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RemoShift Remote Jobs

search_jobs

Search 134,154+ live, scam-filtered remote jobs (updated hourly). Natural-language queries OK (hybrid semantic search). Returns title, company, location, salary when disclosed, posted date, an employer apply_url, and a canonical detail_url to link back to.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagNoSkill tag, e.g. "react", "python", "figma"
sortNorelevance
limitNoMax results, 1-25 (default 10)
queryNoKeywords or a natural-language description, e.g. "senior react engineer"
sourceNoOrigin board/ATS name, e.g. "Himalayas", "RemoteOK", "Greenhouse"
countryNoISO-2 country code the job must accept applicants from, e.g. "US", "HK", "DE"
categoryNoJob category, e.g. "developer", "design", "customer service", "sales", "marketing"
seniorityNo
has_salaryNotrue = only jobs that disclose salary
work_anywhereNotrue = location-independent jobs only
posted_within_daysNoOnly jobs posted in the last N days

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It usefully discloses data freshness ('updated hourly'), data quality ('scam-filtered'), query behavior ('hybrid semantic search'), and the exact shape of the returned data. It does not mention rate limits, authentication, or pagination, but those are less critical for a read-only search tool.

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 compact and well-structured: it opens with the core action and dataset, then adds the distinguishing query feature, then enumerates the result fields. Every sentence earns its place, and there is no filler.

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 an 11-parameter tool with no output schema and no annotations, the description covers the critical output fields and general behavior well. Minor gaps exist: it does not explicitly route the agent to get_job for full detail, nor does it clarify how the limit parameter interacts with pagination, though both are hinted or covered in the schema.

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 description coverage is high (82%), so the baseline of 3 applies. The description reinforces that natural-language queries are accepted, but most parameter semantics are already covered in the schema. It does not add deeply beyond what the structured schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action and resource: searching live, scam-filtered remote jobscas. It also hints at its role as a list-returning search tool via the canonical detail_url, but it does not explicitly distinguish itself from the sibling tools get_job or job_facets.

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 clear usage context: it is for searching remote jobs with natural-language queries and returns job summaries. It does not, however, offer explicit exclusion criteria or point to when to use get_job or job_facets instead.

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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