A
licenseNot graded
qualityA
maintenanceEnables AI agents to discover, filter, and track job openings based on the user's local resume, without uploading data to the cloud.
18
MIT
Landed
Server Details
Search AI-native jobs, inspect application forms, and fetch free interview-prep resources.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Uptime
- 100.0% over 38 days
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
- Repository
- landedjobs/landed-mcp
- GitHub Stars
- 3
- Server Listing
- Landed MCP Server
TDQS
A4.1/5.0
Scored across 3 tools
Disambiguation5/5
Each tool has a distinct purpose: search_jobs finds jobs, get_job_form retrieves application forms, and get_learning_content provides learning materials. No overlap between them.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case: search_jobs, get_job_form, get_learning_content.
Tool Count4/5
With only 3 tools, the server feels slightly thin, but it is well-scoped for job search and preparation. The count is appropriate for a focused domain.
Completeness4/5
The tools cover job search, form retrieval, and learning content. Missing features like application submission or user profile management are not critical given the server's role as a preparation helper. Minor gaps exist.
Available Tools
3 toolsget_job_formGet job application formAInspect
Get the application form for a job (by the jobId returned from search_jobs), so you can prepare answers before the user applies. Fields are grouped: "standard" (auto-fillable from a candidate profile via mapsTo), "screening" (free-text questions to draft from the résumé/experience), and "eeo" (leave to the user). Always free — a job's form is only reachable once you've already found the job via search_jobs.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| jobId | Yes | The jobId from a search_jobs result. |
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must carry the burden. It explains the grouping of fields ('standard', 'screening', 'eeo') and notes the form is 'Always free'. This adds useful behavioral context beyond the raw input schema.
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 concise (three sentences) with no fluff. It front-loads the purpose and then adds necessary details about field groups and prerequisites. Every sentence 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 simple parameter set and no output schema, the description covers the essential aspects: purpose, prerequisites, and structure of the form. It is sufficiently complete for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter jobId, with a clear description. The tool description adds only the context that jobId comes from search_jobs, which is helpful but not significantly beyond the schema. Baseline score of 3 is appropriate.
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 states the tool's purpose: 'Get the application form for a job (by the jobId returned from search_jobs)'. It specifies the verb and resource, and distinguishes from sibling tools like search_jobs and get_learning_content.
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 context on when to use the tool: 'so you can prepare answers before the user applies' and notes prerequisites ('only reachable once you've already found the job via search_jobs'). It does not explicitly mention alternatives, but the guidance is clear enough for a focused tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_learning_contentGet learning contentAInspect
Get Landed's free learning content to help the user prepare — curated interview-prep repos (real questions, company guides, worked system designs), portfolio-project catalogs, and role roadmaps from the landedjobs GitHub org. Filter by topic, role, and/or category. Always free, for any caller.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Target role, e.g. "AI Engineer", "AI PM", "GTM Engineer". | |
| topic | No | Free-text topic, e.g. "RAG", "system design", "evals". | |
| category | No | Restrict to one category of content. |
TDQS
A3.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must bear disclosure burden. It notes 'Always free, for any caller' suggesting no restrictions, but does not explicitly confirm read-only, side effects, auth needs, or rate limits. Adds 'curated' but lacks complete behavioral context.
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?
Two sentences with front-loaded content types and filter guidance. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, description should clarify return format (list vs. single) and pagination. It implies multiple items ('repos, catalogs, roadmaps') but not explicit. Lacks details on ordering, limits, or error handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% parameter description coverage; description adds 'Filter by topic, role, and/or category' which mirrors schema info without additional semantics like combination behavior.
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?
Description clearly states tool returns 'Landed's free learning content' including specific types (interview-prep repos, portfolio-project catalogs, role roadmaps) from the landedjobs GitHub org, distinguishing it from sibling tools for job forms and job search.
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?
Explicitly states filtering by topic, role, and/or category, and implies use for learning content versus job-related siblings. No explicit when-not-to-use or alternatives beyond context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_jobsSearch jobsAInspect
Search Landed's live job corpus for AI-native roles and get a ranked, fit-scored shortlist. Fill the structured fields (role, skills, work mode, physical locations, remote-eligibility countries/regions, seniority, comp, industries…) from the user's request — they drive the ranking. Physical locations are resolved to stable places; use ISO country codes and canonical region codes when known. You may also pass a free-text "query"; it's parsed into the same filters and used as a semantic nudge. Free tier: up to a shared budget of jobs for anonymous callers (each returned job counts). Authenticated callers (Authorization: Bearer ) get unlimited, brief-personalized results.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Target role or title family, e.g. "AI Engineer", "RAG Engineer", "Data Scientist". | |
| avoid | No | Companies or sectors to avoid. | |
| limit | No | How many jobs to return. | |
| query | No | Free-text description of the ideal job, in the user’s own words. Parsed server-side into structured filters; also used as a semantic nudge. | |
| remote | No | Work mode preference. | |
| skills | No | Core skills / technologies the role should involve, e.g. ["RAG", "LangChain", "Python"]. | |
| minComp | No | Minimum acceptable base compensation (numeric). | |
| regions | No | Legacy human-readable remote eligibility regions/countries, e.g. ["APAC", "India"]. Prefer regionCodes/countryCodes when known. | |
| currency | No | Currency for minComp, e.g. "USD". | |
| locations | No | Preferred physical cities/countries as exact labels; include country for ambiguous cities, e.g. ["Bengaluru, India", "London, UK"]. Resolved server-side to stable place IDs. | |
| seniority | No | Seniority target, e.g. "junior", "mid", "senior", "staff", "lead". | |
| industries | No | Preferred company industries / sectors. | |
| regionCodes | No | Canonical remote eligibility regions. | |
| countryCodes | No | ISO 3166-1 alpha-2 countries where the job may be based or remotely eligible, e.g. ["IN"]. | |
| companyStages | No | Preferred company stages, e.g. ["seed", "series-a", "public"]. | |
| workAuthorizationCountryCodes | No | ISO country codes where the candidate is authorized to work. |
TDQS
A3.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses ranking, fit-scoring, free-tier budget limits, and authenticated personalization. However, it does not mention rate limits, error behavior, or confirm read-only nature, which are important for a search tool. Some behavioral context is given but gaps remain.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (~150 words) and front-loaded with the main purpose. Every sentence adds value, including instructions on filling fields and tier differences. Minor redundancy exists but overall efficient.
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 complexity (16 parameters, no output schema), the description covers key aspects: ranking, fit-scoring, field filling, location resolution, free-text parsing, and tier differences. It adequately supports an AI agent in understanding tool usage, though return format details are omitted but acceptable for a search tool.
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?
Schema coverage is 100% (baseline 3). The description adds value by explaining that structured fields drive ranking, physical locations are resolved to stable places, and free-text query is parsed into same filters and used as a semantic nudge. This contextual meaning goes beyond the individual schema descriptions.
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 states the tool searches Landed's live job corpus for AI-native roles and returns a ranked, fit-scored shortlist. It uses a specific verb ('Search'), resource ('Landed's live job corpus'), and result type, which distinguishes it from sibling tools like get_job_form and get_learning_content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a user wants to find AI-native jobs, and mentions filling structured fields and using free-text query. However, it does not explicitly state when not to use this tool or provide alternatives, such as using get_job_form for a specific job's application form. The guidance is clear but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
- First observed
get_job_form - First observed
get_learning_content - First observed
search_jobs
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