recruiter-roles-mcp

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

With annotations already declaring readOnlyHint=true, idempotentHint=true, and destructiveHint=false, the description adds meaningful behavioral context: it specifically returns a paginated list and only active jobs, not all jobs or company metadata. This enriches the agent's understanding of response shape and filtering behavior.

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, front-loaded sentence that wastes no words. It conveys the core action and key scope details ('full profile', 'paginated list', 'active jobs') with zero 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?

Given the output schema is present (not shown here but flagged), the description covers the essential content: profile data plus a paginated active-jobs list. Combined with complete parameter docs and annotations, the tool is well-specified. Minor gap: no mention of behavior for non-existent slugs, but this is a minor omission for a read-only fetch tool.

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%: all three parameters (slug, page, per_page) are fully described in the input schema. The description adds no extra parameter semantics, so the baseline of 3 is appropriate per rubric.

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?

Clearly states the specific verb 'Fetch' and the precise resource: 'a single company's full profile plus a paginated list of its active jobs.' This instantly distinguishes it from siblings like get_job (single job) and list_companies (many companies).

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 its use case: retrieving detailed data for one company, including its active jobs. It does not explicitly name alternatives or when not to use it, but the purpose is unambiguous given sibling tool names. A brief exclusionary note (e.g., 'for multiple companies, use list_companies') would make it a 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 and idempotentHint=true, so the description only needs to add context beyond safety. It does so by specifying the exact content returned (complete description, requirements, benefits, tech stack, apply contact), which clarifies the tool's scope and output without contradicting the annotations.

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, front-loaded sentence with no redundant words. It efficiently states the action, target, and expected contents, making every word valuable.

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?

With one parameter, robust annotations, and an output schema present, the description fully covers what is needed to select and invoke the tool. It names the identifier source and details the return contents, leaving no critical ambiguity.

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 100% with a well-described 'slug' parameter (including how to obtain it). The tool description merely repeats 'by its slug' without adding new parameter-level meaning, so the baseline of 3 applies.

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 uses a specific verb ('Fetch') and clearly identifies the resource ('full detail for a single job') plus the method ('by its slug'). It enumerates the included content (description, requirements, benefits, tech stack, apply contact), which distinguishes it from sibling tools like search_jobs or get_company.

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 states the tool retrieves details for a single job identified by slug, and explicitly references search_jobs as the source of the slug. This implies the typical workflow (search first, then get detail), though it does not provide explicit exclusions or compare against other sibling tools.

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, idempotentHint, and destructiveHint, so the safety profile is known. The description adds behavioral context by mentioning that results include 'active job counts' and that filtering is possible by name, company type, or HQ country—details not present in the annotations.

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 with no filler. The main verb and object appear first, making the tool's purpose immediately identifiable. 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?

Given the rich output schema and comprehensive parameter descriptions, the description is sufficient for a listing endpoint. It does not explain pagination or sorting, but those are well-documented in the schema. The description adequately scopes the tool's function and filter capabilities.

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 baseline is 3. The description echoes three of the filter parameters (q, company_type, country) but does not add any syntax or format details beyond the schema. It adds marginal value but does not elevate beyond what the schema already provides.

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 uses a clear verb ('Browse') and identifies the resource ('directory of recruiting companies and agencies'), and specifies the added value ('active job counts'). This distinguishes it from sibling tools like get_company (single company) and search_jobs (jobs), so the purpose is unambiguous.

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 frames this as a directory-browsing tool, which clearly implies listing use cases. It does not explicitly name alternatives or say when not to use it, but the context is clear given sibling tool names. A score of 4 reflects the clear context without explicit exclusions.

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?

The readOnlyHint, idempotentHint, and destructiveHint annotations already establish a safe read operation. The description adds context about active job counts and the semantic meaning of returned codes/slugs, going beyond the annotations without contradicting them.

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 concise, front-loaded sentences with no filler. The first states the main action and scope; the second provides a direct actionable usage note.

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?

The tool is simple with optional parameters, full schema coverage, an output schema, and annotations. The description covers purpose, hierarchy levels, counts, and downstream usage—everything needed for an agent to select and 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 fully documents all three parameters. The description's mention of codes/slugs and search_jobs filters is more about output usage than parameter meaning, adding limited value for parameter selection.

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 uses a specific verb ('List') and resource ('location hierarchy'), names the exact levels (countries, regions, cities), and mentions active job counts. This clearly distinguishes it from sibling tools like list_companies or 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 second sentence explicitly directs the agent to use the returned codes/slugs for the 'state' and 'city' filters in search_jobs, providing a clear use case. It doesn't state when not to use it or compare against alternatives, but the context is strong.

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?

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safe read-only behavior is covered. The description adds transparency about the data returned (current active job counts) and the fact that all sectors are listed. This extra detail about output content goes beyond the annotations, providing useful context about what to expect.

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 exactly two sentences, front-loaded with the core action ('List all recruiting sectors with their current active job counts') and followed by a practical usage tip. Every word earns its place; there is no redundancy or 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 zero-parameter tool with an output schema, the description is fully complete: it identifies the resource (recruiting sectors), the data returned (active job counts), and how to use the results (slugs for the sector filter). It also implies 'all' sectors, setting expectations for the response scope. The output schema handles return value specifics, so no further description is needed.

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 baseline for parameter semantics is 4. The description does not need to explain parameters, and it adds value by explaining the output (slugs and counts), which indirectly clarifies what the returned data means. No param information is missing.

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's purpose with a specific verb ('List'), resource ('all recruiting sectors'), and adds scope ('with their current active job counts'). It also distinguishes the tool from siblings by mentioning the returned slugs are used for the 'sector' filter in search_jobs, making it clear this is sector-specific and not a general company or location listing.

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 provides clear usage context by instructing the user to 'Use the returned slugs for the 'sector' filter in search_jobs.' This implies when to use the tool: whenever sector identifiers are needed for job filtering. It does not explicitly exclude alternatives like list_companies or list_locations, but the context is sufficiently clear for an agent to choose this tool over siblings.

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, idempotentHint, and destructiveHint, covering the safety profile. The description goes beyond this by detailing the exact metrics and breakdowns returned, giving the agent a clear picture of the tool's behavior without exceeding its scope.

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, tightly packed sentence that front-loads the key concept ('Aggregate market overview') and then enumerates specific outputs. No wasted words; every phrase adds value.

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?

Given the simple, parameterless nature and the presence of an output schema, the description fully covers the tool's purpose and content. No important details are missing, and the agent has enough information to invoke it 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?

The tool has zero parameters, so the baseline is 4. The description fully explains what the tool does, making the absence of parameters completely unambiguous. No additional parameter information is needed.

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 uses a specific verb+resource: 'Aggregate market overview' clearly defines it as a summary/stats tool. It lists concrete outputs (total active jobs, posting velocity, breakdowns) which distinguishes it from sibling tools like get_job, list_companies, 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 clearly conveys the tool's context: use it for market-level aggregate statistics. It does not explicitly mention alternatives or exclusions, but the 'aggregate' framing inherently contrasts with the more specific sibling tools, providing clear context without needing to name them.

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?

The description adds behavioral context beyond the annotations by mentioning pagination, return fields (salary, company, location, tracked apply URL), and the live nature of listings. Annotations already convey read-only/idempotent safety, so the description supplements with useful operational details without being overly verbose.

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 three concise sentences that front-load the purpose, list key capabilities, and state the output format. Every sentence adds value 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?

Given the tool has 15 parameters and a rich schema, the description provides a sufficient high-level overview without needing to enumerate every filter. It covers the return format and scope, though it doesn't explicitly guide usage of list_locations/list_sectors for valid values (that's in schema descriptions). Overall, it's complete enough for effective tool selection.

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 all parameters are already documented. The description provides a high-level summary of filtering capabilities but doesn't add new meaning beyond the schema's per-parameter descriptions. It mentions 'recency' but doesn't map it to posted_since/updated_since explicitly, leaving some ambiguity.

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's purpose with a specific verb ('Search') and resource ('live recruiter and talent-acquisition job listings on Recruiter Roles'). It distinguishes itself from sibling tools by focusing on job search, while siblings handle companies, locations, sectors, or individual 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 provides clear context that this tool is for searching job listings and lists the supported filters, implying when it should be used. It doesn't explicitly mention alternatives or exclusions, but the purpose is unambiguous enough for an agent to determine applicability.

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