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

With no annotations, the description carries the burden. It discloses that the call lists Company Pages, verifying token validity, and is lightweight. This gives behavioral insight beyond the basic existence of the tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, both informative. No fluff. The first sentence states the action and result, the second adds the lightweight quality.

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 has no parameters and no output schema, but the description explains what it does and what it returns (list of Company Pages). It's fully sufficient for an agent to use this 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?

The input schema has zero parameters, so the baseline is 4. The description doesn't discuss parameters because there are none, and no additional meaning is required.

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

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

The description clearly states the tool's function: 'Verify the LinkedIn API token works and list the Company Pages the authenticated member administers.' This is a specific verb+resource pair and distinguishes it from sibling tools by framing it as a lightweight validation call.

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?

It provides context by stating 'Lightweight call to confirm credentials + access,' implying use when you need to validate authentication before other operations. It doesn't explicitly exclude alternatives but clearly positions its purpose.

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 provided, the description carries the transparency burden. It discloses the two behavioral modes and their outputs (demographic breakdowns vs organic/paid gains), which is valuable. However, it doesn't mention any permission requirements, rate limits, or potential errors, which would be expected for a complete behavioral picture. The description is moderately transparent but not exhaustive.

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 sentences, front-loaded with the primary purpose in the first few words. It packs mode differentiation and output expectations into a compact space without redundancy. Every clause adds value, making it highly concise and 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?

Given the tool's complexity (4 params, no output schema, no annotations), the description covers the essential behavioral modes and their outputs. It doesn't explicitly mention the organization_id parameter, but the schema covers that. The lack of output schema is partially compensated by describing the returned categories (seniority, function, industry, etc.). Still, a slightly richer description of the returned data structure would make it fully 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 provides 100% coverage of parameters, so the baseline is 3. The description adds semantic meaning by explaining how start_ms/end_ms and granularity relate to the two modes, e.g., 'Lifetime mode (omit start/end)' and 'Time-bound mode' returns gains per bucket. This goes beyond the individual parameter descriptions and helps the agent understand the interaction between parameters.

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 'Follower analytics for a Company Page', which clearly states the verb (analytics/get), resource (followers/Company Page), and scope. It distinguishes from sibling tools like get_page_statistics and get_share_statistics by focusing specifically on follower data. The two modes (lifetime and time-bound) further clarify what the 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?

The description provides explicit guidance on when to use lifetime mode (omit start/end) versus time-bound mode, which is effectively a usage rule for parameter configuration. It does not explicitly name alternative tools, but the context of follower analytics and the mode descriptions give sufficient clarity for an agent to determine appropriate use.

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

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

No annotations are provided, so the description carries the full burden. It clearly discloses that the operation is a 'Get' (implying read-only) and specifies the returned data fields. However, it does not explicitly state that the operation is safe or describe any potential side effects, though none are expected for a simple read.

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 concise sentence that front-loads the action and resource, followed by the key return fields. There is no wasted language, and every word contributes meaning.

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 no output schema and no annotations, the description satisfactorily explains what is returned (profile fields and follower count). It is complete enough for a simple tool with one optional parameter, though it could mention that there might be additional profile fields beyond those listed.

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 organization_id, which already explains the default behavior and expected format. The description adds no additional parameter meaning, so a 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 tool's purpose with a specific verb ('Get') and resource ('Company Page's profile') while also specifying exactly what is returned (name, vanity, description, total follower count). This efficiently distinguishes it from sibling tools like get_follower_statistics, which likely provides more detailed stats rather than a profile summary.

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 for fetching an organization's profile and follower count but does not provide explicit guidance on when to use this tool versus siblings such as list_organizations or get_follower_statistics. No exclusions or alternatives are mentioned, so the context is only 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?

With no annotations, the description carries the full burden of behavioral disclosure. It explains the available time scopes (lifetime vs time-bound), the breakdown dimensions, and the inclusion of demographics. This gives a clear picture of what the tool returns without exposing side effects, which are minimal since this is a read-only analytics call.

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 concise sentences, front-loaded with the core purpose ('Page view analytics for a Company Page'). Every word adds value—metrics, breakdowns, and time modes—with no filler 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?

Given that there is no output schema, the description does a good job explaining what the tool returns: views, unique visitors, sections, devices, and demographics, plus time-bound options. It's sufficient for an agent to invoke the tool and interpret common use cases, though it stops short of detailing the exact response structure.

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 already describes all four parameters with 100% coverage, so the description doesn't need to add much. It does reinforce that omitting start/end gives lifetime data and mentions DAY/MONTH granularity, but it doesn't go beyond the schema. The slight discrepancy of not mentioning WEEK is minor, but 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 tool's purpose: 'Page view analytics for a Company Page' with specific metrics (views, unique visitors) and breakdowns (section, device, demographics). This distinguishes it from sibling analytics tools like get_follower_statistics and get_share_statistics, which cover different metrics.

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 for when to use the tool: when page view analytics are needed. It mentions the lifetime or time-bound modes, giving an idea of query flexibility. While it doesn't explicitly exclude alternatives, the specific metrics and breakdowns make it obvious this is for page views, not followers or shares.

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 discloses that sponsored activity is excluded, clarifying the 'organic' scope, and explains the aggregate/time-bound behavior. It doesn't mention authentication or side effects, but as a read-only analytics tool, the absence is not a major 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?

Two concise sentences, front-loaded with purpose and metrics, no filler. The description efficiently covers what the tool does and its core usage modes.

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 stats tool with all optional parameters and no output schema, the description covers purpose, metrics, time-bound vs. aggregate behavior, and data exclusions. It does not explicitly describe the response structure beyond listing metrics, but this is an acceptable gap for an agent to select and invoke 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?

The schema covers 100% of parameters with descriptions. The description adds only slight clarification that start/end are omitted together for aggregate queries, largely restating schema info. It does not add meaning for granularity or organization_id 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 tool returns organic post/share analytics for a Company Page with specific metrics (impressions, clicks, etc.), and explicitly distinguishes itself by excluding sponsored activity. This differentiates it from sibling analytics tools like get_follower_statistics and get_page_statistics.

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?

It provides clear usage context by explaining the aggregate vs. time-bound modes through omitting or including start/end parameters. However, it does not explicitly name sibling tools or state when to use this tool instead of them, so it lacks explicit alternative 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?

With no annotations, the description carries the transparency burden. It discloses that only organizations 'the authenticated member can manage' are listed and that results include URNs/ids. But it does not describe pagination, ordering, or other behavioral aspects. Adequate for a simple list, 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?

The description is a single, front-loaded sentence that explains the purpose and provides a usage hint, with no wasted words.

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 list tool without an output schema, the description covers the essential points: what is listed, for whom, and how to use the results. It could mention more about the result structure but is sufficient given its simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so the baseline is 4. The description adds no parameter details, but none are needed due to the empty input 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 the tool lists Company Pages (organizations) the authenticated member can manage, with their URNs/ids. The verb 'List' and resource 'Company Pages' are specific and distinguish it from sibling tools like get_organization or get_follower_statistics.

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?

It provides clear context by stating 'Use these ids with the analytics tools', indicating when this tool should be used as a prerequisite. However, it does not explicitly mention when not to use it or compare with alternatives, so it stops short of full 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?

With no annotations, the description carries the burden. It discloses the response composition (text, timestamps, lifecycle, distribution) and suggests a follow-up action, but it doesn't mention pagination, authorization, or side effects. For a read-only list operation, this is moderate but incomplete.

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, purposeful sentences with no fluff. The first defines the output, the second gives a practical next step—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?

The description covers the core purpose and output composition, but it omits details about response shape, pagination behavior, and sorting semantics. Since there is no output schema, the description could be more informative about the return structure and the meaning of lifecycle/distribution.

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 count and organization_id with descriptions, but sort_by has no description in the schema. The tool description adds no parameter information, so it fails to compensate for the missing sort_by semantics. The description provides little value beyond the structured 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 uses the specific verb 'List' with the resource 'an organization's posts' and enumerates the included content (text, timestamps, lifecycle, distribution). This clearly distinguishes it from sibling tools like list_organizations or get_share_statistics.

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 workflow hint—'Take a returned post id and pass it to per-post share statistics for engagement'—which implies when to use this tool (as a precursor to share statistics). It does not explicitly name alternatives or exclusion scenarios, but the context is clear enough.

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 full burden. It discloses that the tool returns both access and refresh tokens, that it requires specific environment variables, and that values should be persisted into .env. It does not mention whether the old refresh token becomes invalid or any side effects, but for a simple token refresh operation, the key behaviors are covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exactly two concise sentences, front-loaded with the action and then the requirements. Every word earns its place, with no redundancy or filler.

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

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

Given zero parameters, no output schema, and a straightforward purpose, the description is sufficiently complete. It explains what it does, what it returns ('new values'), and what is required. It could be slightly more explicit about the return format (e.g., object keys), but that is not critical for this simple 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?

There are zero parameters, so the baseline is 4. The description adds value by referencing the necessary environment variables (LINKEDIN_CLIENT_ID, LINKEDIN_CLIENT_SECRET, LINKEDIN_REFRESH_TOKEN) which are the actual inputs, even though they are not part of the schema. This gives the agent essential context.

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: 'Mint a fresh LinkedIn access token (and refresh token)' with the resource being tokens. This distinguishes it from sibling tools, which are all about checking connections, listing organizations, or getting statistics. The verb 'mint' is specific and vivid.

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 when tokens need refreshing, though it does not explicitly say 'when to use' or compare with alternatives. It provides clear context by mentioning the configured refresh credentials and required environment variables, which are prerequisites. No exclusions are stated, but the tool's purpose is self-evident among siblings.

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