HireBase MCP Server
HireBase MCP サーバー
HireBase ジョブ API と対話するためのツールを提供するモデル コンテキスト プロトコル (MCP) サーバー。
利用可能なMCPインタラクション
このサーバーは、次の MCP インタラクションを公開します。
ツール
search_jobs: さまざまな基準 (キーワード、タイトル、場所、給与など) に基づいて HireBase API を使用して求人を検索します。パラメータ:
query、and_keywords、or_keywords、not_keywords、title、category、country、city、location_type、company、salary_from、salary_to、salary_currency、years_from、years_to、visa、limit。
get_job: HireBase ID を使用して特定のジョブの詳細情報を取得します。パラメータ:
job_id。
プロンプト
create_candidate_profile: 候補者の詳細 (名前、LinkedIn、Web サイト、履歴書のテキスト) に基づいて構造化されたプロンプトを生成し、求職を支援します。パラメータ:
name、linkedin_url、personal_website、resume_text。
Related MCP server: Google Jobs Scraper MCP
クライアントのセットアップ(例:Claude Desktop、Cursor)
このサーバーを Claude Desktop や Cursor などの MCP クライアントで使用するには、サーバー プロセスを実行するようにクライアントを構成し、オプションで HireBase API キーを提供する必要があります。
uvがインストールされていることを確認します:curl -LsSf https://astral.sh/uv/install.sh | shHireBase API キーを取得する (オプション): HireBaseからキーをリクエストします。これを環境変数 (
HIREBASE_API_KEY) として設定することも、空のままにすることもできます。クライアントを構成します。
uvxの使用:Claude Desktop:
claude_desktop_config.jsonを編集します。{ "mcpServers": { "hirebase": { "command": "uvx", "args": [ "hirebase-mcp" ], "env": { "HIREBASE_API_KEY": "" } } } }カーソル: [設定] > [MCP] > [サーバーの追加] に移動します。
Mac/Linux コマンド:
uvx hirebase-mcp(必要に応じてパッケージ名を調整してください)Windows コマンド:
cmdWindows 引数:
/c、uvx、hirebase-mcp(必要に応じてパッケージ名を調整してください)適切なセクションで
HIREBASE_API_KEY環境変数を設定します。
Python 経由でソースから実行する (代替):
リポジトリをクローンし、クローンした場所をメモします
Claude Desktop:
claude_desktop_config.jsonを編集する: GXP2
発達
このプロジェクトでは以下を使用します:
依存関係管理と仮想環境のための
uvリンティングとフォーマットのための
ruffビルドバックエンドとしての
hatch
一般的なタスク
# Setup virtual env
uv venv
# Install dependencies
uv pip install -e .
# install cli tools
uv tool install ruff
# Run linting
ruff check .
# Format code
ruff format .環境変数
HIREBASE_API_KEY(必須): HireBase API にアクセスするための API キー。サーバーが求人データへの認証済みリクエストを行うために必要です。
テスト
このプロジェクトでunittest.mock 、コアツールロジックのテストにpytestを使用しています。unittest.mockを使用して、外部API呼び出しのモックをテストします。
テストの依存関係をインストールします。
# Ensure you are in your activated virtual environment (.venv)
uv pip install -e '.[test]'テストを実行します:
# Example command
pytest貢献
貢献を歓迎します。
ライセンス
このプロジェクトは MIT ライセンスに基づいてライセンスされています - 詳細についてはLICENSEファイルを参照してください。
Available Tools
2 toolsget_jobA
Get detailed information about a specific job
Args:
job_id: The unique identifier of the job
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'detailed information' but does not specify what fields are returned, any authentication or rate limits, or confirm it is a read-only operation. The description lacks sufficient behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with a structured Args section, no redundant words, and perfectly sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter get tool, the description is adequate but could be improved by noting what 'detailed information' includes, potential error conditions, or expected return format. Without annotations or output schema, additional context would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description compensates by adding meaning: 'job_id: The unique identifier of the job' goes beyond the schema's title 'Job Id', explaining the parameter's role effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'get' and the resource 'detailed information about a specific job', and the sibling tool 'search_jobs' implies this tool is for fetching a single job by ID, distinguishing it effectively.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fetching a specific job by ID, but provides no explicit guidance on when to use it versus the sibling 'search_jobs', nor any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_jobsB
Search for jobs using the HireBase API
Args:
query: Full text search query
and_keywords: Keywords that must all appear in results
or_keywords: Keywords where at least one must appear
not_keywords: Keywords that must not appear
title: Job titles to search for
category: Job categories to filter by
country: Countries to filter by
city: Cities to filter by
location_type: Location types (Remote, In-Person, Hybrid)
company: Companies to filter by
salary_from: Minimum salary
salary_to: Maximum salary
salary_currency: Salary currency (e.g. USD)
years_from: Minimum years of experience
years_to: Maximum years of experience
visa: Whether job offers visa sponsorship
limit: Maximum number of results to return
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | ||
| and_keywords | No | ||
| or_keywords | No | ||
| not_keywords | No | ||
| title | No | ||
| category | No | ||
| country | No | ||
| city | No | ||
| location_type | No | ||
| company | No | ||
| salary_from | No | ||
| salary_to | No | ||
| salary_currency | No | ||
| years_from | No | ||
| years_to | No | ||
| visa | No | ||
| limit | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description lacks behavioral context such as read-only status, pagination behavior, rate limits, or cost implications. The parameter 'limit' hints at pagination but doesn't explain default or maximum results. The tool's safety profile (e.g. destructive or not) is completely unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively concise, starting with a clear one-line summary followed by a bulleted list of parameters. It avoids unnecessary prose. However, the parameter list could be streamlined further, and the formatting is slightly inconsistent (e.g., some descriptions end with periods, others do not).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 17 parameters, no output schema, and no annotations, the description is incomplete. It does not explain what the return format is, how pagination works, or any side effects. The agent lacks essential runtime information for a complex search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description partially compensates by providing brief explanations for each parameter (e.g., 'Full text search query', 'Keywords that must all appear'). However, these descriptions are often repetitive of the parameter titles and lack additional detail like allowed values or formatting. They add value but not enough to fully overcome the coverage gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search for jobs' using a specific API. It identifies the verb (search), the resource (jobs), and the system (HireBase API). The sibling tool 'get_job' likely retrieves a single job, making this tool's purpose distinct for broad search/filtering.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or alternative guidance is provided. The description only lists parameters, leaving the agent to infer usage context. The sibling 'get_job' implies a distinction between search and retrieval, but no direct comparison or exclusion criteria are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- First observed
get_job - First observed
search_jobs
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one retrieves a single job by ID, the other searches/filters jobs. There is no overlap in functionality.
Both tools follow a consistent snake_case verb_noun pattern (get_job, search_jobs), making them predictable and easy to understand.
With only 2 tools, the server feels minimal for a job platform. While it covers basic search and retrieval, the scope is thin and suggests missing functionality like listing or management tools.
The tool set covers searching and fetching job details but lacks listing all jobs, CRUD operations, or endpoints for related entities like categories or companies. This leaves notable gaps for a full-featured job interface.
Maintenance
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