Landed

Official3 toolsLive

by landedjobs · TypeScript

Search AI-native jobs, inspect application forms, and fetch free interview-prep resources.

io.github.landedjobs/landed MCP Server

This MCP server is for searching AI-native jobs, inspecting application forms, and fetching free interview-prep resources. It is associated with Model Context Protocol and uses streamable HTTP, with a declared tool count of 3.

🛠️ Key Features

Search AI-native jobs
Inspect application forms
Fetch free interview-prep resources

🚀 Use Cases

Finding AI-focused roles
Reviewing job application forms via the server
Getting interview-prep resources without leaving the MCP workflow

⚡ Developer Benefits

MCP integration (Model Context Protocol)
Streamable HTTP support
Typed ecosystem alignment noted by topics such as TypeScript, VS Code, and developer-tools

⚠️ Limitations

Tooling scope is limited to 3 tools (as indicated by toolCount)

Topics

aiai-agentsai-jobsanthropicclaudeclaude-desktopcursordeveloper-toolsinterview-prepjob-searchjob-search-enginejobsllmllm-toolsmcpmcp-servermodel-context-protocolstreamable-httptypescriptvscode

Captured live from the server via tools/list.

search_jobs

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 <API token>) get unlimited, brief-personalized results.

Parameters16

query	string	optional	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.
role	string	optional	Target role or title family, e.g. "AI Engineer", "RAG Engineer", "Data Scientist".
seniority	string	optional	Seniority target, e.g. "junior", "mid", "senior", "staff", "lead".
skills	array	optional	Core skills / technologies the role should involve, e.g. ["RAG", "LangChain", "Python"].
locations	array	optional	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.
regions	array	optional	Legacy human-readable remote eligibility regions/countries, e.g. ["APAC", "India"]. Prefer regionCodes/countryCodes when known.
countryCodes	array	optional	ISO 3166-1 alpha-2 countries where the job may be based or remotely eligible, e.g. ["IN"].
regionCodes	array	optional	Canonical remote eligibility regions.
workAuthorizationCountryCodes	array	optional	ISO country codes where the candidate is authorized to work.
remote	string	optional	Work mode preference.
minComp	number	optional	Minimum acceptable base compensation (numeric).
currency	string	optional	Currency for minComp, e.g. "USD".
industries	array	optional	Preferred company industries / sectors.
companyStages	array	optional	Preferred company stages, e.g. ["seed", "series-a", "public"].
avoid	array	optional	Companies or sectors to avoid.
limit	integer	optional	How many jobs to return.

Raw schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "maxLength": 400,
      "description": "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."
    },
    "role": {
      "type": "string",
      "maxLength": 120,
      "description": "Target role or title family, e.g. \"AI Engineer\", \"RAG Engineer\", \"Data Scientist\"."
    },
    "seniority": {
      "type": "string",
      "maxLength": 60,
      "description": "Seniority target, e.g. \"junior\", \"mid\", \"senior\", \"staff\", \"lead\"."
    },
    "skills": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 30,
      "description": "Core skills / technologies the role should involve, e.g. [\"RAG\", \"LangChain\", \"Python\"]."
    },
    "locations": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 20,
      "description": "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."
    },
    "regions": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 20,
      "description": "Legacy human-readable remote eligibility regions/countries, e.g. [\"APAC\", \"India\"]. Prefer regionCodes/countryCodes when known."
    },
    "countryCodes": {
      "type": "array",
      "items": {
        "type": "string",
        "pattern": "^[A-Z]{2}$"
      },
      "maxItems": 20,
      "description": "ISO 3166-1 alpha-2 countries where the job may be based or remotely eligible, e.g. [\"IN\"]."
    },
    "regionCodes": {
      "type": "array",
      "items": {
        "type": "string",
        "enum": [
          "apac",
          "emea",
          "americas"
        ]
      },
      "maxItems": 3,
      "description": "Canonical remote eligibility regions."
    },
    "workAuthorizationCountryCodes": {
      "type": "array",
      "items": {
        "type": "string",
        "pattern": "^[A-Z]{2}$"
      },
      "maxItems": 20,
      "description": "ISO country codes where the candidate is authorized to work."
    },
    "remote": {
      "type": "string",
      "enum": [
        "remote",
        "hybrid",
        "onsite"
      ],
      "description": "Work mode preference."
    },
    "minComp": {
      "type": "number",
      "description": "Minimum acceptable base compensation (numeric)."
    },
    "currency": {
      "type": "string",
      "maxLength": 8,
      "description": "Currency for minComp, e.g. \"USD\"."
    },
    "industries": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 20,
      "description": "Preferred company industries / sectors."
    },
    "companyStages": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 20,
      "description": "Preferred company stages, e.g. [\"seed\", \"series-a\", \"public\"]."
    },
    "avoid": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "maxItems": 20,
      "description": "Companies or sectors to avoid."
    },
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 20,
      "description": "How many jobs to return."
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_job_form

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.

Parameters1

jobId

string

required

The jobId from a search_jobs result.

Raw schema

{
  "type": "object",
  "properties": {
    "jobId": {
      "type": "string",
      "minLength": 1,
      "description": "The jobId from a search_jobs result."
    }
  },
  "required": [
    "jobId"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

get_learning_content

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.

Parameters3

topic	string	optional	Free-text topic, e.g. "RAG", "system design", "evals".
role	string	optional	Target role, e.g. "AI Engineer", "AI PM", "GTM Engineer".
category	string	optional	Restrict to one category of content.

Raw schema

{
  "type": "object",
  "properties": {
    "topic": {
      "type": "string",
      "maxLength": 120,
      "description": "Free-text topic, e.g. \"RAG\", \"system design\", \"evals\"."
    },
    "role": {
      "type": "string",
      "maxLength": 120,
      "description": "Target role, e.g. \"AI Engineer\", \"AI PM\", \"GTM Engineer\"."
    },
    "category": {
      "type": "string",
      "enum": [
        "interview-prep",
        "portfolio",
        "roadmap",
        "jobs"
      ],
      "description": "Restrict to one category of content."
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

README.md13 KBView on GitHub

Search AI-native jobs, prepare applications, and study for interviews — from any MCP client. A public Model Context Protocol server giving Claude, Cursor, VS Code, and any MCP-capable agent live access to Landed's ranked, fit-scored job corpus.

Maintained by Landed — daily AI-native job matches, agent help with every application, and mock-interview prep.

What is this?

Landed MCP connects your AI assistant to a curated, continuously-updated corpus of AI-native roles (AI Engineer, ML Engineer, RAG Engineer, AI PM, GTM Engineer, and more). Instead of copy-pasting job boards into a chat, your agent can:

🔎 Search jobs — ranked and fit-scored against a structured brief (role, skills, seniority, remote, compensation, industries…), or from a plain-English description.
📝 Prepare applications — pull a job's real application form, grouped into standard / screening / EEO fields, so the agent can auto-fill and draft answers before you apply.
🎓 Study for interviews — fetch free, curated interview-prep, portfolio, and roadmap resources.

It speaks Streamable HTTP and works with any MCP client. Use the hosted server in seconds, or self-host this repo against your own Landed API.

Free to start. Anonymous callers get a shared free budget — no signup, no key. Add an API token for unlimited, personalized results.

Quick start (hosted)

The hosted server lives at:

code

https://mcp.landed.jobs/mcp

Pick your client below. Everything works anonymously out of the box — just omit the Authorization header. Add Authorization: Bearer lnd_live_… (mint one at Landed → Settings → API tokens) for unlimited, brief-personalized search.

Claude Code

The one-liner (CLI):

bash

# Anonymous (free tier)
claude mcp add --transport http landed-jobs https://mcp.landed.jobs/mcp

# Authenticated (unlimited, personalized)
claude mcp add --transport http landed-jobs https://mcp.landed.jobs/mcp \
  --header "Authorization: Bearer lnd_live_your_token_here"

Then in a session: /mcp to confirm it's connected, and try the find_jobs prompt.

Claude Desktop

Open Settings → Developer → Edit Config (this opens claude_desktop_config.json), then add:

jsonc

{
  "mcpServers": {
    "landed-jobs": {
      "command": "npx",
      "args": [
        "-y", "mcp-remote", "https://mcp.landed.jobs/mcp",
        "--header", "Authorization: Bearer lnd_live_your_token_here"
      ]
    }
  }
}

Restart Claude Desktop. Drop the --header line for the free anonymous tier.

Claude Desktop bridges remote HTTP servers through mcp-remote. If your build has native Custom Connectors (Settings → Connectors → Add custom connector), you can instead paste https://mcp.landed.jobs/mcp directly.

Cursor

Create .cursor/mcp.json in your project (or ~/.cursor/mcp.json globally):

jsonc

{
  "mcpServers": {
    "landed-jobs": {
      "url": "https://mcp.landed.jobs/mcp",
      "headers": { "Authorization": "Bearer lnd_live_your_token_here" }
    }
  }
}

Then enable landed-jobs under Settings → MCP. Omit headers for the free tier.

VS Code (GitHub Copilot / Agent Mode)

Create .vscode/mcp.json:

jsonc

{
  "servers": {
    "landed-jobs": {
      "type": "http",
      "url": "https://mcp.landed.jobs/mcp",
      "headers": { "Authorization": "Bearer lnd_live_your_token_here" }
    }
  }
}

Open the Chat view → Agent mode → the tools appear under the 🔧 picker.

Windsurf

Edit ~/.codeium/windsurf/mcp_config.json:

jsonc

{
  "mcpServers": {
    "landed-jobs": {
      "serverUrl": "https://mcp.landed.jobs/mcp",
      "headers": { "Authorization": "Bearer lnd_live_your_token_here" }
    }
  }
}

Cline (VS Code extension)

Open Cline → MCP Servers → Configure, or edit cline_mcp_settings.json:

jsonc

{
  "mcpServers": {
    "landed-jobs": {
      "type": "streamableHttp",
      "url": "https://mcp.landed.jobs/mcp",
      "headers": { "Authorization": "Bearer lnd_live_your_token_here" }
    }
  }
}

Any other MCP client

Point it at https://mcp.landed.jobs/mcp using the Streamable HTTP transport. Pass Authorization: Bearer <token> if you have one. Quick sanity check from a terminal:

bash

curl -s -X POST https://mcp.landed.jobs/mcp \
  -H 'content-type: application/json' \
  -H 'accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

Registries

Landed is listed on the Official MCP Registry as io.github.landedjobs/landed, and on Smithery as landedjobs/landed. Clients that read either registry can discover and install it without any manual configuration.

Tools

Tool	What it does	Anonymous cost
`search_jobs`	Ranked, fit-scored jobs. Supports physical city/country labels plus canonical remote-eligibility country/region codes; free text is parsed server-side.	1 unit / job returned
`get_job_form`	Application form for a `jobId`, grouped `standard` / `screening` / `eeo` for answer prep.	free
`get_learning_content`	Curated interview-prep, portfolio, and roadmap resources.	free

Prompts (guided workflows)

MCP prompts are reusable, user-invocable skills that teach the agent how to chain the tools and present results as a clean shortlist — never raw JSON. In most clients they show up as slash-commands.

Prompt	Args	What it guides
`find_jobs`	`describe?`	Search, then present a ranked, fit-scored shortlist + next steps.
`prepare_application`	`jobId`	Fetch the form; auto-fill standard, draft screening, leave EEO to the user.
`prep_for_role`	`role?`	Pull learning resources for a role and suggest a study order.

The server also ships always-on instructions (tool overview, presentation rules, freemium behavior) that compliant clients load automatically.

Location parameters

Location eligibility is structured rather than keyword-matched:

locations: physical cities/countries, with country included for ambiguous cities, e.g. ["Bengaluru, India"]
countryCodes: ISO alpha-2 remote/base eligibility, e.g. ["IN"]
regionCodes: apac, emea, or americas
workAuthorizationCountryCodes: ISO alpha-2 work-authorization countries
remote: work mode (remote, hybrid, or onsite)

The hosted API resolves human city labels to stable place identities. Callers must not invent place IDs. The legacy regions field remains accepted for compatibility.

Auth & pricing

Tier	How	What you get
Anonymous	No header	A free budget of job-units that resets every day at 00:00 UTC. Metered by a hybrid of your IP and an issued `X-Landed-Anon` token (echoed on every response — resend it to keep your budget across sessions).
Authenticated	`Authorization: Bearer lnd_live_…`	Unlimited search, personalized to your saved brief + profile. Mint a token at Landed → Settings → API tokens.

Every result carries a freemium block so your agent can see how much budget is left and how to lift the cap.

Architecture

This repository is a thin, self-hostable proxy. It speaks MCP and forwards to the hosted Landed API — it holds no database, no ranking engine, and no job data.

code

   MCP client                 landed-mcp (this repo)              Landed API
 ┌─────────────┐   HTTP    ┌────────────────────────┐  HTTPS   ┌────────────────────────┐
 │ Claude /    │ ───────▶  │ • MCP protocol + tools │ ──────▶  │ • search engine        │
 │ Cursor /    │  /mcp     │ • forwards caller creds│          │ • job corpus + forms   │
 │ VS Code …   │ ◀───────  │ • local learning list  │ ◀──────  │ • auth · freemium meter│
 └─────────────┘           └────────────────────────┘          └────────────────────────┘

The proxy resolves nothing itself: it forwards the caller's API token (or an anonymous id) to the Landed API, which validates it, runs the search, meters usage, and returns results plus a freemium block the proxy relays verbatim. The get_learning_content catalog is static and lives in this repo (src/mcp/catalog/learning.ts) — that tool works with no backend at all.

Self-host

Run your own proxy against the Landed API (or your own deployment of it).

Prerequisites

Node.js ≥ 20
pnpm (npm i -g pnpm)
A Landed API base URL and its shared internal secret

Run

bash

git clone git@github.com:landedjobs/landed-mcp.git
cd landed-mcp
pnpm install
cp .env.example .env       # then fill in the values below
pnpm dev                   # hot-reload dev server
# → [mcp] listening on :8090 — POST /mcp → https://api.landed.jobs/api/v1

For production: pnpm start.

Environment variables

Variable	Required	Default	Description
`LANDED_API_BASE`	✓	`http://localhost:8000/api/v1`	Hosted Landed API base, including the `/api/v1` prefix.
`LANDED_INTERNAL_SECRET`	✓	—	Shared secret authenticating this proxy to the API (must match the server's `INTERNAL_SERVICE_SECRET`).
`PORT`		`8090`	HTTP port to listen on.
`CORS_ORIGINS`		`*`	Comma-separated allowlist of origins, or `*`.
`RATE_LIMIT_PER_MIN`		`60`	Per-IP first-line request cap. The API's freemium meter is the real economic cap.

Endpoints

Method	Path	Purpose
`POST`	`/mcp`	MCP Streamable HTTP endpoint (stateless).
`GET`	`/healthz`	Liveness probe (`{ "ok": true }`).

Development

bash

pnpm dev           # watch + reload
pnpm check-types   # tsc --noEmit
pnpm test          # deterministic input-contract tests
pnpm start         # run once

The repository includes official MCP Registry metadata in server.json. It describes the public Streamable HTTP endpoint but does not imply that the server has been submitted to or accepted by any external registry.

Project layout

code

src/
├── index.ts                 # bootstrap: start the HTTP server
├── config/                  # env → typed config
├── http/                    # express shell + per-IP rate limiter
├── services/api-client.ts   # typed fetch client → Landed API
├── types.ts                 # the API response contract (owned here)
└── mcp/
    ├── server.ts            # instructions + tool/prompt registration
    ├── prompts.ts           # find_jobs / prepare_application / prep_for_role
    ├── catalog/learning.ts  # static, public learning resources
    └── tools/               # search_jobs · get_job_form · get_learning_content

Stack: TypeScript · Express · @modelcontextprotocol/sdk · Zod. No database, no build step (runs on tsx).

Contributing

Issues and PRs welcome — bug fixes, new client setup guides, and learning-catalog additions especially. Please run pnpm check-types and pnpm test before opening a PR. For private vulnerability reports, follow SECURITY.md.

License

MIT © Landed

Install

Tools (3)

search_jobsget_job_formget_learning_content

Remote endpoint

Streamable HTTP

Hosted server - connect over the network, no local install.

url

https://mcp.landed.jobs/mcp

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "landed": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.landed.jobs/mcp"
      ]
    }
  }
}

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.

io.github.landedjobs/landed MCP Server

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

search_jobs

get_job_form

get_learning_content

What is this?

Quick start (hosted)

Claude Code

Claude Desktop

Cursor

VS Code (GitHub Copilot / Agent Mode)

Windsurf

Cline (VS Code extension)

Any other MCP client

Registries

Tools

Prompts (guided workflows)

Location parameters

Auth & pricing

Architecture

Self-host

Prerequisites

Run

Environment variables

Endpoints

Development

Contributing

License

Install

Remote endpoint

Related servers