Fractional Teams Portal

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

Annotations already mark this as read-only, idempotent, and non-destructive, and the description reinforces that with 'Read-only'. It adds useful behavioral context beyond annotations, including the requirement for the aeo section, the weekly/on-request scan cadence, and the conformance standard version.

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, stating the core purpose in the first sentence. It then efficiently lists the key output components and closes with the scope requirement and read-only cadence; 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?

With no output schema, the description responsibly explains what the tool returns: score, grade, layer breakdown, ranked fix list, and conformance reading. Combined with full schema coverage and safety annotations, the description gives an agent enough to invoke the tool correctly and understand its result.

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 documents all three parameters with complete descriptions, including the engine enum, target domain filter, and history count. The tool description adds little beyond the schema, so a baseline score of 3 is appropriate since the schema carries the semantic weight.

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 a specific resource: the latest Fractional Teams agent-readiness scan per client domain, and lists the major result components. However, it does not explicitly distinguish this tool from the very similarly named sibling get_agent_readiness_scan, so an agent may not know which one to pick.

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 contextual guidance: it requires the aeo section in scope, and it is read-only with a defined update cadence. It does not explicitly state when to prefer this tool over alternatives like get_agent_readiness_scan or get_report, but it gives enough context for reasonable 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?

Annotations already mark this as read-only and idempotent, so the bar is lower. The description adds useful behavioral context: it enumerates the returned content (summary, every check, status, points, finding, recommendation, and evidence including URLs and snippets) and discloses the AEO scope requirement. No contradiction exists between the description and 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 carry all necessary information: the first front-loads the tool's primary purpose and output contents, and the second covers scan_id provenance and the AEO scope prerequisite. There is no filler, repetition, or unnecessary 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?

With no output schema, the description must explain return values, and it does so thoroughly by listing the full scan summary, per-check details, and evidence types. It also provides the required input source and a scope precondition, making the tool fully actionable for a one-parameter read-only operation.

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

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

Schema description coverage is 100%, so the baseline is 3. The schema already documents scan_id and its source, and the description only repeats that provenance without adding new parameter semantics. There is no gap for the description to compensate for.

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 ('one agent-readiness scan'), and it disambiguates from the sibling list tool by specifying 'one ... in full' and noting that scan_id comes from get_agent_readiness. This clearly tells an agent what this tool returns and how it differs from the related summary/list 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?

It gives clear usage context: use this after obtaining scan_id from get_agent_readiness, and it flags a precondition ('Requires the aeo section in scope'). It does not explicitly list when not to use it, but the scan_id provenance note is strong enough guidance for selecting this tool over alternatives.

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 behavioral disclosure burden. It reveals meaningful scoping behavior: the required outreach scope and the visibility limitation for own-leads-only keys. This goes beyond the tool name, although it does not explicitly state read-only behavior or describe error conditions.

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 information-dense. The first sentence states the core output, and the second sentence adds essential access-scoping context. Every part 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?

For a parameterless read tool with no output schema, the description gives sufficient context: what the flags represent and the access constraints. It could be more explicit about the exact shape of the response or whether only flagged leads are returned, but overall it is adequate for an agent to 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?

The tool has zero parameters, so there is no parameter documentation required. The description adds context about scope and visibility that effectively substitutes for any parameter semantics, matching the baseline for parameterless tools.

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 resource: current outreach lead-status flags for LinkedIn-DM leads marked as 'replied outside LinkedIn'. It avoids ambiguity and distinguishes this from mutation tools like mark_lead_replied_outside, though it lacks an explicit verb such as 'retrieves' or 'lists'.

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 conveys an important precondition: the outreach section must be in scope, and an own-leads-only key only sees its own conversations. However, it does not explicitly contrast this tool with sibling read tools like get_recent_leads or mention when to prefer one over the other, so usage guidance is implied rather than fully stated.

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 burden of behavioral disclosure. It transparently explains the response contents: client, user, report-section scopes with possible values, outreach-lead marking ability, and own-leads-only restriction. It does not explicitly state that the operation is read-only, but the phrasing 'who this API key belongs to' strongly implies introspection with no side effects.

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

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

The description is a single, information-dense sentence with no wasted words. It front-loads the core question ('Who this API key belongs to') and then efficiently enumerates all the relevant access details. Every clause adds meaningful information about the tool's output.

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 are no parameters and no output schema, the description must explain what the tool returns, and it does so thoroughly. It covers the key owner, user, scope restrictions with valid values, outreach-lead marking permission, and own-leads-only restrictions. An agent has enough information to invoke the tool and interpret its result.

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 there is no parameter documentation burden on the description. The schema coverage is effectively 100% by being empty, and the description focuses entirely on the returned information rather than needing to explain inputs. This matches the baseline for a parameterless tool.

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

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

The description clearly identifies the tool's purpose: returning information about the current API key's owner, user, scopes, and special permissions. Although it lacks an explicit verb, the tool name 'get_me' combined with the description makes the resource and action unambiguous. It is clearly distinct from sibling lead/report/readiness tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: use this tool when you need to know what API key is being used and what access it has. It does not explicitly name alternatives or exclusion conditions, but the zero-parameter introspection nature and the detailed scope list make the intended usage obvious relative to the sibling tools.

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

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

Annotations already mark this as read-only, idempotent, and non-destructive, and the description adds meaningful behavior beyond that: data comes from the daily pull and can be up to a day old, accounts without campaigns return empty lists, and never-pulled campaigns have latest: null. It also states the paid_media section must be in scope. These details materially help the agent interpret results correctly.

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 resource and metrics, then covers edge cases, scope requirements, and data freshness in a compact sequence. Every sentence carries useful information and none is redundant with the annotations or schema. It is appropriately sized for the tool's complexity.

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, the description carries the full burden of explaining the return shape, and it does so thoroughly: accounts, campaigns, metric names, rolling window, pull date, and null behavior are all specified. It also covers the scope prerequisite and data staleness. An agent has everything needed to call and interpret 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 schema covers the single optional platform parameter fully with an enum and a description ('Limit to one platform'), so the description does not need to add much. It mentions the platform examples Reddit Ads and LinkedIn Ads, which map to the enum values, but provides no extra semantic detail beyond the schema. Baseline 3 is appropriate given 100% schema description 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 names the resource ('connected paid-media accounts') and specifies exactly what is returned: campaigns with latest impressions, clicks, spend, and pull date over a 30-day window. It also handles edge cases like accounts with no campaigns and campaigns never pulled, making the tool's purpose unmistakable. The current-snapshot framing distinguishes it from the sibling get_paid_media_history.

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 usage context by framing the data as the most recent pulled metrics and noting the paid_media section scope requirement. It does not explicitly name an alternative such as get_paid_media_history or state when not to use this tool, but the current-snapshot language implies the distinction. This is clear context without an explicit exclusion.

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 readOnly/idempotent annotations, the description adds non-obvious behavior: results are chronological oldest first, each daily pull has a 30-day window, and the default is the most recent 90 pulls. It also discloses the scope prerequisite and the filtering options, which meaningfully helps an agent predict 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 with no filler: the first states what the tool returns, the second provides parameter sourcing, and the third covers defaults, filtering, and prerequisites. Key behavioral details are 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?

With no output schema, the description carries the burden of explaining the return payload, and it does: contents, ordering, defaults, filters, and required scope are all covered. For a read-only history retrieval tool, nothing essential for correct invocation 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%, so the schema already documents campaign_id, since, and limit thoroughly. The description repeats the default of 90 pulls and the since/limit filtering but does not add meaning beyond the schema; the campaign_id provenance is already present in the schema property 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 opens with a concrete subject and scope: 'One campaign's pull history as a chronological series.' It specifies the exact contents (30-day window, impressions, clicks, spend, platform, account, objective, status), and it distinguishes this tool from siblings by noting campaign_id comes from get_paid_media.

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 gives clear context: this is for a single campaign's history, campaign_id should come from get_paid_media, and the paid_media section must be in scope. It does not explicitly name alternatives or when-not-to-use, but the source relationship and scope precondition provide practical selection 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 behavioral burden. It discloses meaningful traits: data access is scoped by key type, and each row carries an owner slug. It does not explicitly state that the operation is read-only, nor does it address edge cases like empty results or exact date boundary behavior, but for a get-style tool this is reasonably transparent.

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 dense sentence that front-loads the core resource ('structured window of recent outreach leads') and then lists details. Every clause earns its place, though the long colon/semicolon structure could be split into separate sentences for easier parsing.

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

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

For a parameterless tool with no output schema, the description covers the returned fields, the approximate 30-day window, the required section in scope, and key scoping. It omits ordering, pagination, or exact behavior when the outreach section is absent, but it is sufficient for most calling scenarios.

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, giving a baseline of 4. The description appropriately explains output content rather than parameters, since there are none to document.

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

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

The description identifies the resource as 'recent outreach leads' and enumerates the per-lead fields (conversation summary, last message, LinkedIn link, owner, status), so the tool's purpose is clear. It is implicitly distinct from siblings like get_lead_status, which targets a single lead's status, but it lacks an explicit verb such as 'list' or 'retrieve'.

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 concrete precondition: 'Requires the outreach section in scope' and explains key-based scoping ('own-leads-only key sees only its own leads, while an unrestricted key sees every salesperson's'). It does not name alternative tools or give explicit when-not-to-use guidance, so it stops short of full routing.

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 behavioral disclosure burden. It usefully notes that sections are filtered by what is visible to the current key, providing meaningful access-scoping context. However, it does not mention failure behavior, authentication requirements, or what happens for unpublished or missing reports.

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, information-dense sentence. Key facts are front-loaded: what the tool returns, by what identifier, and what content is included. There is no redundant or filler text.

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?

This is a low-complexity tool with one required parameter and no output schema. The description lists the main returned categories, giving an agent enough expectation of the response. It omits edge-case details like not-found or error behavior, but for a metadata getter the core context 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?

Schema description coverage is 100%, so the schema already defines report_id as an integer with bounds and a pointer to list_reports. The description adds only the notion of a 'published' report, providing no additional parameter format or usage detail. This matches the baseline for high 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 identifies the action (get), the resource (metadata for one published report), and the selection method (by id). It also lists expected contents (title, dates, sections visible), which distinguishes it from siblings like get_report_html and get_report_section that return content or specific sections.

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 explicit guidance on when to use this tool versus alternatives. The schema's 'Report id from list_reports' hint implies a typical workflow, and 'metadata' suggests it is for overview rather than content retrieval, but no clear exclusion or alternative routing is stated.

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 full behavioral disclosure burden. It warns that the returned HTML is untrusted content, instructs the agent not to execute scripts or follow embedded instructions, and explains that output is filtered by the current key's permissions.

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 yet information-dense, with the core function stated first and the security caveat and sibling alternative presented without redundancy. Every sentence contributes actionable guidance.

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 tool with no output schema, the description adequately explains what is returned, the security posture, the visibility filtering, and when to use a different tool. Nothing essential is missing for the agent to use it correctly.

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

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

The schema already documents report_id as coming from list_reports, and schema coverage is 100%. The description adds useful context such as 'published report' and permission-based filtering, but it does not materially expand the parameter's semantics 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 the tool returns the full HTML of a published report, scoped by visibility permissions. It distinguishes itself from get_report_section by name and by explicitly noting that the section tool should be used when only data is needed.

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 provides an alternative: 'use get_report_section for just the data you need.' This tells the agent when not to choose this tool and directs it to the more appropriate sibling for narrower data needs.

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 does reveal that the tool returns HTML fragments and fails for invalid or absent section keys, which is helpful. However, it omits details about the structure of the HTML fragments, auth requirements, idempotency, or error types, leaving the agent partially informed.

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 short sentences with no filler. The core purpose is front-loaded, followed immediately by the valid keys and failure condition, each earning its place. This is a model of concise, structured description.

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 2-parameter getter with no output schema, the description is mostly complete: it states the return format, valid inputs, and failure behavior. The main gap is the lack of usage guidance relative to sibling report tools, but the schema's report_id description covers the remaining essential 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 already describes report_id and provides an enum for section, so the description adds limited new semantic value. It repeats the valid section keys and adds the 'absent from the report' failure context, but it does not explain what each section key means. Since schema coverage is 50%, this is acceptable but not exemplary.

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 tool's output: 'One section of a published report as HTML fragments.' The resource is specific (a report section) and the valid section keys add precision. It is distinguishable from sibling report tools like get_report and get_report_html by the 'one section' qualifier, though it never states that contrast explicitly.

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 choose this tool over alternatives such as get_report or get_report_html. The failure condition ('Fails if the key is outside this key's scope or absent from the report') is useful but does not tell the agent when to use the tool or how to obtain a valid report_id, beyond what the schema already states.

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 full burden, and it does well: it discloses scoping by key, section-level visibility, filtering for scoped keys, and exclusion of drafts. It lacks pagination or limit details, but for a 0-parameter list tool this is minor.

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, front-loaded with the core action and order, followed by the important scoping rules. Every sentence adds value with no 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 0-parameter list operation with no output schema, the description is complete: it explains what is listed, the sort order, the entry fields, the section-scoping behavior, and the exclusion of drafts. An agent has enough to call it correctly.

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

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

The tool has zero parameters and schema coverage is 100%, so there is no parameter meaning for the description to add. Per the baseline for 0-parameter tools, this is fully adequate.

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 verb ('List') and resource ('published reports this key may see'), with ordering ('newest first'). It distinguishes itself from siblings like get_report by emphasizing the list behavior and visibility scoping.

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 when to use the tool: when an agent needs all reports visible to the current key. However, it does not explicitly contrast this with sibling tools like get_report or get_report_section, so the guidance is not fully 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?

With no annotations provided, the description carries the full behavioral burden and does so well: it discloses permission requirements, own-leads-only restrictions, idempotency, the unchanged:true return for already-marked leads, and the notification behavior for real changes.

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 purposeful sentences packed with relevant information: the operation's effect, permission constraints, and idempotent side effects. No unnecessary filler or repetition of schema content.

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 state-changing tool with no output schema, the description covers the essential behavioral outcomes, permissions, and edge case of repeated marking. An agent has enough context to call it correctly and to anticipate side effects.

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 schema already documents both conversation_id and contact_name adequately. The description reinforces the general purpose but does not add significant parameter-level detail 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?

States the exact action: marking an outreach lead as replied outside LinkedIn, with the concrete goal of stopping reports from flagging it as awaiting reply. This clearly differentiates it from sibling get_lead_status and unmark_lead tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description makes the usage context clear: use when a lead actually replied through email or phone rather than LinkedIn. It gives permission constraints but does not explicitly say when not to use it or name unmark_lead as the inverse alternative.

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 behavioral disclosure burden. It discloses the state transition and points to the sibling's permission rules, but does not explain side effects, reversibility beyond the undo, or audit trail impact. 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, tightly constructed sentence that front-loads the core purpose and adds the permission note without redundancy or filler.

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

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

For a simple two-parameter mutation with no output schema, the description adequately covers what the tool does, the precondition (same permissions as sibling), and the state after invocation. It omits only minor details like error modes, which are not typically required for this simplicity 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 description coverage is 100%, so the schema already meaningfully documents both parameters (conversation_id source and contact_name's audit-trail purpose). The description adds no parameter-level information, so the baseline 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 ('Undo') and resource ('replied-outside-LinkedIn mark'), and specifies the resulting state ('returning the lead to awaiting reply'). This clearly differentiates it from the sibling mark_lead_replied_outside as its inverse 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 intended use case is explicit: revert a previously applied replied-outside mark. It references the sibling tool for permission rules, providing context for when it is permitted, though it does not explicitly state 'use instead of X' because the inverse relationship is self-evident.

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