Verified job search: every listing confirmed live and accepting applicants within the last 72h.
Snapshot of the verified pool: how many jobs are live right now, counts by work mode, and the occupations (role slugs) with jobs today. Good first call to see what's available before searching.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states the tool is a snapshot (read-only, non-destructive) and lists the specific data returned: counts, counts by work mode, and occupations. This is sufficient transparency for a simple aggregation tool, though it could mention any limitations or performance characteristics.
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 sentences, efficiently front-loaded with the core purpose. Every word adds value, with no redundancies 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?
Given the absence of an output schema, the description adequately explains the return value's content: counts, counts by work mode, and occupations. It omits structural details but for a simple snapshot, this is sufficient for correct use.
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 tool has zero parameters, and the schema coverage is 100% (empty object). The description correctly adds no parameter details because none exist. For a parameterless tool, the baseline of 4 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 it provides a snapshot of the verified pool, including counts of live jobs, counts by work mode, and occupations with jobs today. It distinguishes itself from sibling tools (get_verified_job and search_verified_jobs) by being an aggregate overview.
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 says 'Good first call to see what's available before searching,' providing clear context for when to use it. It implicitly suggests that for searching, one should use search_verified_jobs, though it doesn't explicitly state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full record for one verified job by id (from search_verified_jobs): complete summary, the pirch page url to cite, the original posting apply_url (applications happen there — pirch never sits between applicant and employer), verification timestamps, and the matching career-guide link.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Job id from search results. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that applications occur on the original apply_url and that pirch never intermediates, adding useful behavioral context. It does not explicitly state read-only nature or auth requirements, but the description implies a safe retrieval operation.
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 sentence that efficiently packs key information, but it is somewhat long and could be broken into clearer parts. The core purpose is front-loaded, and there is no redundancy. Minimal but effective.
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?
The tool has low complexity with one parameter and no output schema. The description lists the return fields (summary, url, apply_url, verification timestamps, career-guide link), providing sufficient context for a single-record retrieval. Missing details like structure or limits are not critical for this simple tool.
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% with one parameter 'id' described as 'Job id from search results.' The description adds a mention of the sibling tool but no new detail on the parameter's format or constraints. Baseline 3 is appropriate as the schema already provides clear meaning.
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 it retrieves the full record for one verified job by id, identifies the source sibling tool search_verified_jobs, and lists the included fields (url, apply_url, verification timestamps, career-guide link). It unambiguously distinguishes from siblings by specifying it is a single-record fetch from search results.
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 use after obtaining an id from search_verified_jobs, providing context for when to use the tool. However, it does not explicitly state when not to use it or mention alternatives like get_jobs_overview. The guidance is implicit but clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search pirch's pool of verified-live job listings. Every job returned was opened and confirmed real and accepting applicants by pirch within the last 72 hours — dead and ghost listings are removed automatically, so results can be trusted as live right now. Free, public data. When showing a job to a user, link its url (the pirch page, which shows the verification and links to the original posting).
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Occupation slug filter (e.g. 'ux-designer', 'registered-nurse'). Use get_jobs_overview to see which slugs currently have jobs. | |
| limit | No | Max results, 1-25 (default 10). | |
| query | No | Free-text search over title, company, location, occupation and summary (e.g. 'senior product designer'). | |
| location | No | Substring match on the listing's location (e.g. 'Denver'). Note: remote jobs often have no city and won't match a city filter — filter workmode=remote instead. | |
| workmode | No | Filter by work mode. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description bears full burden. It discloses that jobs are verified within 72 hours, dead listings removed, and data is free/public. No contradictions.
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?
Three sentences, front-loaded with purpose, then verification detail, then usage tip. No 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?
Tool has 5 params, no output schema, 2 siblings. Description covers input filters, verification, linking guidance, and references sibling for role slugs. Complete for complexity.
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% with descriptions. Description adds value: notes role uses slugs from get_jobs_overview, and location param explains remote jobs may not match city filters.
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 it searches pirch's verified-live job listings, specifying that jobs are confirmed real and accepting applicants. It distinguishes from siblings: get_jobs_overview (overview) and get_verified_job (single job).
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 explains when to use the tool (search verified jobs) and gives a usage tip (link to url). It does not explicitly state when not to use it, but the purpose is contextually clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_jobs_overviewget_verified_jobsearch_verified_jobsClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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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Search 6.3M+ live jobs from companies' own career pages, plus resume tailoring & cover letters.
Search a live index of millions of open jobs from employer career sites and 100+ ATS platforms.
Is this company actually hiring, or is the board just still up? Real dates on 187,923 roles.
Search live, vetted India jobs by keyword, location, posting age, and job ID.
Add one secure layer between your agents and this server.
Each tool serves a distinct purpose: get_jobs_overview provides summary counts, search_verified_jobs returns filtered lists, and get_verified_job retrieves full details by ID. There is no functional overlap.
All tool names follow a consistent verb_noun pattern using snake_case (get_, search_), making them predictable and easy to understand.
With only 3 tools, the server is minimally scoped but covers the essential operations for verified job data access. The count is appropriate for a focused read-only service.
The tool set covers the full read lifecycle: overview, search, and detail retrieval. No write operations are needed for this domain, so there are no obvious gaps.
