Skip to main content
Glama
tarunlnmiit

mcp-autopilot-jobhunt

by tarunlnmiit
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

83%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.4.1

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: scanning jobs, drafting applications, and exporting results. No overlap or ambiguity.

    Naming Consistency5/5

    All tools use a consistent verb_noun pattern with lowercase_snake_case (draft_application, export_jobs, scan_jobs).

    Tool Count4/5

    Three tools is on the lower end but appropriate for a focused job-hunt automation server. Each tool is necessary and well-scoped.

    Completeness4/5

    Covers the core workflow of scanning, drafting, and exporting. Lacks tool for direct submission or managing company config, but these are minor gaps given file-based configuration.

  • Average 3.9/5 across 3 of 3 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 32 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 failing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • View related servers

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.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 provided, so the description carries full burden. It only mentions that it drafts documents and returns a summary of file locations. It does not disclose whether files are overwritten, if the tool is destructive, or any other behavioral traits like data usage or prerequisites.

    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 very concise: one sentence for purpose, plus a minimal Args block. Every sentence adds value, and the important information is front-loaded.

    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?

    For a simple tool with one parameter and an output schema, the description covers the purpose, parameter usage, and return value. It is sufficient for an agent to understand the tool's function and how to invoke it.

    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 description coverage is 0%, but the description adds essential semantics: explains job_ref format (e.g., '#1', '1', or full URL) and its source (last scan). This fully compensates for the lack of schema documentation.

    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 it drafts a resume and cover letter for a specific job. It does not explicitly differentiate from sibling tools (export_jobs, scan_jobs), but the purpose is specific and unambiguous.

    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 usage after scanning jobs (via job_ref from last scan) but provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives like export_jobs or scan_jobs.

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

  • Behavior3/5

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

    Describes output location and return value but lacks details on file naming, overwriting behavior, or performance characteristics. With no annotations, more behavioral context could be beneficial.

    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?

    Concise description starting with the main purpose, followed by parameter details and return value. No unnecessary text.

    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 simplicity (2 params, output schema exists), description adequately explains functionality, though could mention file naming or confirmation of success.

    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?

    Both parameters (min_score and days) are fully explained with default values and behavior, compensating for 0% schema description coverage.

    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?

    Clearly states the tool exports job scan results to a CSV file in the output/ directory, distinguishing it from sibling tools like draft_application and scan_jobs.

    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?

    No guidance on when to use this tool versus alternatives, nor any when-not-to-use conditions.

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

  • Behavior3/5

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

    No annotations are provided, so the description must cover behavioral aspects. It explains that it reads config files and triggers notifications, but it does not state whether the tool modifies any state, has rate limits, or is read-only. More disclosure could improve safety.

    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 two sentences long, with the primary action in the first sentence and supplementary detail in the second. Every word adds value, and there is no redundancy.

    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 tool has zero parameters and an output schema exists, the description is largely complete. It covers the main workflow and dependencies, though it could hint at the output format or error handling.

    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?

    The tool has no parameters, and schema coverage is trivially 100%. The description provides context about config files but does not add parameter semantics since none exist. Baseline score of 3 is appropriate.

    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 scans careers pages, scores jobs against a resume, and sends a Telegram notification. It distinguishes itself from sibling tools (draft_application, export_jobs) by focusing on scanning and alerting.

    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 implies the use case: to discover new job postings and receive notifications. While it doesn't explicitly contrast with siblings, the context is clear enough for an agent to infer when to use this tool.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

autopilot-jobhunt MCP server

Copy to your README.md:

Score Badge

autopilot-jobhunt MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tarunlnmiit/autopilot-jobhunt'

If you have feedback or need assistance with the MCP directory API, please join our Discord server