get_job
One listing in full: description, stated salary, deadline, and the institution's original posting URL. Takes the slug from a curatorsearch.com/opportunities/ URL.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
One listing in full: description, stated salary, deadline, and the institution's original posting URL. Takes the slug from a curatorsearch.com/opportunities/ URL.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
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?
No annotations are present, and the description does not disclose side effects, read-only nature, authentication needs, rate limits, or error behavior. The payload is a simple retrieval, but the lack of explicit behavioral transparency leaves gaps.
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 succinct, with two sentences that convey the core functionality and the source of the slug. No redundant information or unnecessary detail is present.
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 one-parameter read operation, the description is mostly complete. It explains the input and the output scope (full listing details). It lacks explicit return schema or error handling details, but these are not critical for basic usage and the description covers the essential context.
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 only parameter, slug, is explained as coming from a curatorsearch.com URL pattern, which gives useful context. However, the schema has no description for the parameter, and the tool description does not specify format constraints, requiredness, or behavior for invalid slugs.
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 that the tool retrieves a single job listing in full, including description, salary, deadline, and original URL. It also specifies the input is a slug from a known URL pattern, which distinguishes it from sibling tools like search_jobs or institution_jobs.
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 when a specific slug is available, but does not explicitly state when to prefer this tool over alternatives such as search_jobs or institution_jobs. No direct comparison or conditional guidance is provided.
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 serves a distinct function: get_job fetches a single job by slug, search_jobs queries listings, institution_jobs filters by institution, and salary_transparency provides aggregate pay statistics. There is no overlap in purpose, so an agent can easily choose the right tool.
All names are snake_case and descriptive, but they mix verb-first (get_job, search_jobs) with noun-first (institution_jobs, salary_transparency) patterns. The convention is not perfectly uniform, but the names remain intuitive and readable.
With 4 tools, the server is tightly scoped to its job-search purpose. Each tool addresses a distinct need—search, detail, institution-specific, and aggregate statistics—without redundancy or bloat.
The core workflows (searching, retrieving details, and filtering by institution) are covered, and the salary transparency tool adds a valuable analytical feature. A minor gap is the absence of a way to list all institutions without knowing a name upfront, but this does not significantly hinder typical usage.