RemoShift Remote Jobs

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

With no annotations, the description must carry the behavioral burden. It does so by clarifying this is a fetch (read) operation that returns the full job including the description field. It does not disclose error/not-found behavior, which is a minor gap for a read-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?

A single sentence that front-loads the action and identifier source, with no filler or 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?

Simple tool with one parameter and no output schema; the description tells the agent exactly what to provide and what it will get back (full job with description). It omits only non-critical details like not-found handling and explicit sibling routing.

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 already describes id as 6-16 char hex (100% coverage), so baseline is 3. The description adds value by explaining where the id comes from (end of detail_url) and reinforces the expected format, though its '8-hex' phrase is slightly narrower than the schema's '6-16 char'.

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 action ('Fetch one job in full'), a concrete resource ('one job'), and a precise identifier source (the 8-hex id from detail_url). The phrase 'in full (description included)' distinguishes this from list/search siblings that return summaries.

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?

Implied usage is clear: use this when you already have the job id from a detail_url and need the complete record. It does not explicitly name sibling alternatives or exclusions, but the single-job-by-id framing makes the routing unambiguous for most agents.

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?

No annotations are provided, so the description carries the burden. It states the tool returns counts across the live index, which implies a read-only aggregation operation. However, it doesn't disclose details like whether counts are approximate, how the live index is defined, or whether there are limits on the counts returned. The description is honest but minimal.

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 one sentence with no wasted words. It front-loads the core function (counts by dimensions) and then gives the usage directive. Every word earns its place.

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 zero-parameter aggregation tool, the description is nearly complete. It explains what the tool does and when to use it. The only missing context is what the output looks like (e.g., a map of dimension to counts) and whether there are any caveats about the live index, but the absence of an output schema and parameters lowers the burden. A 4 is fair.

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, so the schema is trivially complete. The description adds meaning by explaining what the counts are grouped by (source, category, country, tag), which is the entire semantic payload. With no parameters, a baseline of 4 is appropriate, and the description fully compensates.

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 tool counts jobs by source, category, country, and tag across the live index. It also distinguishes its purpose from siblings by saying to call it first to learn what filters are useful, which differentiates it from get_job and search_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 explicitly says when to use this tool: call it first to learn what filters are useful. This implies it should be used before search_jobs to inform filter choices, and it is not for retrieving individual jobs or search results. It provides clear context and a usage directive.

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?

With no annotations, the description carries the behavioral burden. It adds useful context: live data, hourly updates, scam filtering, and semantic search behavior. It also discloses the output fields, including employer apply_url and canonical detail_url. It stops short of mentioning pagination or rate limits, but for a read-oriented search tool this is solid 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?

Two sentences front-load the scale and data quality, then state capabilities and return fields. Every phrase earns its place, with no filler or redundancy. The structure is easy to scan and parse for an agent.

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 an 11-parameter tool with no output schema and no annotations, the description gives the essential return shape but omits parameter interaction semantics (e.g., whether filters combine as AND), behavior when no filters are supplied, and any pagination guidance. These gaps matter for an agent deciding how to compose a correct query, though the schema covers individual parameter meaning.

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 82%, so the schema already documents most parameters. The description reinforces query semantics with 'natural-language queries OK' and 'hybrid semantic search,' but this largely restates the schema's query description. It does not add meaning for the remaining parameters beyond what the schema already provides, so the 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 clearly identifies the action (search), the resource (remote jobs), and the scope (179,000+ live, scam-filtered, updated hourly). It also lists the returned fields, making it easy to distinguish from siblings like get_job (single job retrieval) and job_facets (aggregation). The detail_url reference implicitly routes back to a canonical job record.

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 establishes clear search context, including natural-language query support and semantic search. It does not explicitly state when to prefer get_job or job_facets, but the list-style result set and 'link back to' canonical detail URL make the replacement use case obvious. No misleading alternatives are suggested.

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