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

With no annotations, the description must carry the behavioral burden. It states the check itself but does not explicitly confirm it is non-mutating or describe the return format. The login-wall context implies safety, but the description lacks explicit boundary details. It does not contradict anything, but could be more transparent about side effects.

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, no filler. The purpose is stated first, and the second sentence adds valuable context about why this tool matters. 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 parameterless check with no output schema, the description fully covers what an agent needs: what the tool does and why it's important before other tools. It is complete and self-sufficient.

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, and the schema is already complete (100% coverage). Baseline is 4. The description adds no parameter information, which is appropriate since there are none to document.

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 a specific verb ('check') and resource ('whether the browser is signed in to LinkedIn'). The description distinguishes it from sibling tools by its unique purpose—session verification—and adds context about the login wall, making it unambiguous what this tool does.

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?

Explicitly states that 'every other tool needs this to be true,' implying it should be called before other LinkedIn operations. This provides clear usage context. It does not list exclusions or alternatives, but given its singular nature, the guidance is sufficient.

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 full burden. It transparently discloses the key side effect: it does NOT close the window or the saved session, and it indicates resource freeing. This goes beyond a simple 'close session' and prevents the agent from assuming destructive 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?

Two sentences with zero waste. The core action is front-loaded, and the clarifying sentence adds essential side-effect information without 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?

For a parameterless tool with no output schema, the description is sufficient. It covers what it does and what it doesn't do. One could add typical usage timing (e.g., 'call at end of session'), but that is minor and not strictly necessary for correct invocation.

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?

There are zero parameters, so schema coverage is trivially 100%. The description adds no parameter meaning because none exist; per rubric, a baseline of 4 is appropriate for a parameterless tool.

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: 'Disconnect from the browser and free resources.' It further clarifies by specifying it detaches from Chrome and leaves the window and saved session intact, distinguishing it clearly from the search/get/check siblings.

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 gives no explicit when-to-use or when-not-to-use guidance. It does not mention that it should be used after other LinkedIn operations or contrast itself with linkedin_check_session. The implication is present but not stated, leaving the agent to infer usage context.

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 must bear the burden. It discloses the output content, including the 'See more' clamp expansion, which is a concrete behavior. However, it does not mention prerequisites like authentication needs, rate limits, or error behavior. For a read operation, this is acceptable but not exhaustive, so a 3 is appropriate.

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 two efficient sentences. The first states the overall purpose, and the second lists the return fields with the clamp expansion noted. No fluff, front-loaded key info, and every sentence 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?

Despite having no output schema, the description lists the return values in detail, providing adequate information for an agent to know what to expect. It does not cover prerequisites like active sessions (sibling check_session exists) or error cases, but for a simple GET with clear return fields, it is nearly complete.

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% for the single parameter, already specifying it accepts a job ID or URL. The description itself does not add additional semantics beyond what the schema provides. Baseline 3 is correct given the schema's completeness.

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 retrieves full details for a single job posting and enumerates the exact fields returned (title, company, location, posted age, insights, complete description). This verb+resource focus distinguishes it from siblings like search, profile, and session tools.

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: it requires a job ID or URL to retrieve full details. The context is clear that this is for a single job, not searches or profiles, but it does not explicitly mention when not to use it or name alternatives. Still, for a straightforward GET tool, the usage context is unambiguous.

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 full burden. It transparently discloses the return content and the page-load trade-off, but omits any session/auth prerequisite or error behavior. This is partially transparent but not comprehensive.

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?

Three terse sentences, each earning its place: purpose, returns, and the parameter trade-off. The essential information is front-loaded with zero fluff.

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 read-only profile tool with no output schema, the description covers what's returned and the parameter option. It omits explicit session requirements, but sibling tools (check_session/close_session) imply that context, so it's not a critical 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?

Schema coverage is 100%, so parameters are documented. The description adds value for include_sections by explaining the decision rule and its result-size impact, and reinforces the profile format. This exceeds the baseline.

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 opens with a clear verb-resource pair ('Read a LinkedIn profile') and then lists the specific fields returned (name, headline, location, about, experience, education, skills). This unambiguously distinguishes it from sibling job-search and session tools.

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 purpose is explicit, and the include_sections guidance gives a concrete conditional ('Set include_sections to false when you only need to confirm who someone is') with a performance trade-off. It doesn't explicitly contrast with siblings, but the domain is distinct enough that this is sufficient.

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 discloses that results are paged 25 at a time and that salary may be absent, but it does not mention authentication/session requirements or rate limits. With no annotations provided, the description carries the full burden, and the existence of linkedin_check_session suggests a session prerequisite that is not mentioned. This is a notable gap.

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 about 100 words, front-loaded with the main purpose, and the second paragraph covers pagination. It avoids redundancy and each sentence contributes value, such as noting the paging limit and the alternative tool. No fluff or repetition.

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 description explains the return fields and pagination, and guides to linkedin_get_job, which covers essential operational details. However, it does not mention prerequisites like an active LinkedIn session (implied by check_session sibling) or error/edge-case behavior. Given the parameter count and no output schema, the description is fairly complete but misses these key operational aspects.

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 schema provides 100% description coverage for all 9 parameters, so the schema already documents their meaning. The description does not add additional parameter semantics beyond what the schema offers (e.g., it does not clarify how sort values map to behavior or the exact format of location). Baseline 3 is appropriate given the schema does the heavy lifting.

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 searches LinkedIn job listings, and explicitly distinguishes itself from the sibling linkedin_get_job by instructing to pass the returned job_id to that tool for full descriptions. This separates purpose from the retrieval sibling without opening the schema.

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 tells the agent to use linkedin_get_job for the full description rather than re-searching, which is a clear directive about when to use an alternative. It also implicitly distinguishes from profile/session tools by focusing on job search, so usage context is evident.

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