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

Discloses post creation behavior, character limit (3000), required scopes (w_member_social, w_organization_social), error handling (403, 400), and return type (post URN). Annotations indicate it's not read-only or destructive, and description aligns with no contradictions.

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?

Well-structured with front-loaded purpose, followed by supported types, scope, args, return, examples, and errors. Each section is concise and informative, no unnecessary 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?

Comprehensive for a 7-param tool with no output schema: explains all parameters, return value, error cases, prerequisites, and includes examples. Missing no critical behavioral or usage details.

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?

Adds significant value beyond schema: explains author_urn format and source, visibility enum meanings, optional parameters' conditions (e.g., article_url requires article_title/article_description), and provides examples. Schema coverage is 100% but description elaborates further.

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?

Description clearly states it creates a new LinkedIn post for a member or organization, specifying supported post types (text, image, article). Distinguishes from sibling tools like linkedin_delete_post, linkedin_update_post, and linkedin_upload_image.

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?

Provides context on when to use this tool (creating posts) and mentions prerequisites like uploading images via linkedin_upload_image and required scopes. Does not explicitly contrast with sibling tools like update or delete, but the examples and structure implicitly guide usage.

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 annotations (destructiveHint=true), the description adds critical context: permanence, author-only restriction, error codes (403/404), and required scopes. This fully informs the agent of behavioral traits.

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 concise, using structured sections (Args, Returns, Error Handling) and front-loading key facts. Every sentence adds value 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 simple one-parameter tool with no output schema, the description fully covers purpose, preconditions, side effects, and error handling. It is contextually 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?

The input schema already covers post_urn with a description and example. The description's Args section repeats the same information, adding no new semantics beyond the schema. Schema coverage is 100%, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool permanently deletes a LinkedIn post by its URN, which is a specific verb and resource. It distinguishes itself from sibling tools like linkedin_update_post or linkedin_create_post by being the only delete operation.

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 specifies that only the author can delete, the action is irreversible, and requires specific OAuth scopes. While it does not explicitly mention alternatives, the destructive nature and unique purpose make usage 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?

Beyond annotations (readOnlyHint, idempotentHint), the description discloses critical behavioral details: auth scope (rw_organization_admin), how public vs admin access differs, return fields, and error codes (403, 404). No contradiction 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?

The description is well-structured with logical sections: summary, access-level details, Args, Returns, Examples, Error Handling. Every sentence adds necessary information without redundancy. It is front-loaded with the core purpose and then elaborates.

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 complexity (4 params, no output schema), the description covers all aspects: parameter explanations, output format options, examples, error conditions, and scope requirements. It leaves no significant gap for an agent to misinvoke the 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?

Although schema coverage is 100%, the description significantly adds value by explaining parameter dependencies (e.g., organization_id required when lookup_by='id'), providing concrete examples, default values, and contextual usage like 'vanity_name' from URL. This goes well beyond the schema definitions.

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 'Retrieve a LinkedIn organization (company) page by its numeric ID or vanity name.' It identifies the specific resource and action, and distinguishes from sibling tools like linkedin_create_post or linkedin_get_org_follower_count by focusing on retrieval of organization details.

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 guidance on when to use the tool (to get organization info) and what to expect with different access levels. It mentions required scope and error handling, but could explicitly state not to use it for follower counts or posts, though sibling names make that implicit.

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?

Discloses required scope (rw_organization_admin), error codes (403, 404), and follows annotations (readOnlyHint, idempotentHint). No contradictions; adds context beyond 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?

Description is well-structured with sections for introduction, scope, args, returns, examples, and error handling. Each sentence adds value, and the most critical information is 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?

Covers scope, parameters, return format, error codes, and usage examples. However, it omits potential details like rate limits or data freshness, but given the tool's simplicity, it is largely 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?

The schema covers the single parameter with full description. The description repeats the same information (e.g., 'Numeric organization ID') without adding new meaning or clarification 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 clearly states it retrieves the follower count for a LinkedIn organization. The verb 'get' and resource 'follower count' are explicit, and it distinguishes itself from siblings (e.g., linkedin_get_organization) by focusing solely on followers.

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?

Provides concrete usage examples and error handling scenarios. While it lacks explicit 'when not to use' guidance, the examples clearly illustrate appropriate contexts such as checking or comparing follower counts.

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 indicate read-only and idempotent behavior. The description adds valuable context: scope requirements, error cases (403), and the return structure. It fully discloses behavioral traits beyond what annotations provide.

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 well-structured with clear sections (purpose, scopes, args, returns, error handling). Each sentence adds value, though it is somewhat verbose. It is front-loaded and organized, earning a high 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?

Given the tool's two parameters, no output schema, and the need for special scopes, the description covers all necessary aspects: purpose, usage, parameters, return format, and error handling. It is complete for an AI 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 coverage is 100%, providing baseline 3. The description adds meaning beyond the schema with concrete examples like 'urn:li:share:7123456789' and clarifies response_format as 'human-readable or machine-readable', justifying a higher score.

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 'Retrieve a LinkedIn post by its URN.' This is a specific verb and resource, and it effectively distinguishes the tool from its siblings like linkedin_create_post and linkedin_list_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 explicitly covers when to use the tool, required scopes, and provides an alternative: 'If you only have w_member_social, use linkedin_create_post and linkedin_list_posts instead.' It also includes error handling for 403.

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 annotations (readOnlyHint, idempotentHint, openWorldHint), the description details the authentication requirements (scopes), error cases (missing token, permission issues), and the exact structure of the response. 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?

The description is well-structured with clear sections (Args, Returns, Examples, Error Handling) and front-loaded with the core purpose. It is slightly verbose but every section adds value, making it efficient for an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple parameter set, the presence of annotations, and no output schema, the description is remarkably complete. It includes the endpoint, scopes, all return fields, error handling, and example usage, leaving no critical gaps.

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%, and the description adds context by explaining the default value and providing usage examples for both formats. While the schema already defines the parameter, the description enhances it with practical guidance.

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 the authenticated LinkedIn member's profile information via the OIDC userinfo endpoint, listing specific fields returned. It is easily distinguished from sibling tools like linkedin_create_post or linkedin_get_organization, which have different purposes.

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 when-to-use examples are provided (e.g., 'Who am I logged in as?'), a 'Don't use when' exclusion for looking up others' profiles, and required OAuth scopes are listed. This gives clear guidance on appropriate usage.

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 indicate read-only, idempotent, not destructive. The description adds substantial value: scope requirements, error handling (403), return structure with pagination fields (total, count, offset, has_more, next_offset), and output format options.

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 well-organized into sections (NOTE, Requires scope, Args, Returns, Error Handling). Each section is concise and informative without being bloated. A bit longer than ideal but well-structured.

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 tool with 4 parameters, no output schema, and no nested objects, the description covers all necessary aspects: prerequisites, parameter details, return format with pagination, and error handling. It is complete and self-contained.

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 baseline is 3. The description adds contextual meaning beyond schema: it clarifies scope dependency for author_urn, documents default values and constraints for limit/offset, and explains response_format purpose.

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 'List posts by a LinkedIn member or organization' — a specific verb and resource. It distinguishes from sibling tools like create, delete, get, and update posts by its listing 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 explicitly mentions required scopes (r_member_social, r_organization_social) and admin status for org posts. It does not discuss when not to use, but the prerequisites are clear and helpful.

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?

Adds significant context beyond annotations: author constraint, non-modifiable fields, scope requirements, error codes. No contradiction with readOnlyHint=false.

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?

Well-structured with sections (description, requirements, args, returns, errors). Slightly long but each sentence adds value. Could be slightly more concise.

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 3 params and no output schema, description covers inputs, errors, permissions, and success response. Leaves no critical gaps for agent 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?

Schema coverage is 100%, baseline 3. Description adds value by explaining post_urn format, text length, and visibility options including default behavior when omitted.

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?

Description clearly states it updates text or visibility of an existing post. Distinguishes from siblings like create_post and delete_post through the verb 'update' and specific resource.

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 only the author can update, and notes limitations on attachments. Mentions required scopes. Could add direct comparison to siblings like 'use this instead of deleting and re-creating'.

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?

Discloses the upload process (two steps), required scope w_member_scope, and error handling (403, 400). Adds behavioral context beyond annotations (which only indicate non-readonly, non-destructive, open-world). 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?

Well-structured with clear sections (description, process, args, returns, examples, errors). Every sentence is informative and 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?

Comprehensive coverage of return values, error scenarios, and prerequisites despite no output schema. The two-step upload complexity is fully described, making it complete for agent 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?

With 100% schema coverage, description adds value by specifying author_urn format from linkedin_get_profile, base64 encoding, and default mime_type. Examples and return shape further clarify parameter usage.

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 tool uploads an image to LinkedIn and returns an image URN for use in creating an image post. Distinguishes from sibling linkedin_create_post by explaining it's a prerequisite step.

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 provides when-to-use ('Post an image to LinkedIn') and when-not-to-use ('only a text post'), names the alternative, and describes the two-step process handled automatically.

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