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Ritesh-sudo

Job Search MCP Server

by Ritesh-sudo
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TypeScript
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation2/5

    The two tools have overlapping purposes, as both are for searching jobs, with one being a general AI/ML search and the other for specific sites. This creates ambiguity—an agent might struggle to choose between them when searching on a particular site, as the descriptions don't clearly delineate when to use each tool.

    Naming Consistency4/5

    Both tools follow a consistent snake_case naming pattern with 'search' as the verb, which is predictable and readable. However, the slight inconsistency in naming (e.g., 'search_ai_ml_jobs' vs. 'search_specific_job_site') is minor, as they maintain a clear verb_noun structure.

    Tool Count2/5

    With only 2 tools, the server feels thin for a job search domain, lacking essential operations like filtering, applying, or managing job listings. This limited scope suggests the tool set is underdeveloped and may not support comprehensive agent workflows.

    Completeness2/5

    The tool set is severely incomplete for job searching, missing critical functions such as filtering by location or salary, applying to jobs, saving listings, or tracking applications. This will likely cause agent failures in handling typical job search tasks beyond basic searches.

  • Average 2.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits 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

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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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions searching 'across multiple job sites', which hints at aggregation behavior, but fails to detail critical aspects like rate limits, authentication needs, result format, pagination, or error handling for a search tool.

    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 a single, efficient sentence that front-loads the core purpose without unnecessary elaboration. Every word contributes directly to understanding the tool's function, making it highly concise and well-structured.

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

    Completeness2/5

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

    Given the complexity of a search tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It lacks details on behavioral traits, result format, and usage guidelines, leaving significant gaps for an AI agent to operate effectively.

    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 description coverage is 100%, providing full parameter documentation. The description adds no additional semantic context beyond the schema, such as explaining interactions between parameters (e.g., how 'keywords' refine searches). Baseline 3 is appropriate as the schema handles the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb 'search' and the resource 'AI/ML internships and full-time roles across multiple job sites', making the purpose evident. However, it doesn't explicitly differentiate from the sibling tool 'search_specific_job_site', which might handle single-site searches versus this multi-site approach.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives, such as the sibling 'search_specific_job_site'. It lacks context on prerequisites, exclusions, or comparative scenarios, leaving the agent without clear usage direction.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden. It states the action is a search but doesn't disclose behavioral traits like whether it's read-only, what permissions are needed, rate limits, or what the output looks like (e.g., list of jobs with details). This leaves significant gaps for a tool with parameters.

    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 a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the core purpose without unnecessary elaboration.

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

    Completeness2/5

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

    Given no annotations, no output schema, and a tool with 3 parameters, the description is incomplete. It doesn't explain what the search returns (e.g., job listings, error handling) or behavioral aspects, making it inadequate for proper agent usage despite good conciseness.

    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 description coverage is 100%, so the schema already documents all parameters (site, location, maxResults) with descriptions and defaults. The description adds no additional meaning beyond implying a search context, which is minimal value. Baseline 3 is appropriate when schema does the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Search for jobs') and resource ('on a specific job site'), making the purpose understandable. However, it doesn't differentiate from the sibling tool 'search_ai_ml_jobs' (which presumably searches for AI/ML jobs rather than by site), so it lacks explicit sibling distinction.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives like 'search_ai_ml_jobs'. It mentions 'specific job site' but doesn't explain when to choose this over other search methods or what contexts it's best suited for.

    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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