offer-quest mcp
Server Details
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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Score is being calculated. Check back soon.
Available Tools
1 toolofferquest_mcp_fetch_and_format_jobsAInspect
Search 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). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It successfully discloses the output format (Markdown report) and implies recency ('latest'), but omits operational details like external API calls, rate limits, caching behavior, or error handling that would be necessary for full transparency.
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?
Two efficiently structured sentences with zero waste: the first front-loads the core purpose and output format, while the second adds capability context. Every word earns its place.
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?
Given the 5 optional parameters with complete schema coverage and no output schema, the description adequately compensates by describing the return format ('Markdown report'). It could be improved by mentioning empty result behavior or data freshness guarantees.
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?
Schema coverage is 100%, establishing a baseline of 3. The description adds value by clarifying that the tool 'Supports multiple titles and locations,' reinforcing that these string parameters accept comma-separated or combined values, and connecting 'latest' to the hours_old parameter concept.
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 specific action ('Search for the latest jobs and internships') and distinguishes the output format ('structured, LLM-ready Markdown report'), providing exact resource and format clarity.
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?
While there are no sibling tools to contrast against, the description implies usage context through 'LLM-ready Markdown report' suggesting when to use it (when formatted output is needed), but lacks explicit when-to-use or prerequisite guidance.
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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