China state-owned enterprise campus recruitment: search jobs, deadlines, official source links.
Scored across 5 tools
Each tool targets a distinct query type: specific job detail, aggregate stats, deadline listing, filtered search, and recent activity. The slight overlap between list_deadlines and whats_new is mitigated by their different emphases (deadline-only vs. combined new jobs and deadlines), so there is no ambiguity for an agent.
All tool names follow a consistent snake_case pattern with imperative verbs (get_, list_, search_, whats_new). While whats_new is informal, it still fits the lowercase convention and is not confusingly different.
Five tools is within the ideal 3-15 range for a specialized job-radar service. Each tool covers a clear functional area without redundancy, making the set well-scoped and balanced.
For a read-only job data service, the set covers the full lifecycle: searching with filters, retrieving full details, viewing deadlines, getting statistics, and checking recent updates. There are no obvious missing operations needed for the stated purpose.
按 id 取单条岗位全字段（含岗位介绍/要求原文与原公告链接）。先 search_jobs 拿 id。
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It usefully reveals that the response includes full fields, original requirement text, and the announcement link, and it implies read-only retrieval. It does not explicitly confirm side-effect-freeness, auth requirements, or not-found behavior, which is a minor gap for a simple get-by-id tool.
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?
Two short sentences with no filler. The core operation is front-loaded in the first sentence, and the prerequisite search step is given compactly in the second.
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?
This is low-complexity tool: one parameter, an output schema, and no nested objects. The description covers the return content, the required id, and the prerequisite search_jobs call. It omits error/not-found behavior and the list_deadlines sibling, but those are not essential for this straightforward detail lookup.
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?
The schema only provides 'job_id: integer' with no description (0% coverage). The description supplies the missing semantic by explaining that the id identifies a single job and is obtained from search_jobs. It does not name the 'job_id' property explicitly, but the schema already exposes that property name.
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 states a specific verb ('取' / fetch by id), the resource ('单条岗位' / single job), and the scope ('全字段', including original job description/requirements and announcement link). It clearly differentiates from search_jobs by framing this as the id-based detail lookup that follows a search.
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?
It explicitly instructs '先 search_jobs 拿 id', telling the agent to call search_jobs first and then use the returned id here. It gives clear usage context but does not discuss list_deadlines or negative conditions, so it stops short of full when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
数据总览：库容/开放量/企业性质分布/数据源数/近期新增。适合回答"国央企校招现在整体什么情况"。
返回：总量、开放中、按企业性质分布、数据源数、今日新增、近7天新增、数据截至日期。
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose the return payload (totals, open count, distribution, data source count, recent additions, as-of date) and implies a read-only overview. However, it does not explicitly state read-only behavior, data freshness policy, or definitions of terms like '库容'.
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 compact and well-structured: the first sentence defines scope and gives a usage example, the second enumerates the return fields. Every sentence adds value and the key purpose is front-loaded.
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 no parameters, an output schema is present, and the description covers purpose, usage context, and return fields, an agent has everything needed to select and invoke it correctly. No critical information is missing.
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?
The tool has zero parameters and the schema is empty, so there is nothing to document. The baseline for 0-parameter tools is 4, and the description appropriately adds no parameter information because none is needed.
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 identifies the tool as a data overview ('数据总览') and enumerates the metrics it covers: capacity, open positions, enterprise-type distribution, data source count, and recent additions. It also states what is returned. It does not explicitly contrast itself with sibling tools, but the aggregate-stats nature is distinguishable from search/detail/deadline tools.
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 gives a concrete use case: answering questions like '国央企校招现在整体什么情况' (what is the overall situation of state-owned enterprise campus recruitment). This is clear context for when to use the tool, though it does not name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
列出未来 N 天内截止的招聘项目/岗位（求职最刚需的"倒计时"查询）。
days_within: 往前看几天（默认 7，最大 90） include_events: 是否包含宣讲会信号（默认 False） org_nature: 企业性质筛选（"国央企"=央国企+地方国企+银行，默认；"全部"=不限）
| Name | Required | Description | Default |
|---|---|---|---|
| org_nature | No | 国央企 | |
| days_within | No | ||
| include_events | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It clarifies the relative time window, the org_nature filter, and the include_events toggle, but it does not disclose read-only behavior, result limits/pagination, or any edge cases. The behavior is broadly predictable for a list operation, so it is adequate but not rich.
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 main behavior is front-loaded in one sentence, followed by three compact parameter definitions. There is no filler or unnecessary repetition of schema defaults.
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 list tool with three optional, fully explained parameters and an existing output schema, the description covers invocation needs. The only gap is lack of guidance about relationship to sibling tools and any expected result ordering or limits, which would make it fully complete.
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?
The schema has 0% parameter documentation, but the description fully compensates: it explains days_within's default and maximum, include_events's default and meaning, and org_nature's allowed values with expansion of '国央企'. This is exactly the added value needed.
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 states a clear verb and resource: it lists recruitment projects/positions with deadlines within N days. It does not explicitly contrast itself with siblings search_jobs or get_job_detail, though the deadline/countdown framing makes the distinction reasonably inferable.
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 phrase '求职最刚需的倒计时查询' gives a general use case, and the parameter docs clarify the time window and filters. However, there is no explicit guidance on when to choose this over search_jobs or get_job_detail, and no exclusions or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
按条件搜索国央企校招岗位/招聘项目。
参数： city: 工作地点关键词（如 "深圳"、"广东"；为空查全部） major: 专业关键词，对"专业要求原文"做包含匹配（如 "计算机"、"会计"） graduate_year: 届别，如 "2027"（为空查全部） keywords: 对岗位名/单位名做包含匹配 kind: "岗位" | "招聘项目" | "宣讲会"（为空查前两类） org_nature: 企业性质筛选。默认 "国央企"（=央国企+地方国企+银行）； 传 "全部" 看所有（含顺带覆盖的民企/事业单位/未知长尾）； 也可精确传：央国企/地方国企/银行/事业单位/民企/外企/未知 employment_type: 批次筛选："校招" | "实习" | "提前批"（为空查全部） only_open: 只返回未截止的（默认 True） limit/offset: 分页（默认每页 20）
返回字段含截止日、截止状态、专业要求原文、企业性质、原公告链接、数据复核日期。 未写明的条件留空（null），本服务不猜测；重大决策请打开 source_url 核对原文。
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | ||
| kind | No | ||
| limit | No | ||
| major | No | ||
| offset | No | ||
| keywords | No | ||
| only_open | No | ||
| org_nature | No | 国央企 | |
| graduate_year | No | ||
| employment_type | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does substantial work: it discloses defaults (kind empty means first two types, org_nature default includes banks, only_open defaults to true), coverage quirks (incidental private/unknown tail), and a no-guessing policy with a recommendation to verify source_url for major decisions. It also lists important return fields such as deadline status and data review date.
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 front-loaded with a one-line purpose, then uses a tight bullet-style parameter list, followed by return-field and caveat notes. Despite the large number of parameters, every line adds a distinct piece of operational information. The length is justified and easy to scan.
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 tool with 10 optional parameters, no annotations, and no schema descriptions, the description covers all operational needs: filter semantics, defaults, allowed values, pagination, return fields, and the null/no-guess caveat. An agent can select and invoke the tool correctly without supplementary documentation, and an output schema is also present for return shape.
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?
The input schema has 0% description coverage, but the description fully documents all 10 parameters with concrete matching semantics, allowed values for kind and org_nature, defaults, pagination behavior, and example values like '深圳' and '2027'. The description fully compensates for the schema's lack of parameter documentation.
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 opening line states a specific verb and resource: condition-based search for state-owned campus recruitment positions and projects. The enumerated filter dimensions (city, major, graduate year, kind, org nature) make the scope concrete and clearly distinguish it from sibling tools focused on details, stats, deadlines, or recent changes.
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 by showing how to filter jobs/projects and documents defaults, but it never explicitly states when not to use this tool or when to prefer a sibling such as get_job_detail or list_deadlines. An agent can infer the primary use case, but exclusion/alternative guidance is left to the agent's judgment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
看动态：最近 N 天新收录的岗位 + 未来 N 天即将截止的岗位（默认各 20 条）。
适合每天问一句"有什么新岗位/快截止的"，也适合生成校招日报。 days: 回看/前看的天数（1-14，默认 3） limit: 每组最多条数（1-50，默认 20）
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| limit | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It discloses the dual-section output, per-section defaults, lookback/lookforward windows, and parameter constraints. Some details like ordering and exact date-boundary semantics are omitted, but nothing is misleading.
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?
Core behavior is front-loaded in the first sentence, use cases follow in the second, and parameter semantics are compressed into two clear lines. Every element earns its place with no redundancy.
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 tool with two optional parameters, an output schema, and no required fields, the description is complete: it covers purpose, usage context, defaults, and constraints. An agent has enough information to select and invoke this tool correctly.
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?
The input schema provides only types and defaults, with 0% description coverage. The description fully compensates by explaining days as the lookback/lookforward window with range 1-14 and default 3, and limit as the per-group maximum with range 1-50 and default 20.
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 states a concrete action ('看动态') and a clear resource: recently added jobs plus jobs with upcoming deadlines, presented as a combined feed. It implies differentiation from siblings like list_deadlines and search_jobs, but does not explicitly name or contrast them, so it stops short of a 5.
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?
It explicitly names suitable use cases: daily questions about new/expiring jobs and generating campus-recruitment daily reports. It does not provide exclusion guidance such as 'for deadlines only, use list_deadlines', so no when-not-to-use instruction is present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
get_statssearch_jobs1 field changedInput schema / properties / employment_typeAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": null
+}whats_newlist_deadlines1 field changedInput schema / properties / org_natureAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": "国央企"
+}search_jobs1 field changedInput schema / properties / org_natureAdded value: +{
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ],
+ "default": "国央企"
+}get_job_detaillist_deadlinessearch_jobsTPool — Agent-only 招聘平台 (jobs for AI Agents): 岗位/校招/公告/HR评分 read-only; 检索需 X-API-Key, 无投递 no apply.
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