Skip to main content
Glama
Ashishkosana

jobs-mcp

by Ashishkosana
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools serve clearly distinct purposes: one lists data sources and the other searches for jobs. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow the same verb_noun snake_case pattern: list_sources and search_jobs. This is perfectly consistent and predictable.

    Tool Count3/5

    With only two tools, the server feels minimal, but the scope is very narrow—job searching and source listing. This is borderline, as the rule suggests 1-2 tools feels thin, yet each tool serves a necessary role.

    Completeness5/5

    The server fully covers its stated purpose: list the job sources and search live US software-engineering job openings. There are no obvious missing operations for this narrow domain.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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.

  • Add related servers to improve discoverability.

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

  • Behavior3/5

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

    The description only says 'List', which implies a read-only operation, but with no annotations, it provides no explicit safety guarantees or output details. It doesn't mention whether the response includes full details or just names, nor any rate limits or auth. For a simple list, this is adequate but lacks added behavioral transparency.

    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 entire description is one sentence, 12 words, front-loaded with the verb and resource. It contains no filler and every word 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?

    Given the tool's simplicity (no parameters, no output schema, no annotations), the description sufficiently explains what it lists and the categories. It doesn't overpromise or leave major gaps. A slightly more detailed note about the return structure could push it higher, but for a listing tool, this is adequate.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has zero parameters, so the description doesn't need to explain parameters. The baseline for 0 params is 4, and the description adds context about source categories, which is helpful.

    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 identifies the action ('List') and the specific resource ('job sources'), with an added parenthetical defining what those sources include ('ATS boards + community feed'). This distinguishes it from the sibling tool 'search_jobs', which searches jobs rather than listing sources.

    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: when an agent needs to know the server's configured job sources. It doesn't explicitly name the sibling alternative, but the purpose itself differentiates it from 'search_jobs'. Because there are no exclusions or prerequisites, it falls at 'clear context, no exclusions'.

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

  • Behavior5/5

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

    With no annotations provided, the description carries the full burden. It discloses behavior: pulls from specific ATS boards, filters to US roles, excludes security-clearance/US-citizenship-required postings, de-duplicates, and returns newest first. This is detailed behavioral transparency.

    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 structured with a summary line, behavioral details, and an Args section. Every sentence adds value, and the arg descriptions are clear and brief. No waste.

    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 3 optional params and an output schema, the description covers return structure ('list of {company, title, location, url, posted, source}'), parameter semantics, and behavioral filters/ordering. It leaves little ambiguity for a search tool.

    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 coverage is 0%, but the description's Args section extensively explains each parameter: query semantics ('words that must all appear'), location format ('us' or substring), limit range (1-100), including examples. This fully compensates for the schema gap.

    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 opens with a specific verb+resource: 'Search live US software-engineering job openings.' It further specifies data sources (Greenhouse/Lever/Ashby, community feed) and exclusions, clearly distinguishing it from the sibling tool list_sources.

    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 provides clear context for when to use the tool: to find current US software-engineering jobs matching query/location. It does not explicitly mention alternatives or when-not-to-use, but the context is enough for a search 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

jobs-mcp MCP server

Copy to your README.md:

Score Badge

jobs-mcp 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/Ashishkosana/jobs-mcp'

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