-insensitive match).

Args:
    name: Partial name to search for (optional).
    page: Page number (default: 1).
    page_size: Items per page (default: 20, max: 100).

Returns:
    List of matching students with their basic information.
list_studentsA

List all students with pagination.

Args:
    page: Page number (default: 1).
    page_size: Items per page (default: 20, max: 100).

Returns:
    Paginated list of students with total count.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored across 17 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: component analyzers target different platforms, aggregate reports target different metrics, and student queries differ by lookup method. The pipeline analyzer subsumes the individual analyzers without ambiguity, and import/validation/status tools are clearly separated.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern (analyze_*, get_*, import_*, validate_*, search_*, list_*), with no mixed conventions or arbitrary names. This makes the tool surface predictable and easy to navigate.

Tool Count4/5

At 17 tools, the server is slightly on the heavier side but still well-scoped: the import pipeline (validate/import/status), analysis pipeline (full + component analyzers), and reporting tools each serve distinct needs. The count feels appropriate for the domain, though a couple of tools could theoretically be merged.

Completeness4/5

The tool set covers the full workflow: importing data, validating it, running individual component analyses, generating aggregate statistics, and inspecting per-student details like skill evidence, gaps, and completeness. Minor gaps exist (no update/delete for student records, no export tool), but these are peripheral to the analysis-focused purpose.

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources