Skip to main content
Glama
lapras-inc

lapras-mcp-server

Official
by lapras-inc

search_jobs

Search for job listings by keyword, position, skills, salary, and more. Filter by programming languages, frameworks, work styles, and employment types to find matching opportunities.

Instructions

Search job by keyword, position, and minimum annual salary

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number for pagination
db_idsNoList of database IDs (28: MySQL, 10: PostgreSQL, 419: SQL Server, 318: Oracle, 33: Aurora, 60: Redis, 221: DynamoDB, 170: MongoDB, 169: Elasticsearch, 200: BigQuery)
keywordNoThe keyword to search for in job listings
infra_idsNoList of infrastructure and CI/CD IDs (15: AWS, 52: GCP, 165: Azure, 18: Docker, 17: Terraform, 224: Kubernetes, 51: Firebase, 16: CircleCI, 122: Jenkins, 180: GitHubActions)
positionsNoList of job position keys (e.g., FRONTEND_ENGINEER, BACKEND_ENGINEER, WEB_APPLICATION_ENGINEER, INFRA_ENGINEER, SITE_RELIABILITY_ENGINEER, ANDROID_ENGINEER, IOS_ENGINEER, MOBILE_ENGINEER, MACHINE_LEARNING_ENGINEER, DATA_SCIENTIST, PROJECT_MANAGER, PRODUCT_MANAGER, TECH_LEAD, ENGINEERING_MANAGER, RESEARCH_ENGINEER, TEST_ENGINEER, SOFTWARE_ARCHITECT, SYSTEM_ENGINEER, EMBEDDED_ENGINEER, DATABASE_ENGINEER, NETWORK_ENGINEER, SECURITY_ENGINEER, SCRUM_MASTER, GAME_ENGINEER, CTO, CORPORATE_ENGINEER, DESIGNER, DATA_ENGINEER, OTHER)
sort_typeNoSort order (人気順: popularity_desc, 新着順: updated_at_desc, 年収が低い順: annual_salary_at_asc, 年収が高い順: annual_salary_at_desc)
work_stylesNoList of work style IDs (1: フルリモート, 2: 一部リモート)
framework_idsNoList of framework IDs (4: Vue.js, 1428: React, 20: Next.js, 31: Nuxt.js, 6: Angular, 172: Redux, 21: Ruby on Rails, 76: Laravel, 140: Spring Boot, 8: Django, 237: Express, 41: Flutter, 171: ReactNative)
prog_lang_idsNoList of programming language IDs (3: TypeScript, 39: JavaScript, 5: Python, 32: Go, 2: Ruby, 25: PHP, 45: Java, 40: Kotlin, 27: Node.js, 43: Swift, 82: Scala, 421: C#, 46: Rust, 56: C++, 42: Dart, 55: Objective-C)
business_typesNoList of business type IDs (1: 自社開発, 2: 受託開発, 3: SES)
employment_typesNoList of employment type IDs (1: 正社員, 2: 業務委託, 3: インターンシップ, 4: その他)
annual_salary_minNoMinimum annual salary requirement in JPY
preferred_condition_idsNoList of preferred condition IDs (1: 副業OK, 2: 副業からのジョイン可, 3: SOあり, 4: BtoB, 5: BtoC, 6: 株式上場済み, 7: グローバル, 8: 残業平均20時間未満, 9: アジャイル開発, 10: 英語で書く・話す業務がある, 11: フレックス, 12: 役員以上にエンジニアがいる, 13: 育休取得実績あり, 14: 地方在住社員がいる, 15: スタートアップ, 16: 副業)
Behavior2/5

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

With no annotations, the description bears full responsibility for behavioral disclosure. It only states 'Search job by keyword, position, and minimum annual salary', omitting critical behaviors like pagination (page parameter), sorting, result format, or rate limits. The parameter descriptions in the schema provide some detail but the description itself adds minimal value.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence, which is efficient, but it lacks structure for a tool with 13 parameters. It does not front-load key information beyond the first few words. While not verbose, it is underspecified.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (13 parameters, no output schema, no annotations), the description is severely incomplete. It does not explain what the search returns (list of jobs?), how pagination works, sorting options, or any advanced filtering behavior. The description alone is insufficient for an agent to use the tool correctly without exploring 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?

The input schema has 100% description coverage, so baseline is 3. The description mentions only three of the thirteen parameters, but the schema descriptions are detailed. The description does not add meaning beyond what the schema already provides, nor does it compensate for the low coverage within the description itself.

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 the verb 'Search' and the resource 'job', and mentions three key filtering parameters. It distinguishes from sibling tools like 'get_job_detail' which fetches a single job, but does not explicitly differentiate from other search-like tools. Still, the core purpose is clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives, nor are there any exclusions or prerequisites stated. The description simply lists a few parameters without contextualizing its role among siblings.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/lapras-inc/lapras-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server