Search live, vetted India jobs by keyword, location, posting age, and job ID.
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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.
Average 4.5/5 across 2 of 2 tools scored.
get_job and search_jobs have clearly distinct purposes: one lists/searchs jobs and the other retrieves full details for a specific job. There is no overlap or ambiguity between them.
Both tool names follow a consistent verb_noun pattern: 'get_job' and 'search_jobs'. The naming is predictable and clear.
With only two tools, the server feels somewhat thin, but for its narrow purpose of searching and viewing job details, the count is borderline acceptable. It does not feel excessively incomplete, but is at the low end.
The core workflow of searching for jobs and retrieving full details is covered. Minor gaps exist, such as lack of pagination or advanced filtering, but these are not fatal and the main use case is served.
get_jobAInspectFull details for one job (description, qualifications, salary, metadata) by job_id from search_jobs.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the return content (description, qualifications, salary, metadata) which is useful. It does not explicitly state it is read-only or mention error cases, but 'get' strongly implies a safe retrieval operation, and the listed content adds transparency beyond the schema.
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, front-loaded sentence conveying purpose, content, and parameter source without redundancy. 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?
For a simple tool with one parameter and no output schema, this description is complete: it states what it does, what it returns, and how to obtain the required parameter. The sibling relationship is also clarified.
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 description coverage is 0%, so the description must compensate. It clearly explains the job_id parameter's purpose (identifies the job) and its origin (from search_jobs), adding semantic value that the raw schema lacks.
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 fetches full details for one job, listing specific content categories (description, qualifications, salary, metadata). It distinguishes from the sibling search_jobs by focusing on a single job by ID.
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 phrase 'by job_id from search_jobs' explicitly conveys the workflow: use search_jobs to obtain a job_id, then call get_job for full details. This differentiates it from the sibling and clarifies 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.
search_jobsAInspectSearch live job openings across India, checked at official employer sources. India only — non-Indian locations return no results. Returns up to 10 jobs per call with title, company, location, and a jobvetta URL.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Role, skill, or company keywords | |
| days | No | Only jobs posted within the last N days (e.g. 2) | |
| limit | No | Max results (1-10, default 10) | |
| location | No | Indian city, town, or state (India only) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosure. It reveals that data is 'checked at official employer sources', limits results to 10 per call, defines the geographic scope, and lists the output fields (title, company, location, jobvetta URL). This goes beyond a generic statement, though it does not cover error behavior or pagination.
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 two concise sentences with no wasteful words. The first sentence states the core purpose and data source, the second adds the key constraint and return format. Every part is informative and front-loaded.
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?
For a search tool with four simple parameters and no output schema, the description covers the essential context: the action, scope, data source, result limit, and output fields. It does not mention error cases or pagination beyond the 10-item limit, but these are minor gaps.
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 description coverage is 100%, so all four parameters are already well-documented. The description reinforces the location restriction ('India only') but adds little meaning beyond what the schema already provides, so the baseline 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 verb 'search', the resource 'live job openings', and the geographic scope 'across India'. It distinguishes itself from the sibling get_job by indicating it returns a list of up to 10 jobs with basic fields, which is a different function.
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 explicitly states 'India only' and warns that non-Indian locations return no results, providing a clear when-not-to-use condition. It implies use for job searching in India but does not explicitly mention the alternative get_job for detailed views, so it falls short of a perfect score.
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.
Control your server's listing on Glama, including description and metadata
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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
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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
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