Nuntior

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

Annotations already declare the mutation and open-world profile, and the description adds valuable behavior: it lists what gets created, says the API key is live, explains the claimUrl, and covers the existing-email path. It stops short of clarifying key lifecycle details such as whether the API key is shown only once or how claim URLs expire.

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: purpose first, then usage condition, then return values and edge-case behavior. Every sentence carries useful information and none 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 no output schema, the description appropriately explains the key return values and the existing-email behavior. A minor gap remains around what happens when optional parameters are omitted, since none of the four inputs are required.

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 all four parameters well. The description adds some meaning around email reuse and workspace labeling, but most parameter-level detail still comes from the schema rather than 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 states a specific verb and a precise set of resources: provisioning a user account, multi-tenant workspace, default brand profile, and live Bearer API key. It is immediately distinguishable from sibling tools such as account_status and accounts_manage.

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 clearly says to use this tool when an agent or developer needs browserless onboarding from scratch, and it explains the existing-email case. It does not explicitly name alternatives or state when not to use it, but the intended 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?

Annotations declare openWorldHint=true and destructiveHint=false, and the description adds real value beyond that: AES-256-GCM encryption at rest, zero markup on BYOK, live latency diagnostics, and a specific failure mode (structured PLAN_CONNECTION_LIMIT_REACHED prompt when the connection quota is full). It stops short of saying whether disconnect is reversible or what authentication/permission level is needed for the vaulted key operations.

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 opening sentence is densely front-loaded and enumerates each action with its internal action name, which is genuinely useful. But it is a single run-on sentence packed with marketing phrasing ('1-click', 'zero markup', 'vault'), and the second and third sentences repeat the returns explanation rather than adding new routing 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 multi-action, 9-parameter tool with no output schema, the description covers the full action surface, the return shapes (metadata, authorizationUrl, latency diagnostics), and an error contract. What's missing is per-action required-parameter guidance in prose and any notion of OAuth flow completion or authorization failures, leaving a small gap for a fairly complex 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?

Schema description coverage is 100%, so every one of the 9 parameters is already documented in the schema, including enum meanings and conditional requirements (platform required for connect_oauth/connect_byok, accountId required for disconnect). The description doesn't add syntax, format, or interaction detail beyond restating the action list, 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 names five specific operations (list, connect_oauth, connect_byok, health checks, disconnect) mapped to the action enum, and states the resource domain (social accounts across 18+ platforms). It distinguishes itself from siblings like account_register and account_status by focusing on OAuth/BYOK credential lifecycle rather than registration or status reporting.

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 a concrete when-to-use clause: 'Use this tool whenever you need to link a new social network, verify OAuth token validity and latency, or inspect which platforms are ready for publishing.' That covers the main triggers well. However, it never explicitly excludes or routes against the closest siblings (account_register, account_status), which an agent may confuse with account creation or status inspection.

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, idempotentHint, and destructiveHint=false, so the safety profile is covered; the description reinforces 'read-only diagnostic' and, importantly, discloses that it returns a structured summary object with named keys, which annotations cannot convey. It omits error behavior for unauthenticated or invalid workspace IDs, keeping it below a 5.

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 scope of inspection before the usage directive and return shape. The long enumeration of tier/quota values is information-dense but each item is genuinely inspectable output, so it earns its place; slightly sentence-heavy but well ordered.

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 compensates by listing the returned object's keys (workspace, planLimits, usage, billing, profiles, connectedAccounts), and it establishes the auth model via the API key mention. Missing only edge-case behavior such as what happens with an invalid workspaceId.

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 both the apiKey override and workspaceId tenant-selection semantics are already documented in the schema. The prose adds no further parameter detail (no format hints, no interaction between apiKey and the Authorization header), so 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?

Names a specific verb (inspect) and resource (authenticated workspace's account status), then enumerates exactly what is surfaced: subscription tier values, quota limits, monthly post usage, API key prefix, Stripe billing status, and brand profiles. This is clearly distinguishable from siblings like billing_upgrade or accounts_manage.

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 when to reach for it: before connecting new social accounts or launching high-volume campaigns, to verify quota availability and readiness. It does not name an alternative tool for the upgrade path (e.g., billing_upgrade), so it stops short of full when/when-not 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?

It discloses key behavioral traits beyond the schema and annotations, such as the business advantage of '0% Nuntior ad-spend commission' and the exact data returned ('real-time spend/conversion snapshots').

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 text is dense but efficient, using every sentence to convey either a capability, a scope, a business benefit, or an output specification. It is perfectly sized for its 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?

Given the tool's multi-platform nature and the breadth of its functions, the description is highly complete, covering the full lifecycle of an ad campaign and the context of its 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?

While the schema is complete, the description reinforces the scope by listing specific supported platforms and clearly linking the 'boost_post' action to the strategic use case, helping the agent choose the correct 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 provides an exhaustive list of capabilities: 'Create, list, pause, resume, inspect metrics, or boost organic posts.' This clearly defines the scope of the tool beyond just 'ads_campaign' and specifies the platforms it supports.

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 a perfect 'when-to-use' scenario: 'Use this tool when an organic post performs strongly and should be amplified.' This is highly effective for routing the agent when a post goes viral organically.

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 safety profile (readOnlyHint/idempotentHint/openWorldHint), so the bar is lower. The description adds real value by describing what comes back — aggregated engagement metrics, X Smart Link cost savings, CET/UTC posting slots, or live signals with direct thread URLs and author handles — which the annotations do not disclose.

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 dense sentences, front-loaded with the action modes and their purpose. The trailing sentence on return values is useful but partially duplicates what the leading sentences already implied about outputs.

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 compensates by sketching the return shape for each action, and it names the five modes and covered networks. It omits nothing critical for calling the tool, though per-action parameter requirements (e.g., that query matters only for social_listen) are left to the schema.

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 every parameter (including the action enum and query) is already documented in the schema and the description's restatement adds little. Baseline 3 applies; the description neither adds syntax/format detail nor introduces confusion.

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 specific verbs (query, compute, execute) and enumerates the five action modes alongside the covered networks, so an agent knows exactly what the tool does and how it differs from siblings like traffic_analytics. The distinction between analytics, best_times, and social_listen is spelled out rather than implied.

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 explicitly routes one mode ('Use `social_listen` to discover live community threads, competitor mentions, and high-intent buyer questions'), which is clear when-to-use guidance for that action. It stops short of naming a sibling alternative or stating when not to use this tool versus traffic_analytics, so it falls short of a 5.

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 safety (destructiveHint=false, openWorldHint=true), so the bar is lower; the description adds value by disclosing the return payload (checkoutUrl/portalUrl, plan metadata, quotas, instructions). It could go further on the payment lifecycle — e.g., that no charge occurs until the user completes checkout — and the stated 'idempotentHint: true' sits slightly awkwardly with 'generates a live 1-click session URL' each 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?

Front-loaded with the core action and output type, then dense supporting detail. The pricing/quota enumeration is long but each figure is decision-relevant for an agent choosing a plan; nothing is clearly padding.

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 return-shape burden and does so (checkoutUrl/portalUrl plus instructions). Auth is covered indirectly by the schema's apiKey note. Minor gap: it doesn't state that the returned URL must be surfaced to the end user in a browser, which matters for correct handling.

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 four parameters and both enums, making 3 the baseline. The description reinforces the plan tiers and adds one genuinely new fact — the $0.50 per extra connection overage — but otherwise largely restates the enum documentation rather than adding usage semantics.

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?

Names a specific verb (Generate) and concrete resources (Stripe Checkout session URL / Customer Billing Portal link), scoped to workspace subscription upgrades. The plan tiers and add-on are spelled out, so an agent can immediately distinguish this from account_register, account_status, and the other billing-adjacent siblings.

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

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

The upgrade context is clear ('to upgrade a Nuntior workspace subscription') and the two modes are named, with plan choices enumerated. However, it never explicitly routes the agent — the checkout-vs-portal decision only appears in the schema's `action` description, and there are no stated prerequisites (e.g., must already have an account or active subscription) 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?

Annotations already declare openWorld=true, non-idempotent, non-destructive writes, so the safety profile is covered. The description adds real value by disclosing the return payload (articleId, slug, url, blogMirrorUrl, timestamp) for a tool with no output schema, plus the canonical-URL mechanic. It stops short of stating auth/profile requirements or what happens to an existing article on republish.

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, front-loaded with the action and destinations, then workflow, then return values — no filler. It is dense with platform names and could be trimmed, but every clause carries routing or expectation-setting 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 10-parameter, multi-platform write tool with no output schema, the description supplies the missing return-value and workflow context an agent needs. Minor gaps remain: per-platform prerequisites/credentials and whether 'update_article' is idempotent when a slug collides are not addressed.

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 three enums are fully self-documenting, so the schema carries parameter meaning. The description's only added semantics are the canonical/syndication role of canonicalUrl and the auto-slug fallback, both already implied or stated in the schema. 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?

Opens with a specific verb+resource ('Publish or update long-form Markdown SEO articles') and enumerates the exact destinations (Nuntior Live Reader, Ghost, WordPress, Webflow, Dev.to, Medium, Hashnode). It also explicitly distinguishes itself from the sibling posts_publish by declaring this the canonical/article tool and that one the syndication 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?

Gives an explicit workflow: publish the full technical article here first to obtain a live canonical URL, then feed that URL into posts_publish with xLinkStrategy 'first_reply'. That names the alternative, the ordering, and the parameter handoff — an agent can route without 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 adds real behavioral context (a threshold-triggered automatic follow-up comment, supported platforms, returned status/config), but it explicitly offers a 'delete' action while annotations declare destructiveHint=false, a meaningful mismatch for an agent deciding whether the call can remove data. The tension is never resolved (soft-hide vs permanent removal), so the behavioral picture is muddied rather than clarified.

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?

Front-loaded with the action set before the usage and return-value sentences; two sentences carry the whole load with no filler. It is dense but every clause maps to a distinct capability or outcome.

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?

Return information is disclosed ('moderation execution status or the active Auto-Plug rule configuration'), which matters since there is no output schema, and all seven parameters are schema-documented. What's missing is the per-action dependency logic (which of postId/commentId/replyText are required for each action), which an agent must guess.

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 each parameter already carries enum values, formats (`post_...`, `cmt_...`), and defaults, so the schema does the heavy lifting. The description adds no syntax or conditional guidance beyond what the schema provides, 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?

States specific verbs (list, reply, hide, delete, configure) against a concrete resource (public comments on published social posts) and names the conditional Auto-Plug behavior. An agent can distinguish this from siblings such as inbox_messages or posts_manage without opening the schema.

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

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

Gives clear use cases ('engage commenters', 'remove spam on X/LinkedIn/Instagram/YouTube', 'plug a newsletter or product URL underneath high-performing posts'), which frames the when-to-use context well. It does not state when NOT to use it or explicitly route to alternatives (e.g. inbox_messages for DMs vs comments), so it stops short of a 5.

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=false, openWorldHint=true, idempotentHint=false and destructiveHint=false, and the description usefully adds the return shape (normalized objects with sender handles, direction, delivery confirmation). It omits the consequences an agent most needs: that 'reply' sends a real external message through the connected network, that 'mark_read' mutates state, and any authorization or rate-limit 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, front-loaded with the scope, then operation modes, then return values — no filler. Its one flaw is that the platform roll-call names only 6 of the 18 platforms in the enum, which is noise that could mislead rather than inform.

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?

There is no output schema, and the description partially compensates by describing the returned normalized conversation objects. But for an open-world, non-idempotent multi-modal tool it still leaves out error behavior, the fact that unsupported platform values exist beyond the six listed, and any prerequisite for outbound replies.

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 every parameter including the action enum, threadId format (`thr_...`), profileId, and unreadOnly. The description merely restates the action list rather than adding format, defaulting, or dependency semantics, 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 resource (unified cross-network social and messaging inbox) and enumerates the four discrete operations, so an agent knows exactly what the tool does. It does not, however, differentiate itself from plausible siblings such as telecom_messaging or comments_moderate, leaving the boundary to inference.

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?

Each action is glossed ('listing conversation threads', 'dispatching outbound replies', 'marking threads as read'), which implies when each mode applies. But there is no explicit when-to-use/when-not guidance and no routing statement versus siblings like comments_moderate for public comments or telecom_messaging for SMS/voice.

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 mutability, open-world behavior, idempotency, and non-destructiveness. The description adds useful behavioral detail beyond annotations by naming the returned artifacts: presigned PUT URLs, CDN URLs, aspect-ratio variant maps, and clip URLs. It does not cover auth requirements, rate limits, or persistence 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 front-loaded with capabilities, then usage guidance, then return values. It is dense but appropriately sized for a multi-action pipeline with nine parameters, though the first sentence is long and packs several capabilities together.

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 usefully explains the kinds of URLs and variant maps returned. It covers the main actions and workflow, but does not mention `check_status` polling in the prose and gives no error or quota behavior 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?

Schema description coverage is 100%, so all nine parameters and the action enum are already documented in structured fields. The description reinforces the major operations and aspect ratios but does not add syntax or format details 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 set of verbs and resources: media ingestion, storage uploads, aspect-ratio video reframing, clip extraction, and AI visual generation. It also explicitly distinguishes the tool from `posts_publish` by describing the upload/transcode workflow that must happen first.

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 a clear when-to-use rule: call before `posts_publish` when a campaign needs media uploads or landscape-to-vertical conversion. It names the related sibling and the workflow ordering, though it does not state explicit exclusions or when not to use the tool.

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 non-read-only, idempotent, open-world, non-destructive behavior, so the bar is lower. The description usefully adds the return shape (ScheduledPost records with per-platform delivery status and timestamps) and that retry re-dispatches failed deliveries, but says nothing about whether delete is permanent, whether updates affect already-published posts, or what retry_failed does on success. Note: destructiveHint=false sits uneasily with the advertised delete action, though that is an annotation-to-capability tension rather than a description 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?

Two tight sentences: capabilities and target store first, use cases second, return shape last. No filler, nothing buried.

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 six-parameter, action-dispatched tool with no output schema, the description covers what the tool reaches, when to use each action, and what comes back. It stops short of per-action preconditions (e.g., retry_failed only meaningful when deliveries failed, delete irreversibility), which is the main remaining gap.

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

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

Schema description coverage is 100%, so the schema already explains action, postId, content, profileId, scheduledFor, and statusFilter with per-action conditions. The description adds no syntax, defaults, or edge-case detail beyond that, so 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?

Names specific verbs (list, inspect, update, delete, retry) against a concrete resource (scheduled/queued/published social posts in Nuntior Cloud). Scope is unambiguous, though it never explicitly contrasts with the sibling posts_publish, which an agent could confuse for the update/publish boundary.

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?

Enumerates concrete use cases mapped to actions: auditing the content calendar, editing copy or timestamps before publication, removing cancelled posts, retrying failed deliveries. Clear context for when to reach for it, but no explicit exclusions or named 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?

The description provides rich behavioral detail beyond the standard hints, especially regarding the `xLinkStrategy`. It explains the economic and functional benefits of link optimization (85% cost reduction, avoiding reach penalties), offering critical context for decision-making.

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 definition is highly dense but logically structured, front-loading the primary function and then detailing complex, high-value features (X Link Optimization and return values) efficiently.

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 complex (18 platforms, scheduling, media, optimization, authentication). The description covers every major functional area, ensuring an agent has complete context before attempting an 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?

Even with 100% schema coverage, the description adds high semantic value by clarifying the operational difference between the `action` enum modes and explaining the specific implications of using the `xLinkStrategy` parameter.

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 defines the tool's purpose as publishing and cross-posting across 18+ social networks, which is highly specific. It immediately distinguishes itself from other potential publishing tools by specifying its scope and modes of 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?

It provides explicit usage guidelines by defining four distinct operational modes (`publish_now`, `schedule`, `queue`, `cross_post`) and explaining the specific conditions under which each mode should be invoked.

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 beyond safety hints by specifying the behavioral scope of the management, such as including 'default timezone' and 'automatic media aspect-ratio fitting rules.' It also explicitly clarifies the possible return values (list or a specific record).

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?

Although lengthy, the description is well-justified by the tool's complexity (7 parameters, multi-action support). The core function and major benefits are front-loaded into the first two sentences.

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 detailed input schema and annotations, the description is highly complete. It explains complex settings (like media fitting and link strategy) and confirms the expected output behavior, leaving no critical behavioral unknowns for the agent.

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 description adds substantial semantic value by explaining the *business impact* of specific parameters, such as the X Smart Link strategy 'saving 85% in X API link fees' and 'avoid[ing] reach penalties.' This context is far richer than the schema alone 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 tool clearly states its purpose as managing isolated multi-tenant brand or client profiles using the specific verbs: create, list, update, or delete.

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 excellent context on *when* to use this tool—specifically for segmenting social accounts by brand, business unit, or agency client. While it doesn't name alternatives, the strong focus on isolation defines its necessary use case.

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 this is a non-read-only, non-idempotent, non-destructive, open-world operation, and the description adds useful beyond-schema facts: per-message pricing ($0.004/msg), the pre-approved template constraint, and the returned confirmation fields. The significant omission is the non-idempotency risk: nothing warns that retrying a send may dispatch duplicate messages or calls, which is exactly the behavior an agent most needs disclosed for a costly outbound 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?

A single dense paragraph that front-loads the four actions before use cases and return values; every sentence carries information. It is tightly packed rather than padded, though the sentence is long and would scan faster if the action list and use-case list were separated.

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 correctly compensates by summarizing return values (dispatch confirmation, messageId, E.164 numbers, per-message cost). For a multi-action tool with 7 parameters, the gaps are behavioral rather than structural: no rate limits, no idempotency warning, and no statement of what the `logs` action returns relative to the dispatch actions.

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 each parameter is already documented in the schema, including required-combination notes for toNumber, templateName, and voiceScriptOrAgentId. The description reinforces the action-to-parameter mapping in prose but adds no format, constraint, or interaction detail beyond the schema, 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 names specific verbs and resources for each mode: transactional SMS, WhatsApp Business Cloud template dispatch, outbound AI voice calls, and telecom usage ledger audit, each tied to its action value in backticks. The purpose is unmistakable. It does not explicitly contrast itself with the sibling telecom_numbers tool, which manages numbers rather than sending, so it falls just short of full sibling differentiation.

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?

Concrete use cases are given: out-of-band customer notifications, executive alerts, appointment reminders, autonomous voice follow-ups. It also states that WhatsApp messages must be pre-approved templates, which is a real prerequisite. However, it offers no when-not guidance and no explicit alternative routing (e.g., when to use telecom_numbers instead for provisioning).

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 non-read-only, idempotent, open-world and non-destructive, so the safety profile is covered. The description adds value with per-number cost ($1.50/mo vs $59/mo legacy bundles) and the shape of returned capabilities, though it says nothing about what happens when a requested number is unavailable or whether provisioning can be undone.

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, front-loaded with the operations and then the use case, with no filler. The parenthetical price comparison is slightly promotional but does carry decision-relevant cost 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 four-action, seven-parameter tool with no output schema, the description covers what each action does, the geographic scope, the cost, and the returned record fields. Gaps remain around failure modes (number already taken, unsupported country) and the fact that SIP configuration requires an existing numberId.

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 enum's own description already explains each action and the country/phone-number formats. The description only echoes the same action list and country set, adding no format or constraint detail beyond the schema, 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 first sentence names four concrete operations (search E.164 numbers, provision, list, bind to SIP/WebRTC trunk) on a clearly identified resource, so an agent can see the scope immediately. It does not mention the sibling tool telecom_messaging, so the boundary between 'numbers' and 'messaging' is left implicit.

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 second sentence gives an explicit triggering condition: use it when an agent needs a dedicated SMS, WhatsApp Business Cloud, or voice number in the listed countries. It supplies no exclusion or alternative-tool guidance, and never says when to prefer the telecom_messaging sibling instead.

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, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered structurally. The description adds useful context beyond annotations by framing it as live production observability and by naming the return categories: aggregated counters, channel breakdowns, latency percentiles, and recent event logs. It still does not mention rate limits, auth requirements, or exact data-freshness semantics.

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 action and metric scope, and the return-value sentence is relevant. The long parenthetical channel list is somewhat list-heavy, but it supports the sourceChannel field and does not obscure the main purpose.

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?

There is no output schema, but the description names the return categories and the production-monitoring purpose, which is enough for an agent to understand what to expect. It could still clarify freshness, pagination, or output shape details, but it is largely complete for a read-only query 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?

Schema coverage is 100%, and the schema already documents limit, eventType, windowHours, and sourceChannel with defaults, max values, enums, and examples. The description echoes the channel list and event-stream concept but does not add syntax or filtering behavior beyond what the schema provides, so 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 uses the specific verb 'Query' and enumerates the telemetry resources it covers, such as traffic, attribution, MCP tool calls, OAuth funnel, geography, and live events. It clearly separates this from account, billing, posts, and messaging siblings by domain, though it does not explicitly distinguish itself from the generic analytics_query sibling.

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 explicitly says to use this read-only observability tool to monitor how developers and AI agents discover and invoke Nuntior in production, which gives a clear usage context. It does not name exclusions or route the agent to alternatives such as analytics_query when a broader or different analytics scope is needed.

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