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Landed

by io.github.landedjobs

Search jobs

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 ) get unlimited, brief-personalized results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleNoTarget role or title family, e.g. "AI Engineer", "RAG Engineer", "Data Scientist".
avoidNoCompanies or sectors to avoid.
limitNoHow many jobs to return.
queryNoFree-text description of the ideal job, in the user’s own words. Parsed server-side into structured filters; also used as a semantic nudge.
remoteNoWork mode preference.
skillsNoCore skills / technologies the role should involve, e.g. ["RAG", "LangChain", "Python"].
minCompNoMinimum acceptable base compensation (numeric).
regionsNoLegacy human-readable remote eligibility regions/countries, e.g. ["APAC", "India"]. Prefer regionCodes/countryCodes when known.
currencyNoCurrency for minComp, e.g. "USD".
locationsNoPreferred physical cities/countries as exact labels; include country for ambiguous cities, e.g. ["Bengaluru, India", "London, UK"]. Resolved server-side to stable place IDs.
seniorityNoSeniority target, e.g. "junior", "mid", "senior", "staff", "lead".
industriesNoPreferred company industries / sectors.
regionCodesNoCanonical remote eligibility regions.
countryCodesNoISO 3166-1 alpha-2 countries where the job may be based or remotely eligible, e.g. ["IN"].
companyStagesNoPreferred company stages, e.g. ["seed", "series-a", "public"].
workAuthorizationCountryCodesNoISO 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.

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TDQS

A4.1/5.0
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.

Resources