Glassdoor Remote MCP Server

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value beyond that by enumerating what the call returns (title, company, rating, location, salary estimate, posted date, description, qualifications/benefits), which is important given there is no output schema. It omits any note on auth, rate limits, or failure modes.

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 core action and resource are front-loaded in the first two sentences, with the use-case list following. The five-item use-case enumeration is somewhat long but still earns its place by orienting downstream consumers; nothing is padded or redundant.

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 single-parameter read tool with no output schema, the description supplies both the input semantics (vacancy URL) and a solid enumeration of the returned fields, which is what an agent needs to decide to call it. It does not cover failure behavior or URL validation, leaving a small gap.

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?

Only one parameter and schema coverage is 100%, so the schema already documents the required url field. The description's phrase 'by its vacancy URL' adds only a mild hint about the expected URL shape and nothing about format or validation, so this lands at the baseline 3.

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?

States a specific verb and resource ('Fetches a Glassdoor job posting by its vacancy URL') and names the returned entity precisely. It is clearly distinguishable from the sibling hasdata_glassdoor_listing_getJobListings, since this one operates on a single posting identified by URL.

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?

Gives concrete contexts for use (ATS ingestion, job aggregators, comp benchmarking, company-profile enrichment, LLM matching/resume tailoring), which tells the agent when this tool is appropriate. It stops short of naming an alternative or stating exclusions, so it does not reach 5.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations: it describes the returned job fields and notes that nextPageToken pagination preserves the original filters. It does not disclose rate limits or authentication requirements, but that is a minor gap given the annotation coverage.

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 direct statement of purpose, then moves through filters, return values, and use cases in a single efficient paragraph. It is slightly dense and could be better structured with bullet points, but every sentence carries relevant information and there is no filler.

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 12-parameter search tool with no output schema, the description is complete enough: it explains the search surface, the returned fields (title, company, location, salary estimate, posted date, job URL, jobId, page tokens), and the pagination behavior. Combined with annotations that cover safety and open-world scope, an agent has what it needs to invoke the 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?

Schema description coverage is 100%, so the schema already documents all 12 parameters in detail. The description summarizes filter categories (date posted, distance, salary range, company rating, easy apply, remote), sort, domain, and pagination behavior, but adds little semantic detail beyond what the schema provides. Baseline 3 is appropriate.

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 and resource ('Searches Glassdoor job listings by keyword and location') and enumerates supported filters and pagination. It also distinguishes itself from the sibling tool:'collect URLs for downstream full-detail scraping via the Glassdoor Job endpoint' points to the separate job-details endpoint.

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 gives clear usage contexts ('build job feeds, monitor hiring trends for roles/companies/regions, power candidate sourcing tools') and identifies the downstream sibling for full-detail scraping. However, it does not explicitly state when not to use this tool or list alternatives beyond that one reference, so it stops short of the 5-level specificity.

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