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

The destructiveHint annotation already signals this is a destructive action, and the description confirms it by saying 'Close' and 'clean up resources'. It adds some scope detail ('current browser session') but does not describe side effects such as invalidating cookies, logging out the user, or making the session unusable. Since the annotations carry much of the safety signal, a mid-range score 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 entire description is a single, front-loaded sentence with no redundant words. It states the action and consequence efficiently, earning a top score for conciseness.

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, destructive tool with an output schema and annotation coverage, the description is complete enough for an agent to know what will happen. The absence of an alternative, zero parameters, and existing destructiveHint mean no additional operational context is essential.

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 empty with 100% coverage, so there are no parameter semantics for the description to clarify. With no params, the baseline is 4, and the description does not need to add parameter-level explanation.

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 ('Close') and names the resource ('current browser session') plus a clear secondary purpose ('clean up resources'). It is well-distinguished from all sibling tools, which focus on profiles, searches, messaging, and feed actions, not session lifecycle.

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 clear context that this tool ends the current browser session, implying it should be used when the session is no longer needed. There are no sibling tools providing an alternative session-close action, so explicit exclusions are unnecessary. A fully explicit 'use this when you are done' statement would improve clarity, but the context is already 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?

Beyond the destructiveHint annotation, the description explains what 'destructive' means here: the comment is public and permanent, and it will not run without explicit confirmation. It also discloses dry-run behavior, that no draft text is left behind, and the real risk of LinkedIn restrictions from near-duplicate comments. All of this adds meaningful behavioral context beyond 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?

The description is front-loaded with the core purpose, then each subsequent sentence earns its place: safety model, dry-run workflow, and spam-risk warning. It is longer than a one-liner but every section adds actionable information, with no filler or repetition of schema details.

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 input schema is fully covered, an output schema exists, and the annotations flag destructiveness, the description adds exactly the missing operational context: how confirmation gates publishing, what the dry run does, and the behavioral consequences of misuse. An agent has enough to invoke the tool safely and 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%, and the schema already documents all three parameters thoroughly, including URL formats and the confirm_comment boolean semantics. The description reinforces these points but does not add substantial new meaning beyond what the parameter descriptions already state, 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 opens with 'Post a first-level comment on a LinkedIn post', which names a specific verb, resource, and scope. It clearly distinguishes this from sibling tools like send_message, connect_with_person, or get_feed, and the qualifier 'first-level' adds precision about the kind of interaction.

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 concrete usage guidance: confirm_comment=False performs a dry run to verify the comment box exists before committing, and the tool never runs without confirm_comment=True. It does not explicitly name alternatives or say when not to use this tool, but the practical workflow (dry run first, then confirm) is clearly conveyed.

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 mostly restates the destructiveHint annotation by saying MCP clients will prompt for confirmation. It does not add meaningful behavioral context beyond that, such as irreversibility, notification to the recipient, authentication requirements, or rate-limit implications.

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 short and front-loaded with the primary action. The second sentence about destructiveHint is slightly redundant with the annotations, but it communicates the user-confirmation behavior clearly and does not add much bulk.

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 two well-documented parameters and an output schema present, the description is mostly sufficient for invoking the tool correctly. It lacks explicit guidance on when to accept versus send, or on side effects, but these are not critical given the schema and annotations.

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 parameter docs already explain linkedin_username and note in detail. The description adds no additional parameter-level meaning beyond what the schema 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 clearly states the specific action: send or accept a LinkedIn connection request. It names the exact resource and distinguishes it from sibling tools like send_message or search_people.

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 makes it clear this tool handles connection requests and acceptances, which provides usable context for when to choose it. It does not explicitly name alternatives or exclusions, but the action is specific enough to avoid major ambiguity.

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 openWorldHint=true, so the description does not need to restate safety. It adds meaningful behavioral context: the data comes from the /people/ page, demographics are unique to this view, and the company_name must be the exact URL slug, not the display name, with a concrete example. This goes beyond 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?

The description is moderately long but every sentence earns its place: core purpose first, then usage routing, then keyword semantics, then the critical slug caveat. The Anthropic example is concrete and high-value, not filler. Structure is logical and front-loaded.

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 exists, annotations cover safety, and the description covers purpose, routing, param semantics, and a caveat with an example, the definition is complete for an agent to select and invoke the tool correctly. No critical gap is evident.

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%, but the description adds substantial semantic value beyond the schema. It clarifies that company_name must be the exact LinkedIn URL slug, warns that display names often differ, gives the Anthropic example, and tells the agent to resolve ambiguity via search_companies. This is exactly the kind of parameter nuance that prevents incorrect calls.

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 'List employees at a company from the LinkedIn /people/ page' and uniquely identifies its value-add: the demographics aggregate (location, education, function breakdown). It distinguishes itself from siblings by noting these demographics are unique to this tool, so an agent can immediately tell it apart from search_people or get_company_profile.

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?

Explicit guidance is provided for when to use this tool vs alternatives: for filtered search by network degree or location, prefer search_people with current_company set. It even notes search_people returns more result pages, and instructs the agent to call search_companies first if the slug is uncertain. This is strong, actionable routing guidance.

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 and openWorldHint, so the description only needs to add behavioral context beyond those. It adds the 'recent' scoping and company-feed source, but says nothing about pagination, ordering, result limits, or how recent the posts are. This is adequate but not rich.

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 a single, front-loaded sentence with no wasted words. It conveys the core action and resource clearly in minimal space.

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-only tool with an output schema and strong schema parameter documentation, the description covers the essential invocation context. It lacks sibling differentiation, but that is already reflected in the usage dimension; overall, nothing critical is missing for basic correct usage.

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%, and the schema itself explains company_name with examples and URL-slug normalization. The tool description adds no additional parameter semantics, 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 states a specific verb and resource: 'Get recent posts from a company's LinkedIn feed.' It clearly identifies the tool's scope as company posts, though it does not explicitly contrast it with related post/feed tools like get_feed or search_posts.

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?

No guidance is provided on when to use this tool versus alternatives such as get_feed, search_posts, or get_company_profile. The usage context is only implied by the name and description, with no exclusions or routing hints.

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 and openWorldHint=true, covering the safety and live-data aspects. The description itself adds little behavioral context beyond the schema's mention of 'scrape', and it does not disclose potential scraping limitations, authentication needs, or rate-limit behavior. No contradiction with annotations exists.

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 a single, direct sentence with no filler or redundancy. It is concise, though it mostly restates the tool name and relies on the schema for substantive detail.

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 read-only tool with two well-documented parameters and an output schema, the definition is mostly complete. The main missing piece is usage guidance around sibling tools, but that gap is already captured in the usage_guidelines dimension.

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%, and both parameters are already well documented: company_name accepts a slug or full URL, and sections specifies comma-separated extras with defaults and examples. The description adds no additional parameter meaning beyond what the schema 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 states a specific verb ('Get') and resource ('a specific company's LinkedIn profile'), which clearly distinguishes it from sibling tools like get_person_profile or search_companies. The word 'specific' also signals that this tool targets one known company rather than returning a list.

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 no explicit guidance on when to use this tool versus alternatives such as search_companies, get_company_posts, or get_company_employees. It does not mention that search_companies should be used when the exact company slug is unknown, nor does it note that get_company_posts is the tool for post-specific data.

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 only openWorldHint in annotations, the description carries the burden of disclosing side effects. It explicitly warns that username-based resolution click-visits matching rows and 'may mark it as read,' which is a non-obvious behavioral trait an agent must know before invoking the tool. It also explains the LinkedIn UI limitation that forces this 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?

Three sentences, zero filler. The core purpose is front-loaded, and the longer second sentence earns its length by explaining a critical behavioral caveat and its root cause. The final sentence gives a clear escape hatch. No word is wasted.

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 an output schema present, return-value documentation is already handled. The description covers all invocation paths, parameter interactions, side effects, and a rationale for the unusual behavior. An agent has everything needed to call this tool correctly, including how to avoid the side-effectful path.

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 the baseline is 3. The description adds meaningful parameter context: explaining that linkedin_username triggers a UI enumeration process, that index selects among multiple threads, and that thread_id bypasses the enumeration. This goes beyond the schema's dry definitions without being redundant.

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 specific verb and resource: 'Read a specific messaging conversation.' It then details two unambiguous lookup modes, making it clear this tool targets one conversation rather than inbox-wide operations, which distinguishes it from siblings like get_inbox and search_conversations even without naming them.

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 clear guidance on when to use each input path: pass thread_id to avoid the expensive username enumeration, and use linkedin_username when you don't have a thread_id. It does not explicitly state when to prefer this tool over sibling tools, but the read-specific-conversation scope is strongly implied.

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 does not disclose behavioral traits beyond the annotations: it only says 'Get posts', which is consistent with readOnlyHint=true but adds no new behavioral context. Notable behavior like batched loading and count overshooting appears only in the schema property description, not in the tool description.

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 short sentence with no filler, front-loading the action and resource. It is concise without sacrificing clarity.

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 read-only feed fetch with one optional parameter and an output schema, the description plus schema provide all necessary information. The feed scope is clear, and nothing critical is missing.

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%, with the num_posts parameter fully documented including default, range, and batch behavior. The tool description itself adds no parameter detail, but the schema already carries the full semantic burden, 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?

States a specific verb 'Get' and a specific resource 'posts from the authenticated user's LinkedIn feed', which clearly distinguishes it from sibling tools like get_company_posts and search_posts. The phrase 'authenticated user' pins the scope unambiguously.

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 intended use — fetching the current user's LinkedIn feed — is reasonably implied by the description, but there is no explicit guidance about when to choose this over alternative tools such as search_posts or get_company_posts. No exclusions or routing hints are provided.

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 carry the safety profile (readOnlyHint=true) and the volatility expectation (openWorldHint=true), lowering the bar. The description adds modest behavioral value by specifying a recency ordering and inbox scope, but it does not disclose pagination behavior or whether ordering is guaranteed beyond the word 'recent.'

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?

One sentence with zero filler: the verb is front-loaded, the resource and scope are named immediately, and no structured information from the schema or annotations is redundantly repeated.

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 list tool with one fully documented optional parameter, an output schema, and safety annotations, almost nothing an agent needs to call it is missing. The only slight gap is pinning down the meaning of 'recent' and the ordering guarantee, which is minor at this complexity level.

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%: the single parameter (limit) has its type, numeric range, default, and meaning fully documented in the schema. The description contributes no parameter-level detail, so the baseline 3 for fully covered schemas 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 states a specific verb ('List') and a well-defined resource ('recent conversations from the LinkedIn messaging inbox'). It distinguishes cleanly from the sibling tools get_conversation (a single thread's messages) and search_conversations (query-driven search), so an agent can tell them apart without opening schemas.

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 context: this is the default, recency-ordered browsing entry point for inbox conversations. However, it never names the alternatives or states when not to use it — especially relevant given the closely related search_conversations and get_conversation siblings — so the routing decision is left to inference.

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 openWorldHint=true, so the description does not need to restate basic safety. The description adds no meaningful behavioral context beyond the annotations, such as what kind of details are returned or whether the job must be publicly accessible. It is consistent with the annotations, so no contradiction.

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 short sentence with no filler. It front-loads the action and resource, and 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 tool's simplicity—one parameter, full schema coverage, output schema present, and read-only annotations—the description is nearly complete. The only minor gap is lack of explicit guidance on when to choose this over search_jobs, but the 'specific job posting' wording covers the core usage 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 schema has 100% coverage for the single parameter, including an example format for job_id. The description does not add additional parameter meaning beyond what the schema already provides, but with full schema coverage the baseline of 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 states the action ('Get'), the resource ('job details'), and the scope ('for a specific job posting on LinkedIn'). It distinguishes itself from sibling tools like search_jobs and get_saved_jobs by emphasizing a specific job ID rather than searching or 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 phrase 'for a specific job posting' implies this tool should be used when you already have a job_id and need details, as opposed to searching for jobs. However, it does not explicitly state when not to use it or name alternative 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 and openWorldHint, so the safety profile is covered. The description adds valuable behavioral context by explaining that the tool navigates to /in/me/, follows the redirect, and returns the canonical profile URL instead of the /in/me/ placeholder.

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 the core purpose in the first sentence, and the second sentence adds a single high-value behavioral detail about redirect resolution. There is no filler, repetition of schema content, or tangential information.

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 low complexity, full schema coverage, and presence of an output schema, the description provides everything needed to invoke the tool correctly. The redirect-resolution note closes the only likely source of confusion about the returned url field.

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%, and the input schema already documents sections and max_scrolls with defaults, examples, and constraints. The tool description does not add additional parameter meaning, so the baseline score of 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 states the tool gets the authenticated user's own LinkedIn profile, using a specific verb and resource. It also distinguishes itself from sibling tools like get_person_profile by emphasizing 'own' profile, and adds a concrete detail about resolving /in/me/ to the real 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?

The description clearly implies this tool is for the authenticated user's own profile, which differentiates it from get_person_profile. It does not explicitly name alternatives or enumerate when-not-to-use, but the context is clear enough for an agent to select it appropriately.

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 declare readOnlyHint=true and openWorldHint=true, so the safety and external-data nature are already known; the description adds no contradiction. The description itself does not reveal behavioral details like scraping behavior or result shape, but the rich parameter descriptions cover pagination and sections, so this is adequate.

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 a single sentence with no filler words. It front-loads the core purpose and does not repeat information already in the schema or annotations.

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 and the schema plus output schema provide substantial detail about sections, pagination, and return values. The description could be slightly richer by mentioning that the main profile page is always included, but this is already in the sections parameter description, so nothing critical is missing.

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%, with detailed descriptions for all three parameters: linkedin_username includes examples and URL normalization, sections lists all options and defaults, and max_scrolls explains its behavior and defaults. The main description adds no parameter-level meaning, but the baseline of 3 applies because the schema already 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 states a specific verb ('Get') and resource ('a specific person's LinkedIn profile'), making the operation unambiguous. It clearly distinguishes this tool from siblings like get_my_profile (own profile) and get_company_profile (company profile) without needing to read 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?

There is no guidance on when to use this tool versus alternatives such as search_people, get_sidebar_profiles, or connect_with_person. The description only states what the tool does, not the conditions that make it the right choice, leaving selection to inference.

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 goes well beyond the readOnlyHint and openWorldHint annotations by disclosing the actual mechanism: navigating to /activity/all/, scrolling for SDUI/RSC payloads, returning unique permalinks with most-recent-first ordering, and providing the exact URL shape. This gives the agent accurate expectations of what the tool does and how it behaves.

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 one-sentence purpose, then gives compact, relevant mechanics: the page path, loading behavior, count semantics, ordering, and URL format. Every sentence earns its place and there is no redundant 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?

Given the tool's moderate complexity, the presence of an output schema, and the readOnly/openWorld annotations, the description covers all essential context: what page is visited, how results are gathered, the max count, ordering, URL form, and downstream usage. An agent has enough 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 coverage is 100%, so the schema fully documents both username and count. The description reinforces count's meaning with 'up to *count* unique post permalinks' and adds ordering context, but it does not add substantive parameter-level detail beyond what the schema already 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 opens with a specific action and resource: 'Resolve canonical post permalinks from a profile's recent activity.' It clearly distinguishes this from sibling tools like get_person_profile and search_posts by emphasizing canonical permalinks and direct use by comment_on_post.

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 a clear use case: when you need canonical, recent post URLs from a specific profile for commenting. It names the downstream consumer (comment_on_post) and limits scope to a profile's activity page. However, it does not explicitly state when not to use this tool or name alternatives, so it falls just short of full routing guidance.

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 declare readOnlyHint and openWorldHint, so the safety profile is already covered. The description adds value by clarifying that the result is limited to the authenticated user's saved job postings and that it returns job_ids, not full job details—an important behavioral distinction from get_job_details. No contradictions with 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 first sentence states the action and scope, and the second sentence provides the important downstream usage detail about feeding job_ids into get_job_details. 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 simple read-only list tool with one optional parameter and an output schema, the description is complete. It identifies the authenticated-user scope, the return value, and the logical next step. The annotations cover read-only and open-world behavior, and the schema covers the only parameter. Nothing essential is missing.

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 input schema already fully documents max_pages with type, default, range, and description, so the description does not need to repeat it. The tool has only one optional parameter, and the schema coverage is 100%. The description adds no parameter-specific meaning, but none 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 ('List') and a clear resource ('job postings saved by the authenticated LinkedIn user'), which precisely distinguishes this tool from the sibling search_jobs and get_job_details. It also states the key output type (job_ids) and how they connect to another tool.

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 the right usage context: it is for the authenticated user's own saved jobs, not general job search. It also gives explicit guidance that the returned job_ids should be passed to get_job_details for full information, which effectively routes the agent to the correct follow-up tool. It does not explicitly list when not to use it, but the context is clear.

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 meaningful behavioral detail beyond the readOnlyHint and openWorldHint annotations: it follows 'Show all' links to get full lists and skips sections that redirect to linkedin.com/premium. This helps the agent understand what the tool will and will not return without inventing expectations.

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 compact and front-loaded with the core purpose, followed by two sentences of genuinely useful behavior details. Every sentence earns its place, with no filler or repetition of schema information.

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-only tool with an output schema and safe annotations, the description is complete. It covers what sections are scraped, how 'Show all' behavior is handled, and which sections are skipped, so an agent has enough context 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?

Schema description coverage is 100%, and the parameter linkedin_username is already well documented in the schema, including the accepted full-URL format. The description only restates that the parameter identifies the profile page to scrape, so it adds little semantic value beyond the schema.

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 action and resource: 'Get profile links from sidebar recommendation sections on a LinkedIn profile page.' It also names the concrete sections ('More profiles for you', 'Explore premium profiles', 'People you may know'), which clearly distinguishes it from broader sibling tools like get_person_profile or search_people.

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 implies when to use the tool: when sidebar recommendation profiles are needed from a LinkedIn profile page. However, it does not explicitly contrast this with alternatives or state when not to use it, so the agent is left to infer the boundary between this and other profile-related 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?

The description discloses no behavioral traits beyond what 'search' and 'LinkedIn' naturally imply. Annotations already declare readOnlyHint and openWorldHint, but the description adds no further context about result limits, matching behavior, or ordering. It is not contradictory, but it is also not informative beyond the basic action.

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 a single compact sentence that wastes no words and immediately states the core action and target resource. It is appropriately sized for a simple tool.

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 one-parameter tool with an output schema and safety annotations, the description is minimally adequate. However, it does not clarify edge behavior, result scope, or how it differs from nearby company-related tools, so an agent may still need to infer intent.

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 input schema already fully documents the only parameter, keywords, with a useful example. The description adds no additional semantic detail beyond the schema, so a baseline score of 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 verb ('Search') and resource ('companies on LinkedIn'), so an agent can understand the basic function. However, it does not differentiate itself from sibling tools like get_company_profile or search_people, leaving some ambiguity about scope or result type.

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?

There is no guidance on when to use this tool instead of search_people, get_company_profile, or get_company_employees. The description implies keyword-based discovery but provides no explicit context, prerequisites, or 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 only annotation is openWorldHint, so the description carries most of the behavioral disclosure burden. It does not mention that enumerating results may mark conversations as read (noted only in the limit parameter description), nor does it disclose result-shaping behavior. 'Search' implies a read operation, but side effects are hidden.

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 a single compact, front-loaded sentence with no filler. It is easy to parse, but it is so terse that it contributes little beyond the tool name and parameter names, making it less effective than it could be.

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 two-parameter tool with an output schema and fully described parameters, the description is minimally adequate for invocation. However, it lacks usage guidance, scope clarification, and behavioral disclosures, so an agent cannot fully judge when to use it or what side effects may occur.

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 input schema already fully documents keywords and limit. The description adds no new parameter semantics beyond restating the keyword filter; it does not provide format details, examples, or constraints beyond what the schema offers.

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 action (search) and resource (messages), making the tool's core purpose clear. It is somewhat distinct from sibling search tools by focusing on messages, but it does not explicitly differentiate itself from related tools like get_inbox, get_conversation, or search_posts.

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 guidance on when to use this tool versus alternatives such as get_inbox, get_conversation, or search_posts. There is no mention of scope, prerequisites, or situations where another tool should be preferred.

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 mark this as read-only and open-world. The description adds value by stating that the tool returns job_ids that serve as inputs to get_job_details, which is useful contextual behavior. It does not discuss pagination, max_pages behavior, or filtering limits, but the schema covers those details.

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 sentences with no filler. The primary purpose is front-loaded, and the follow-up guidance about job_ids is immediately useful. 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?

For a search tool with a rich, fully documented schema and an output schema present, the description is sufficient: it states the purpose and the relationship to get_job_details. It does not need to explain parameters or return values in depth because those are already structured elsewhere.

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 the heavy lifting is already done by the input schema. The description adds no additional meaning about parameters beyond what the schema documents, matching the baseline for full schema coverage.

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 a specific action ('Search') on a specific resource ('jobs on LinkedIn') and adds the key behavioral result: it returns job_ids intended for get_job_details. This differentiates it from sibling search tools like search_people and search_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 provides clear context: use this tool when you want to search LinkedIn jobs, and use the returned IDs with get_job_details for fuller information. It does not explicitly list exclusions or compare against alternatives like get_saved_jobs, but the intended use case is obvious.

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 cover the read-only and open-world nature of this tool. The schema adds a useful caveat that plain company names are silently ignored by LinkedIn's currentCompany filter, but the main description discloses no additional behavioral traits such as result limits, pagination, or network-scope behavior. This is adequate but not rich.

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 a single front-loaded sentence with no wasted words. It is appropriately concise, though it sacrifices useful context that could have been included without much bloat, keeping it just below a top score.

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 schema fully documents all parameters, annotations cover safety and open-world behavior, and an output schema exists, so the definition is mostly sufficient for invocation. The main gap is the lack of explicit sibling differentiation in the description itself, but the tool name and parameter guidance compensate.

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 parameters are already well documented and the baseline is 3. The tool description itself adds no parameter-level meaning. The detailed current_company guidance lives in the schema, not in the description.

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 a verb and resource: it searches for people on LinkedIn. This distinguishes it from other search tools like search_companies and search_jobs, though it does not explicitly contrast it with person-related siblings such as get_sidebar_profiles or get_person_profile.

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 a clear use case: finding people. The current_company parameter description adds valuable routing guidance, directing agents to get_company_profile for URN lookup and get_company_employees for company-wide demographics. However, the tool description itself does not provide a broader when-not-to-use statement versus other people lookup 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?

With readOnlyHint and openWorldHint already present, the description adds useful context: this is a global content search, not a feed or company-scoped search. It also implies results may include informal/unofficial hiring language, which is relevant behavioral context beyond 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?

The description is compact and front-loaded: one sentence defines the action, the next gives a concrete use case, and the final sentence distinguishes it from siblings. Every sentence earns its place with 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?

Given the 100% parameter schema coverage, the presence of an output schema, and annotations covering safety and openness, the description covers all essential decision factors. It tells the agent what the tool does, when to use it, what alternatives exist, and how to craft effective queries.

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 the baseline is 3. The description adds value by providing richer keyword examples and clarifying that content search is global rather than scoped, supplementing the schema's parameter descriptions. It meaningfully supports keyword selection without redundancy.

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 clear verb and resource: 'Search LinkedIn posts/content globally by keyword.' It also explicitly differentiates itself from get_feed and get_company_posts, so an agent can immediately identify what this tool is and what it is not.

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 a concrete use case—catching informal hiring posts before formal job listings exist—and provides example keyword patterns. It also names the two sibling alternatives and explains why they are different, making the selection decision explicit.

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 explicitly warns that this is a write operation when confirm_send is True, which adds useful side-effect context beyond the destructiveHint annotation. It also states the recipient must be directly messageable, giving the agent an important precondition. No contradiction with annotations exists.

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 short and front-loaded with the core purpose, followed by the key prerequisite and side-effect warning. Every sentence earns its place, and there is no redundant 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?

Given the destructiveHint annotation, an output schema, and full schema coverage, the description is nearly complete. It communicates the prerequisite and the guarded write behavior, though it could briefly mention that messages are not necessarily retrievable afterward or suggest a verification path, which the schema partially does via search_conversations.

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 parameters well. Baseline 3 applies because the description adds no extra parameter meaning beyond what is in the schema; however, the schema descriptions themselves are informative, especially for profile_urn and confirm_send.

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 sends a message to a LinkedIn user, which distinguishes it from connection requests, comments, and profile lookups. It does not explicitly contrast with sibling tools, but the action and resource are specific enough for an agent to understand the core function.

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 a key prerequisite: the recipient must be directly messageable from the profile page, and it notes that confirm_send controls whether the message is actually sent. It does not explicitly state when to use this over connect_with_person or comment_on_post, but the parameter descriptions offer some contextual guidance like using get_person_profile for the URN and search_conversations as a fallback.

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