Skip to main content
Glama
mozzan

job104-mcp

by mozzan

search_jobs

Search 104 job listings using natural-language filters for area, job category, salary, type, remote, experience, and education. Sort results by relevance, date, or salary to find matching positions.

Instructions

搜尋 104 職缺。area/jobcat 用中文名稱(如 ["台北市大安區"]、["軟體工程師"]), 內部自動轉成 104 代碼;若名稱不明確會回傳建議選項。每筆結果的 detail_id 可傳給 get_job_detail 取得完整內容。

sort: relevance|date|salary(不填時:有給 salary_min 就自動用 salary 排序, 否則 relevance)。

薪資門檻提醒:104 沒有可靠的伺服器端薪資篩選,所以給 salary_min 時是「用薪資 排序把高薪職缺排到前面」,不是硬篩。薪資會標月薪/年薪(年薪職缺數字較大)。標 「待遇面議」的職缺薪資未公開——若職稱/公司看起來可能達標,呼叫 get_job_detail 通常能拿到真實薪資(detail 常有列表沒有的數字)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordNo
areaNo
jobcatNo
salary_minNo
job_typeNo
remoteNo
is_newNo
exp_yearsNo
eduNo
sortNo
pageNo
page_sizeNo
Behavior3/5

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

With no annotations, the description carries full burden. It explains that area/jobcat are converted from Chinese names, ambiguous inputs return suggestions, salary_min sorts rather than filters, and '待遇面議' jobs may have actual salary in detail. However, it does not cover behavior for other parameters like 'remote', 'is_new', etc., leaving gaps.

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 well-structured with clear sections: purpose, area/jobcat handling, sort behavior, and salary notes. It front-loads the main purpose. While not extremely concise, every sentence adds value.

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

Completeness2/5

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

Given 12 parameters, no output schema, and no annotations, the description is incomplete. It does not explain the output format (only mentions detail_id) and omits many parameters. The salary note, though helpful, does not fully compensate for missing behavioral details.

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 0%, so the description must compensate. It adds meaning for area, jobcat, sort, and salary_min (explaining conversion, suggestions, and sorting behavior). However, 8 of 12 parameters (e.g., keyword, job_type, exp_years) are not described, so the compensation is partial.

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 '搜尋 104 職缺' (search 104 job openings), specifying the verb and resource. It differentiates from sibling tools by noting that the 'detail_id' from results can be used with 'get_job_detail', and 'lookup_code' is a separate tool for code lookup.

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 context on when to use this tool (searching jobs) and references 'get_job_detail' for detailed info. It explains sorting behavior and salary filter limitations. However, it does not explicitly mention when not to use this tool or alternatives for other parameters.

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/mozzan/job104-mcp'

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