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Glama
Abhinav-Sriharsha

ashby-jobs

iterate_list

Find recent job postings across tracked companies within a set time window, with optional keyword filters for title or department.

Instructions

Check every tracked company and return the jobs posted in the last 24 hours.

Args: keyword: optional text to filter by, matched against job title and department (case-insensitive). Accepts several comma-separated keywords and returns jobs matching any of them. Leave blank to use the department saved by set_department, or pass "all" to include every department. hours: how far back to look (default 24)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hoursNo
keywordNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries full responsibility for behavioral disclosure. It explains keyword matching semantics and the hours window, but omits side effects, what happens when no companies are tracked, behavior with no matches, and potential execution cost of checking every company.

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

Conciseness5/5

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

The description is front-loaded with a clear one-sentence summary followed by compact, information-dense parameter documentation. Every sentence contributes meaningful detail with no redundancy.

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

Completeness3/5

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

For a two-optional-parameter tool with no output schema or annotations, the description covers the main behavior and parameters. However, it leaves edge cases unspecified (no tracked companies, no saved department, no matches) and does not describe the return format, which is more burden because no output schema exists.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides only types and defaults with 0% coverage, so the description must compensate. It fully explains keyword semantics: case-insensitive matching against job title/department, comma-separated OR behavior, blank meaning set_department, and 'all' meaning every department. It also clarifies the hours parameter's meaning and default.

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 operation: iterate over tracked companies and return jobs posted in the last 24 hours. It is specific and uses a verb+resource pattern, but it does not explicitly differentiate itself from siblings like list_jobs or get_latest_jobs, relying instead on the 'tracked company' and time-window phrasing.

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?

The description explains how to use parameters (keyword, hours) but never states when to prefer iterate_list over sibling tools, nor does it give exclusions or alternatives. There is no direct guidance on tool selection.

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

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