search_jobs
Search for real-time job listings with freshness badges on every result. Sources: Remotive + HN Who is Hiring.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Job search query e.g. 'typescript remote' | |
| max_length | No |
Search for real-time job listings with freshness badges on every result. Sources: Remotive + HN Who is Hiring.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Job search query e.g. 'typescript remote' | |
| max_length | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds valuable context beyond these: it indicates real-time freshness badges and the specific sources, which helps set expectations about result freshness and scope. It does not disclose details like pagination or rate limits, but for a read-only open-world search, this is adequate.
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 concise sentences, front-loaded with the primary action and resource. It includes the most relevant differentiators (freshness badges, sources) without unnecessary filler, earning a 5.
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 2-parameter tool with no output schema and annotations covering safety, the description is largely sufficient. It specifies sources and a key output trait (freshness badges), though it could mention the meaning of max_length or return format. Given the low complexity and good annotation coverage, this is a 4 rather than a 3.
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?
Schema coverage is 50%: the 'query' parameter has a helpful description with an example, but 'max_length' has no description. The tool description does not add any semantic detail for the parameters, leaving max_length's meaning and purpose undocumented. This is a middle-ground score because the primary parameter is well-explained but the secondary one is not.
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 'Search' and the resource 'real-time job listings', with specified sources (Remotive + HN Who is Hiring). This distinguishes it from sibling tools like search_repos and extract_* tools, making the tool's purpose unambiguous and specific.
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 finding job listings from the two named sources, providing clear context for when to use the tool. However, it does not explicitly state when not to use it or name alternative tools, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct data source (finance, GitHub, Hacker News, etc.), with clear separation and no overlap. An agent can easily distinguish which tool to use for a given source.
Tools use a consistent verb_noun pattern with 'extract_' for data extraction and 'search_' for search functions. The outlier 'package_trends' is still descriptive and fits the theme, so the pattern is mostly predictable.
11 tools is well-scoped for a data aggregation server. Each tool serves a clear purpose and the count is neither too sparse nor overwhelming.
The server covers a broad range of sources (finance, code, news, social, academia, jobs, packages). Minor gaps like missing Twitter or general news are acceptable given the breadth already provided.