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Remote Jobs by Country

Find remote jobs open to a country

find_remote_jobs
Read-onlyIdempotent

Find remote jobs a person can be hired for from a country. Use it for "remote jobs I can get from Vietnam" or "crypto jobs hiring in Brazil". Pass country (name or ISO code). Optional: family (software, data, design, product, marketing, sales, support, ops, finance, hr, assistant, other), type (full_time, part_time, contract, internship), niche (crypto, ai, startup, nonprofit, academia, government), keywords, only_open, limit (default 10, max 25). Returns jobs newest first with open ones before unclear ones: title, company, verdict (open or unclear), a reason quoting the post, apply link, date, family, type, niches, location, salary, page link, source to credit, and with keywords matched (where each whole word was found). Jobs whose post rules the country out are never returned. Daily index of public job boards and feeds.

Input Schema

NameRequiredDescriptionDefault
typeNoJob type, only when the user asked for one.
limitNoHow many jobs, 1 to 25. Defaults to 10.
nicheNoOnly when the user asked for crypto/web3, AI, startup, nonprofit, academia or government jobs.
familyNoRole family, only when the user named a kind of work.
countryYesThe country the user would work from, as a name or 2-letter ISO code, such as "Vietnam" or "VN".
keywordsNoUp to 5 words that must all appear as whole words in the job, such as "react typescript". Each job then names where in matched.
only_openNoTrue to leave out unclear jobs and return only jobs whose post includes the country.

Output Schema

NameRequiredDescriptionDefault
jobsYes
moreYes
judgedYes
countryYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / keywords / description
      Previous value: -"Up to 5 words that must all appear in the job, such as \"react typescript\"."New value: +"Up to 5 words that must all appear as whole words in the job, such as \"react typescript\". Each job then names where in matched."
    • addedOutput schema / properties / jobs / items / properties / matched
      Added value: +{
      +  "description": "With keywords: each keyword, the field it was found in and the text around it",
      +  "items": {
      +    "properties": {
      +      "field": {
      +        "enum": [
      +          "title",
      +          "company",
      +          "summary",
      +          "description"
      +        ],
      +        "type": "string"
      +      },
      +      "keyword": {
      +        "type": "string"
      +      },
      +      "text": {
      +        "type": "string"
      +      }
      +    },
      +    "required": [
      +      "keyword",
      +      "field",
      +      "text"
      +    ],
      +    "type": "object"
      +  },
      +  "type": "array"
      +}
  2. Changed4 schema fields changed
    • removedInput schema / examples
      Removed value: -[
      -  {
      -    "country": "Vietnam",
      -    "limit": 3
      -  }
      -]
    • removedOutput schema / properties / as_of
      Removed value: -{
      -  "description": "ISO 8601 time the data was published by its source or fetched from it. A cached answer keeps the time of its fetch.",
      -  "type": "string"
      -}
    • removedOutput schema / properties / source
      Removed value: -{
      -  "description": "Who the data comes from, to cite and to check it against.",
      -  "properties": {
      -    "name": {
      -      "type": "string"
      -    },
      -    "url": {
      -      "type": "string"
      -    }
      -  },
      -  "required": [
      -    "name",
      -    "url"
      -  ],
      -  "type": "object"
      -}
    • changedOutput schema / required
      Previous value: -[
      -  "country",
      -  "jobs",
      -  "judged",
      -  "more",
      -  "as_of",
      -  "source"
      -]New value: +[
      +  "country",
      +  "jobs",
      +  "judged",
      +  "more"
      +]
  3. Changed3 schema fields changed
    • addedOutput schema / properties / as_of
      Added value: +{
      +  "description": "ISO 8601 time the data was published by its source or fetched from it. A cached answer keeps the time of its fetch.",
      +  "type": "string"
      +}
    • addedOutput schema / properties / source
      Added value: +{
      +  "description": "Who the data comes from, to cite and to check it against.",
      +  "properties": {
      +    "name": {
      +      "type": "string"
      +    },
      +    "url": {
      +      "type": "string"
      +    }
      +  },
      +  "required": [
      +    "name",
      +    "url"
      +  ],
      +  "type": "object"
      +}
    • changedOutput schema / required
      Previous value: -[
      -  "country",
      -  "jobs",
      -  "judged",
      -  "more"
      -]New value: +[
      +  "country",
      +  "jobs",
      +  "judged",
      +  "more",
      +  "as_of",
      +  "source"
      +]
  4. First observed

TDQS

A4.3/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Goes well beyond the readOnly/idempotent/openWorld annotations: it discloses result ordering (newest first, open before unclear), the verdict taxonomy with a reason quoting the post, the hard filter that country-excluded jobs are never returned, and the data source (daily index of public job boards and feeds). Those are exactly the behavioral facts an agent needs to interpret results and set expectations about freshness and coverage.

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?

Front-loaded with the purpose and examples, then parameters, then return shape — a sensible ordering with no filler sentences. The inline re-enumeration of family/type/niche values is somewhat redundant with the schema and lengthens the block without adding much.

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?

For a 7-parameter search tool this covers purpose, filtering semantics, ordering, and result fields thoroughly. Since an output schema already exists, the long field-by-field return listing is partly duplicative, but nothing an agent needs in order to call it correctly is missing.

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%, so every parameter already carries its own description and enums; the description mostly restates them (family list, niche list, limit default 10 / max 25). It adds a small amount of guidance — that optional filters should be passed only when the user actually asked for them — but no syntax or interpretation detail the schema lacks.

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?

States a specific verb and resource with an explicit scope constraint: 'Find remote jobs a person can be hired for from a country.' The title and first sentence together make the eligibility framing (jobs you can actually be hired for from a given country) unmistakable, and it is clearly unrelated to the feedback siblings.

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?

Gives concrete trigger phrasings ('remote jobs I can get from Vietnam', 'crypto jobs hiring in Brazil') that map natural user requests onto this tool, which is strong context. It does not name when NOT to use it or point to any alternative, but with only unrelated feedback siblings there is little routing ambiguity to resolve.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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