A fast, secure, and LLM-friendly Model Context Protocol (MCP) server that scrapes job listings from major platforms (LinkedIn, Indeed, Google) and converts them into structured Markdown format.
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Usage analytics
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Average 3.7/5 across 1 of 1 tools scored.
There is only one tool, so there is no possibility of overlapping purposes or confusion. The tool's purpose is clearly stated.
With a single tool, there is no pattern to compare against, but the name itself follows a descriptive verb_noun pattern (fetch_and_format_jobs) and is internally consistent.
One tool feels thin for a server, even though it serves a focused purpose. It falls into the borderline category of 1-2 tools.
The tool covers the primary search-and-format use case, but lacks complementary operations such as retrieving individual job details or filtering results further, representing notable gaps.
offerquest_mcp_fetch_and_format_jobsAInspectSearch for the latest jobs and internships and return them as a structured, LLM-ready Markdown report. Supports multiple titles and locations.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | The target country for the search. | India |
| hours_old | No | Only show jobs posted within this many hours (default 48, max 168). | |
| locations | No | Cities or locations (e.g., 'Delhi, Remote'). | |
| job_titles | No | The roles you are looking for (e.g., 'Python Developer Intern'). | |
| max_results | No | Number of jobs to fetch per source per combo (1-10). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It does reveal the output format (Markdown report) and capability (multiple titles/locations), but it does not mention potential side effects, authentication, rate limits, or whether the operation is read-only. The read-only nature is only implied by 'Search', not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that is front-loaded with the primary action and output. Every word contributes meaning: 'Search for the latest jobs and internships' defines the scope, and 'structured, LLM-ready Markdown report' specifies the output. There is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parameters and no output schema or annotations, the description is somewhat thin. It does not explain how results are organized or grouped, what 'latest' means in terms of hours, or the structure of the Markdown report. While the schema covers parameter details, the report's content format and aggregation behavior are still unclear, making the description only minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides descriptions for all 5 parameters (100% schema description coverage), so the schema already conveys parameter meanings. The description adds no additional parameter-level detail beyond what the schema provides, such as value formats or interactions between parameters, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: searching for latest jobs/internships and returning a structured, LLM-ready Markdown report. It uses specific verbs ('search', 'return') and identifies both the resource (jobs/internships) and output format, making it distinct from any hypothetical sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for job/internship searches but provides no explicit guidance on when to use it versus alternatives, prerequisites, or exclusions. No sibling tools are listed, so the description cannot differentiate, but it also does not offer clear context for choosing this tool over other potential approaches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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