DevTune MCP

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

Annotations already mark the tool as idempotent and non-destructive, and the description adds valuable beyond-annotation context: it requires actions.write, preserves the finding/evidence/score, and forbids inferring customer intent. This gives the agent a clear operational and safety profile.

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 concise sentences front-load the purpose, then state prerequisites and behavior. There is no filler, redundancy, or unhelpful repetition of schema fields.

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 mutation tool with no output schema, it covers confirmation, permissions, scope, and non-destructive behavior. It does not explicitly spell out the relationship between auditRunId and findingId or return behavior, but those gaps are minor given the self-explanatory names and idempotent annotation.

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

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

With 0% schema description coverage, the description compensates for the most nuanced parameters: urls must be exactly the URLs shown in the audit finding, and confirmedIntent must reflect explicit customer confirmation rather than inference. auditRunId and findingId are left to their self-explanatory names.

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

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

The description uses a specific verb ('acknowledge'), a clear resource ('retrieval restriction'), and a precise scope ('only the exact URLs displayed in an audit finding'). This makes its purpose distinct from siblings like reconsider_retrieval_restriction.

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 states when to use the tool: with explicit customer confirmation, for exact displayed URLs, and with actions.write permission. It also gives a 'never infer customer intent' guardrail, though it does not explicitly name alternative tools or contrast with reconsider_retrieval_restriction.

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 of behavioral disclosure. It discloses that measurement is tied to the action completion date, that publishedUrls replaces the existing list (with a warning to retain entries), and that existing measurement dates cannot be overwritten. It also notes the required permission (actions.write). These are significant behavioral details that an agent needs to know before calling this mutation tool. It does not mention reversibility or idempotency, but the coverage is solid.

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 succinct and front-loaded with the core action. Each sentence adds essential information: the action itself, the human approval prerequisite, the measurement timing nuance, the replacement behavior for publishedUrls, the immutability of existing dates, and the permission requirement. There is no fluff or redundancy; every word earns its place.

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

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

For a mutation tool with four parameters, no output schema, and no annotations, the description covers key contextual aspects: prerequisites (human approval), behavioral details (measurement timing, list replacement, immutability), and permissions. It does not describe return values or error conditions, but without an output schema these may not be critical. The description is adequate for an agent to invoke the tool correctly, though it could mention what happens if the action is not completed or if the URL already exists.

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 0%, so the description must compensate. It specifically explains the semantics of publishedUrls (it replaces the list and existing entries must be retained), which is valuable. However, it does not explain the semantics of actionId, url, or changeEventId beyond the obvious from names and context. Since the schema has no descriptions, the description could have elaborated on these parameters, especially changeEventId, but it covers the most complex one. It adds partial value but not full compensation for the zero 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 opens with a specific verb and resource ('Attach a published URL to a completed action'), clearly stating what the tool does. It distinguishes itself from the many 'get_', 'list_', and 'update_' siblings by focusing on the unique action of attaching a URL to a completed action. No other sibling covers this operation, so purpose is unambiguous.

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 some usage context: it instructs to 'Obtain human approval first' and clarifies that measurement starts at the completion date, which are important prerequisites and behavior notes. However, it does not explicitly mention when to use this tool versus alternatives (though no direct alternatives exist), nor does it state when not to use it. The guidance is helpful but not exhaustive regarding selection criteria.

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

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

With no annotations, the description carries the burden of disclosing behavioral traits. It reveals the mutation nature ('claim', 'restores'), explains the re-claim side effect (preserves original content), lists conflict cases, and states the required permission (actions.write). This adds significant context beyond the schema, though it stops short of detailing the success response or other 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 three tight sentences with no filler. The primary purpose is front-loaded, and the most consequential behavioral nuances (re-claim, conflicts, permission) are compactly conveyed. Minor density around the conflict sentence does not hurt clarity.

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

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

For a mutation tool with no annotations, no output schema, and three undocumented parameters, the description omits essential information: meaning of each parameter, what a successful response looks like, and how the 'claim' materially alters state beyond marking. It covers prerequisites and failure modes but is far from 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 coverage is 0% and the description provides no explanation of the three parameters (title, description, changeEventId). An agent cannot determine what each field represents or how they relate to the change being claimed, leaving a critical gap for correct invocation.

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

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

The description clearly states the specific action ('Claim a detected change') and defines it as marking a change as a completed measured action. It distinguishes the tool's purpose from that of siblings like devtune_unclaim_content_change by describing the claim and re-claim behavior 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?

It provides a prerequisite ('Obtain human approval first') and describes conditions under which a conflict arises (14-day rule, concurrent update), implying when the tool is appropriate. However, it does not explicitly contrast with alternatives such as unclaim or other action tools, leaving some selection guidance to inference.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses two key traits: saves the action even when brief generation fails, and does not publish content. It also mentions credit reservation for generateBrief, adding cost implications. This is substantial value beyond the schema, though it does not cover all potential edge cases (e.g., idempotency of reuse).

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 three sentences: the first states the core purpose, the second gives a critical parameter guideline, and the third discloses behavior. It is front-loaded with the primary action and contains zero fluff. Every sentence earns its place.

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

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

For a create-action tool with 6 parameters and no output schema, the description covers the purpose, key parameter usage, and critical failure/success behavior. It lacks details on the response format or what 'reuse' entails, but given the simplicity and absence of an output schema, it is adequately complete. The description does not overpromise or leave the agent guessing on essential call decisions.

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 50%, and the description adds meaningful context for generateBrief by clarifying the credit cost and the condition under which it should be set, complementing the schema's default and queue description. The other parameters (promptId, title, description) are self-explanatory strings, and targetUrl already has a schema description. The description effectively compensates for the partial coverage.

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

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

The description clearly states a specific verb-resource pair: 'Create or reuse an action from a tracked prompt.' It identifies the input source (tracked prompt) and distinguishes from sibling tools like devtune_get_actions or devtune_get_prompt_actions by implying a write operation. The additional behavioral notes (saves even on failure, does not publish) further clarify its role.

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

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

The description provides explicit conditional guidance: 'Only set generateBrief when the customer requests metered brief generation (reserves 2 credits).' It also clarifies that saving occurs even if brief generation fails and that content is not published, implying publishing is a separate step. However, it does not name alternative tools or state when not to use this tool, so it lacks explicit 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?

Adds substantial behavior beyond the annotations: the webhooks.write scope requirement, the one-time-only return of the signing secret ('store it securely'), and the absence of automatic retries. These are exactly the traits an agent needs before calling a non-idempotent mutation.

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?

Four short, front-loaded sentences with no filler; the core action leads and the security/retry caveats follow. Slightly terse on request-shape details but nothing 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?

For a 2-parameter create tool with no output schema, the description covers the return value (signing secret, once) and key constraints. It leaves out duplicate-URL behavior and error/validation semantics, which are minor gaps here.

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 0%, so the description must compensate, and it only partially does: 'public HTTPS endpoint' adds a real constraint on url beyond format:uri, but it omits the 2048-char limit, and 'supported events' is redundant with the enum already in 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 a specific verb and resource ('Create a project webhook') plus scope constraints (public HTTPS endpoint, supported events), which is enough to distinguish it from delete_webhook, list_webhooks, and rotate_webhook_secret. It does not, however, explicitly name or contrast any 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?

The description explains what the tool creates but gives no when-to-use guidance, no exclusions, and no pointers to the sibling webhook tools (rotate_webhook_secret, delete_webhook, list_webhooks). The 'Requires webhooks.write' line is a prerequisite, not usage 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Confirm or cancel' implies a state-changing mutation, but the description does not disclose what confirming does (does it dispatch a specialist, consume the handoff?), what canceling reverts, whether the operation is idempotent, or what happens if the handoff is not actually pending. This is a consequential decision tool with no behavioral detail.

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 efficient sentence with no filler or repetition. It is appropriately sized, though the brevity comes at the cost of missing parameter and behavioral detail. Concise but not comprehensive.

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 mutation tool with three required parameters, no output schema, and zero annotations, the description is inadequate. It does not explain the parameters, the consequences of each decision value, or any prerequisites (e.g., the handoff must exist and be pending). An agent would not know the full effect of invoking it.

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 0%, so the description must compensate. It implicitly maps to the decision enum ('confirm or cancel' corresponds to the decision property) and 'one pending' hints at handoffId, but threadId is entirely unexplained, and there is no clarity on how the three parameters relate. The description adds only minimal value over the raw schema.

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

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

The description states a specific verb ('Confirm or cancel') and a specific resource ('one pending specialist-agent handoff in a Dex conversation'). It clearly distinguishes from siblings like devtune_list_dex_handoffs (which lists rather than decides) and devtune_start_dex_conversation. An agent can tell what this does 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?

The phrase 'in a Dex conversation' gives some contextual anchor, and 'pending' implies it is used after handoffs have been surfaced (e.g., via list_dex_handoffs). However, it provides no explicit guidance on when to use this tool versus alternatives, and no exclusions or when-not-to-use conditions.

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 mentions that saves verify the text against the persisted agent event, which is helpful, but it does not disclose side effects of dismissing, idempotency, reversibility, permission requirements, or what happens on repeated calls. This is a significant gap for a mutation tool.

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

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

The description is two short sentences, front-loaded with the core purpose and a behavioral note. There is no fluff or repetition. It is appropriately sized for the information it conveys, though it omits important details that are penalized elsewhere.

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 has 5 required parameters, no output schema, and no annotations, the description is far from complete. It does not explain parameter semantics, usage context, or expected behavior beyond the basic action. An agent would struggle to invoke it correctly without external knowledge, making the definition inadequate for a tool of this complexity.

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 0%, so the description must explain the parameters. It does not mention any of the five parameters (taskId, offerId, decision, threadId, offeredText) by name or explain their roles. The only hint is the verification note, which loosely relates to offeredText, but it is not explicit. The agent has no guidance on what values to provide or how they interact.

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 (save or dismiss) and a distinct resource (durable memory offer from a Dex conversation), which distinguishes it from sibling tools like devtune_decide_dex_handoff. It also adds a meaningful behavioral detail about verification on save, making the purpose unambiguous.

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

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

The description gives no explicit guidance on when to use this tool versus alternatives. It does not mention prerequisites, when to choose save vs dismiss, or direct the agent to other tools in edge cases. The usage context is only implied by the name and the action, which is insufficient given the large sibling set.

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 clearly indicates the destructive nature of the operation, notes that 'Billing and audit records are retained,' and specifies the ownership/auth prerequisite. This is strong transparency for a delete operation.

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 three short sentences with no filler. The primary action is front-loaded, followed by key retention and authorization details. Every sentence earns its place.

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

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

For a single-parameter delete tool with no output schema and no annotations, the description covers the essential context: what is deleted, when it can be deleted, what is retained, and who is allowed to delete it. No critical information 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 coverage is 0%, but there is only one parameter, threadId, whose UUID format is documented in the schema. The description adds meaningful context by specifying 'your private Dex chat' and the ownership requirement, even though it does not explicitly name threadId as the target.

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 precise action and resource: 'Delete your private Dex chat'. It clearly distinguishes this deletion tool from sibling tools like devtune_list_dex_threads, devtune_rename_dex_thread, and devtune_send_dex_message.

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 usage context: it should be used 'after stopping its live run' and only with 'a user-attributed API key owned by the chat creator.' It does not explicitly name alternatives or when-not-to-use conditions, but no sibling deletion tool exists, so the guidance is sufficient.

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 destructiveHint=true and idempotentHint=true, yet the description adds genuinely new context: the exact return payload ({deleted:true}), not_found for unknown/foreign IDs, the required webhooks.write permission, and the no-automatic-retries policy. That is meaningful behavioral detail beyond the structured hints, though it stops short of explaining whether the operation is reversible or recoverable.

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?

Four short, dense sentences with the action front-loaded and no filler. Each sentence carries distinct, actionable information (return shape, edge case, error case, permission, retry policy).

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 mutation with no output schema, the description covers the return value, idempotent behavior, error semantics, permission requirement, and retry policy. An agent has everything needed to call it correctly and interpret the 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?

Schema description coverage is 0% and the description never explains webhookId format, source, or syntax. The only parameter insight is indirect — 'foreign IDs return not_found' hints that IDs are project-scoped — which is thin compensation for a completely undocumented 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?

States a specific verb and resource ('Deactivate a project webhook'), and within the webhook sibling group (create_webhook, list_webhooks, rotate_webhook_secret) the action is unmistakable. It also usefully signals that this is a soft deactivation rather than a hard purge, which the tool name 'delete' alone would not convey.

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?

Usage is implied by the verb and the required webhooks.write scope, and the edge-case note (already-inactive still returns deleted:true) helps an agent decide it is safe to call. However, no alternative is named and no explicit when-to-use/when-not condition is stated, so the agent must infer from the name and siblings.

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

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

No annotations are provided, so the description carries the disclosure burden. It clearly reveals that the tool may trigger background processing and reports whether the brief is ready, queued, or already generating. This gives meaningful behavioral context, though it does not mention failure/error 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?

The description is concise and front-loaded: it states the trigger action and then the value of the return across three states. No additional filler or redundant restatement appears.

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 trigger tool with two documented parameters and no output schema, the description is sufficient for an agent to understand the invocation's effect and the possible return state. It covers the asynchronous background aspect that is not present in the structured fields.

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 a 3. The description does not add further parameter-level semantics beyond the schema's UUID and style details, which is acceptable because the schema already documents both parameters completely.

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 specific action (brief generation for an action) and clarifies the return semantics: it returns whether the brief is ready, queued, or already generating. This sufficiently distinguishes it from siblings like devtune_get_action_brief.

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 call this tool versus devtune_get_action_brief or other action-related siblings. The status-oriented return implies usage for requesting or checking generation, but no alternative or condition is named.

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 and does disclose meaningful behavior: materialization state, machine-traffic sensor coverage, inferred project relevance, the volume-vs-fit ranking of clusters, and that unrelated demand is shown but excluded from automatic action suggestions. It stops short of stating permission/auth or freshness guarantees.

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 core purpose is front-loaded in the opening clause, and each subsequent clause adds distinct information (cluster separation, ranking rule, action-suggestion exclusion, hosts semantics). It is dense and jargon-heavy but not padded.

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 a zero-param read tool with no output schema and no annotations, the description supplies a solid account of what comes back and how it is organized. It could say more about the meaning of the 'materializer window' and completeness/freshness of the data, but it is largely sufficient to call 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 takes zero input parameters (empty schema, additionalProperties: false), so the baseline is 4. The 'optional hosts / null host' passage reads as explanation of the returned hosts breakdown rather than a missing input 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?

States a specific verb+resource ('Get project-scoped 404 Demand') and immediately characterizes the content (intent clusters vs scanner/technical noise). It is a well-defined data-retrieval tool, though it never names a sibling or explicitly contrasts itself with adjacent analytics tools like get_content_gaps or get_page_metrics.

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

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

No explicit when-to-use or when-not-to-use statement, and no alternatives are referenced. Usage is only implied by the scoped purpose (pull 404 demand analytics for the current window). Adequate but leaves routing between analytics siblings to inference.

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

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

Annotations already cover safety (readOnly/idempotent/non-destructive), but the description adds real behavioral context: null semantics (match_rate null when nothing judged, prior_totals null when incomparable), the method_changed/prior_status comparison gate, and the pending-answers trap when facts is zero. This is well beyond what the annotations provide.

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

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

The first sentence front-loads the purpose well, but the remainder is one massive run-on paragraph blending field semantics into a single blob with no separation. Given no output schema the content is relevant, but the structure makes it hard to parse.

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

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

With no output schema and high domain complexity, the description carries the return-value burden thoroughly: totals breakdown, prior-window comparison rules, weekly trend, per-platform split, coverage states, and series_started_on. Only the intent/platform filter parameters are left unexplained.

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 0%, so the description must carry parameter meaning, yet it never mentions the intent parameter at all and only loosely implies window (30/90 days) and platform (per-platform split, not as a filter). Two of three parameters are effectively undocumented.

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

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

States a specific verb+resource: reading Accuracy (claims checked against the product's own site) over 30 or 90 days, with aggregate totals and rates. It is distinguishable from the singular devtune_get_accuracy_claim and devtune_list_accuracy_contradictions siblings, though it never explicitly contrasts itself with them.

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

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

Usage is implied rather than stated: the tool is for aggregate accuracy reporting over a window and it names devtune_list_accuracy_contradictions as the source of individual rows. There is no explicit when-to-use-this vs. the claim/contradiction-level siblings, so the agent must infer the selection rule.

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 read-only and idempotent safety, and the description adds genuinely new behavioral context: the null semantics for outcome and claim when no answer exists in the window/filters, and the refusal of a group id outside the project. No output schema exists, so this return-payload enumeration and error disclosure carry real weight.

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?

Purpose and identifier source are front-loaded, then the payload is enumerated. The middle run-on list of return fields is dense and slightly sprawling, but every clause carries information rather than 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?

For a 4-parameter read tool with no output schema, the description thoroughly enumerates the response shape, covers the empty-window null case, and states the cross-project error. The only material gap is the undocumented 'intent' parameter.

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 0%, so the description must annotate all four parameters. It explains groupId's origin and implies window/platform via 'same window and filters', but never documents the 'intent' parameter, and gives no format or default detail for the window enum. Partial compensation only.

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

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

The description opens with a specific verb and resource ('Read one Accuracy claim group') and immediately routes to the sibling that supplies the identifier, distinguishing it from devtune_list_accuracy_claims and devtune_get_accuracy. An agent can tell what this returns 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?

It states a clear prerequisite and scope constraint ('the group_id from devtune_list_accuracy_claims', 'over the same window and filters'), which effectively tells the agent when and how to call it. It stops short of naming when to prefer devtune_get_accuracy or devtune_get_accuracy_contradiction instead, so no explicit 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 cover the safe-read profile (readOnly, idempotent, non-destructive), and the description adds substantial behavior beyond them: the full returned shape, ordering ("active ones first"), the empty/null sentinel behavior when no wording matches, and an explicit error contract ("A malformed key is refused").

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 purpose is front-loaded and the two trailing sentences handle edge cases tersely. The middle sentence is a long run-on enumerating the return shape, but with no output schema every clause carries real information rather than 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 4-param, zero-coverage, no-output-schema tool, the description covers return contents, ordering, null/empty semantics, key provenance and error handling. The remaining gap is the meaning of intent and platform, which neither the schema nor the description explains.

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 0%, so the description carries the burden, and it does well for sentenceKey (source tools, format implied) and gestures at window/filters via "same window and filters." However intent and platform are never explained, and the enum values for window are left to the schema, so compensation is partial.

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

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

The description opens with a specific verb+resource ("Read one Accuracy contradiction row") and immediately scopes it against siblings by citing devtune_list_accuracy_contradictions and devtune_list_accuracy_claims as the sources of the key. An agent can distinguish this single-row reader from the list tools without opening any 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?

It states where the sentenceKey comes from and that it must be read "over the same window and filters" as the list call, which is clear procedural context. It stops short of an explicit when-not or an alternative-tool exclusion, so it lands just below the top.

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 and does so extensively: it discloses that locked/retiring pages have edits stripped on read, that stale inputs omit the plan while preserving the stored brief, that edgeStatus is a suggestion not a measurement, that format 1 briefs cannot be drafted from, and that claimCheck annotates but never changes the brief (read-only 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 core purpose is front-loaded in the first sentence, which is good. However, the remainder is an extremely dense run-on covering formats, edge cases, and claim checks, making it hard to parse; much is genuinely necessary because there is no output schema, but the structure could be broken into digestible units.

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

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

With no output schema and a single simple input, the description must carry the return-value burden, and it does: it details both brief formats, the structured brief fields, evidence objects, the inboundLinkPlan, and claimCheck contents. An agent has enough to interpret results 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?

There is a single parameter (actionId) with 100% schema description coverage, so the schema already documents it fully. The description adds no extra meaning about the parameter, which matches the baseline of 3 when the schema does the heavy lifting.

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

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

The opening sentence gives a specific verb+resource ('Get the stored brief for a specific action') and enumerates what the response contains (readiness status, cut-short research, credits, timestamp, the brief). It is clearly distinguishable in spirit from devtune_generate_action_brief and devtune_get_action_draft, but it never names those siblings or explicitly contrasts retrieval with generation/drafting.

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 when-to-use, when-not-to-use, or alternative-selection guidance; the text dives straight into return-shape details. At best the agent can infer 'read a brief' from the purpose, but nothing tells it whether to call this versus generate_action_brief or get_action_draft.

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, and it delivers substantial behavioral context: draftKind semantics, briefState staleness, run-in-flight behavior, and the guarantee that 'Drafts are never published by DevTune.' It omits operational traits such as permissions, error behavior, or pagination, but the behavioral disclosure is far above a bare getter.

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 purpose is front-loaded, but the remainder is a single very dense run-on paragraph covering every nested field, which is hard to parse. The detail largely earns its place because no output schema exists, yet the lack of structure works against readability.

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?

Since there is no output schema, the description must document the return shape, and it does so extensively (versions, factCheck states, coverage, current). For a complex nested read tool this is close to complete, though the field documentation is dense and could be organized better.

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 there is a single UUID parameter, so the schema already documents actionId fully. The description adds no syntax or meaning for the input beyond that, 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 first clause gives a specific verb and resource ('Get an action's content draft'), which clearly separates it from siblings like devtune_get_action_brief or devtune_get_actions. It does not name an alternative tool explicitly, but the purpose itself is unambiguous.

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 qualifier 'for accounts with content drafts' implies scope of applicability, and a reader can infer you call this to inspect an existing draft. However, it never states when to prefer this over get_action_brief or when a draft won't exist, and no alternatives or exclusions are named.

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 and does so richly: it discloses the return shape (kind, from/to values, actor, createdAt), the actor variants (user, api_key, system), the limitation that older edits predate recording, and pagination behavior. This is substantial behavioral context beyond what any structured field provides.

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 purpose and scope, then progressively adds kinds, actor details, exclusions, and pagination. It is a single dense paragraph but every sentence carries information (event kinds, exclusions, history boundary, pagination), so little is wasted despite the length.

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?

No output schema and no annotations, so the description must explain return values and constraints — and it does: event kinds, from/to representations, actor types, createdAt, the history boundary, and pagination. An agent has everything needed to call and interpret the 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?

Schema description coverage is 100%, so the baseline is 3. The description adds usage meaning for cursor ('Pass nextCursor back as cursor for the next page'), which links the output field to the input parameter and is not stated in the schema. pageSize and actionId semantics are left to 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 a specific verb (List) plus resource (who changed an action's fields) and enumerates the exact fields tracked (title, owner, channels, description, published pages) with an explicit ordering (newest first). It also distinguishes itself from devtune_get_actions by excluding lifecycle moves, so an agent can route correctly without opening either 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 an explicit when-not with an alternative: lifecycle moves (accepting, going live, archiving) are not events and must be read from devtune_get_actions. It also warns that history only begins when recording started, setting expectations. It lacks a positive 'use this when...' framing, but the exclusion routing 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?

With no annotations, the description carries the full burden and does disclose substantial behavioral traits: read-only intent ('No edits, redirects or relinks have executed'), ordering (portfolioRank ascending, nulls last), truncation limitations, and null-handling for percentiles. It stops short of permissions, rate limits, or pagination mechanics beyond the ordering rule.

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?

Purpose is front-loaded, but the body is an enormous wall of domain jargon about return-value semantics that dwarfs the tool's actual operation. Much of it reads like a data dictionary rather than invocation guidance, and the sheer length hurts scannability.

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 an 8-parameter read tool with no output schema, the description is more than complete on return semantics—status meanings, percentile/status null rules, truncation flags, and ordering are all covered. If anything it is over-complete, but nothing an agent needs to call it 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 coverage is 100%, so the schema already documents all eight parameters. The description nonetheless adds meaning beyond the schema by explaining the four status values and the detailLevel 'context' payload contents, which helps an agent interpret enum choices.

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 opening sentence 'Get the actions for a project' states a clear verb and resource. However, it never differentiates this general actions endpoint from close siblings like devtune_get_agent_picks_actions or devtune_get_prompt_actions, and the purpose is buried under a sprawling data-semantics dump.

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 when-to-use versus alternatives guidance. The only routing hints are incidental ('change either with devtune_update_action', 'this tool cannot split'), which describe adjacent mutations rather than when to call this read tool over its siblings.

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

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

With no annotations, the description carries the full burden. It does disclose the data sources and competitive scope, but it does not mention default behavior (e.g., 30-day window), output format, or any constraints such as data availability. 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?

Two tight sentences with the core action and metrics first, and the scope in the second. No filler or repetition of parameter names.

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 tool with two optional parameters and no output schema, the description covers purpose, metrics, and scope. It could mention the output shape or default window, but nothing critical prevents a correct call.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning by mapping the metric enum to 'npm weekly downloads' and 'GitHub stars.' It does not add detail on the windowDays parameter, but the schema already explains the allowed values and default.

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 a specific resource ('SDK/package adoption trends') and concrete metrics ('npm weekly downloads and GitHub stars'), so an agent knows exactly what the tool returns. It also states the competitive scope, which distinguishes it from other read-only analytics tools among the many devtune_get_* 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 phrase 'adoption trends' implies when the tool is appropriate, and the scope ('primary domain and competitors') is useful context. However, it gives no explicit when-not-to-use guidance or alternatives, even though sibling tools like devtune_get_competitive_position and devtune_get_visibility_summary could overlap in an analytics context.

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/non-destructive annotations, the description discloses pagination (next_offset until null), coverage semantics (incomplete coverage explicit, aggregate evidence not first-cell), re-test state semantics (only queued/running are still_measuring; shipping alone is unmeasured; re-tests estimated and confirmed in the app), and credit accounting. That is substantial behavioral context, though it is stated as bare assertions rather than organized guidance.

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 verb and resource, and every clause carries domain meaning, but it is one long run-on paragraph of stacked particulars and jargon (Wilson 95% range, still_measuring, credited accounting) with no grouping, which hurts scanability.

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 return-shape burden and does so in detail (rendered titles/descriptions, loss rationale, pick rate and Wilson range, re-test status, Outcomes), plus pagination and required scopes. It is close to complete for this complex read tool, though the offset/next_offset relationship and the single-action mode could be stated more cleanly.

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 50%: actionId is documented ('Read one Action and its re-test result'), while offset is undocumented in the schema. The description partially compensates by describing the pagination contract ('Follow next_offset until null'), giving offset usable meaning, but it is borderline rather than a full coverage of both 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?

States a specific verb (Read) and resource (100 Agent Picks Actions) with a clear scope, and the dense field list (titles, descriptions, loss rationale, pick rate, re-test status, Outcomes) makes it distinguishable from siblings like get_agent_picks_cells or get_agent_picks_summary. It is clear what the tool returns, though it never explicitly contrasts itself with those 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?

'Follow next_offset until null' gives pagination guidance, and 'Requires Agent Picks and visibility.read' states a prerequisite. However there is no when-to-use vs when-not, and no routing to the alternative Agent Picks tools an agent would weigh against this one.

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 only declare readOnly/idempotent/non-destructive, so the description carries the interpretive burden and does so thoroughly: it explains null semantics for non-completed tests and unmeasured tools, that install_rate is null (not 0) when install_rate_hidden_reason is no_installable_tool, that fallback guesses are included in counts but excluded from loss verdicts, that outcome counts vanish when run detail is unavailable, and that the feature and visibility.read are required.

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 first sentence is well front-loaded, but the remainder is a dense, run-on enumeration of output fields packed into a handful of overloaded sentences, several of which repeat 'Each test includes...' openings. Length is partly justified by the absence of an output schema, but the structure is hard to scan.

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, no annotations beyond safety flags, and a complex payload, the description is unusually complete on return semantics: it enumerates the returned fields, null conditions, comparison_key behavior, and measurement caveats. It stops short of telling the agent how to source test_ids, the one thing needed to actually call the tool.

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

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

Schema coverage is 0% and there is a single parameter (test_ids), so the description must compensate. It constrains the parameter meaningfully — '1 to 8 distinct project-owned tests' — implying these are project-owned test UUIDs and matching minItems/maxItems, but it never defines where an agent obtains valid test IDs, leaving a real gap.

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 opening sentence gives a specific verb (Read), a specific resource (per-model cells), and a scope (1-8 distinct project-owned tests from test history), which is clear enough for an agent to identify the operation. However, it does not explicitly differentiate itself from closely related siblings such as devtune_get_agent_picks_test or devtune_get_agent_picks_history, so sibling disambiguation is left 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?

The description provides some operative guidance — 'Compare equal keys only and break history lines when they change', 'report the pick rate alone and say why' — and states the required Agent Picks feature and visibility.read permission. It never says when to choose this tool over the adjacent agent-picks siblings, so usage context is only implied.

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

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

Annotations already cover readOnly/idempotent/non-destructive, but the description adds real behavioral context: the auth requirement ('Requires Agent Picks and visibility.read'), pagination behavior ('Follow next_offset until null'), and the no_installable_tool fallback where install_rate is null rather than 0. That is genuine value beyond the annotations.

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

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

The description is a single long run-on paragraph dense with jargon, Wilson ranges, directional flags, micros, and baseline rules. Much of it explains output fields rather than helping select or invoke the tool, and it is not front-loaded beyond the first clause, making it hard to parse.

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 return-value burden and does so thoroughly, explaining pick rate, installs, install_rate_hidden_reason, directional, settled_micros, baseline_eligible, open_loss_actions and action_id. Combined with the disclosed auth requirement and pagination, it is complete enough to use, though the density works against clarity.

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 0% for both params, so the description must compensate. It does so partially: 'only the test test_id names' implies test_id selects a single test, and 'Follow next_offset until null' explains the offset's pagination role. It stops short of stating what offset counts or the default page size.

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 opening states a clear verb+resource: reading the history of paid tests (newest first, or a single test by test_id). It distinguishes the list vs single-test-id modes. However, it never distinguishes itself from close siblings like devtune_get_agent_picks_test or devtune_list_agent_picks_runs, so an agent still has to infer which 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?

There is no explicit when-to-use guidance or when-not/exclusion, and no routing to alternatives among the many agent-picks siblings. The only 'guidance' is interpretive (keep directional tests off full-test trends), which is about reading results, not about when to call this 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 declare read-only and idempotent behavior, but the description adds extensive behavioral disclosure: null semantics with *_withheld flags, pagination via evidence.pagination.next_cursor, retention rules for observations, withheld data classes, registry-snapshot limitations, and unknown states. This goes well beyond the safety hints.

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 first sentence is front-loaded and useful, but the remainder is a single dense block of implementation-level caveats. It would benefit from grouping, and its length is disproportionate even for a complex tool, so it reads as over-specified rather than concise.

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

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

With no output schema, the description carries the full return-shape burden and does so comprehensively: status, stage, model activity, pick classification, pagination, withheld/null semantics, registry comparison, and explicit unknowns. Given the tool's complexity, almost nothing an agent needs to invoke it correctly 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 coverage is 0% and the schema only defines limit, cursor, run_id, and test_id. The description explains where run_id/test_id come from and how to use cursor (follow evidence.pagination.next_cursor), but it never describes limit's page-size role or bounds, leaving one parameter undocumented in prose.

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 opening sentence names a specific verb (Inspect), resource (Agent Picks run), and scope (one project-owned run using test_id and run_id from the run list). It implicitly distinguishes this detail endpoint from the run-list and mutation siblings, though it never names the sibling tools directly.

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 tells the agent where the identifiers come from ('from the run list'), states prerequisites ('Requires Agent Picks and visibility.read'), and rules out mutation ('Cannot change or start a run'). It does not explicitly name an alternative tool for trace or list, so routing is inferential rather than 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?

Goes well beyond the readOnly/idempotent annotations: it explains availability states (stored/pending/not_retained), why a running attempt has no lines, the single 'Trace unavailable' error line, how calls become 'unplaced' with reasons, preview truncation at 65,536 chars, the 256 KiB page cap, and the Agent Picks + visibility.read requirement. Declares 'Cannot change a run', consistent with annotations.

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

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

Purpose is front-loaded, which helps, but the body is one dense, run-on block that is hard to parse and repeats pagination concepts. Information density is high but structural clarity is poor for a description this long.

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 6-parameter, paginated, no-output-schema read tool, the description covers return shape (lines, unplaced, totals, model_call items), ordering, pagination termination, availability, and permissions. An agent has everything needed to call and page 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?

With 0% schema description coverage the description carries the load and largely does: it defines limit (default 50, max 100), full, after_attempt/after_seq, and after_unplaced roles. Minor weakness is the confusing phrasing 'passing unplaced_pagination.after_id as after_unplaced', which introduces a name not present in 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?

Opens with a specific verb and resource: 'Read one page of a project-owned Agent Picks run's console trace', and enumerates exactly what the trace contains (prompt, setup, narration, tool calls, edits, errors). The scope is precise enough to separate it from run/summary siblings, though it never explicitly names an alternative 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 strong operational guidance (pagination via pagination.after_attempt/after_seq until has_more is false, 200-at-a-time unplaced reads) but never states when to reach for this tool versus devtune_get_agent_picks_run or devtune_list_agent_picks_runs. Usage is implied by the trace resource rather than contrasted against siblings.

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?

Goes well beyond the readOnly/idempotent annotations by disclosing return-value semantics: last_skipped is null when cadence is off or unskipped, the full enum of skip reasons with meanings, that test_in_flight/unavailable have null action/action_href, and that action_href is the recovery path. This is unusually rich behavioral context for a read tool.

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

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

Front-loaded with the resource and returned fields, then progressively drills into edge-case semantics. It is dense and long, but for a no-output-schema read tool almost every clause (null rules, reason enums, recovery link) earns its place; only the parenthetical reason glosses edge toward verbosity.

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 describing return values, and it does so completely — cadence, next run, skipped period, reasons, actions, and credit estimates. Combined with annotations covering the safety profile, an agent has everything needed to call and interpret it.

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?

Zero parameters, so the baseline is 4; there is nothing for the description to disambiguate, and it correctly adds no parameter-level noise.

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

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

States a specific verb (Read) and resource (the project test cadence and paid-test estimate), then enumerates the exact fields returned: next UTC run, newest skipped period with reason, action/action_href, and per-run and monthly credit estimate. This is clearly distinguishable from the write-side sibling devtune_set_agent_picks_schedule and from other agent-picks getters.

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 tells the agent when to use it ('Read this estimate before setting a schedule') and states the prerequisites (Agent Picks and visibility.read), which routes the agent relative to the set-schedule sibling. It lacks an explicit 'when not to use' or named alternative beyond the scheduling pointer.

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 read-only/idempotent/non-destructive, and the description goes well beyond them: null semantics for test/tools/model_calls, freshness ('live per-run results, not rates'), stage lifecycle values, and the caveat that model_spend_incomplete marks a lower bound rather than a final bill. These edge-case disclosures materially change how an agent should interpret and report the data.

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?

Purpose is front-loaded, but the body is a single dense ~250-word paragraph mixing request semantics, outcome enums, tool_key explainers and spend caveats with no bullets or headings. Much of the content is necessary absent an output schema, but the run-on structure hurts scannability.

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 return values and does so comprehensively: every field (test, runs, outcome, tools, stage, model_calls, model_spend_usd) is defined including its null/edge-case behavior. An agent has what it needs to call and interpret the 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 takes zero parameters and the schema is empty, so there is nothing for the description to disambiguate; baseline 4 applies. No missing parameter meaning is a gap here.

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 and resource: 'Read the project's Agent Picks starter test as it runs, run by run,' and distinguishes the starter-test concept from a full own test. It does not explicitly route against the closest siblings (devtune_get_agent_picks_test, devtune_get_agent_picks_run), which is the only thing keeping it from a 5.

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 names an alternative with a clear condition: 'for install rates with their likely ranges, read devtune_get_agent_picks_summary,' and adds a constraint ('a starter never displaces a full own result or feeds loss Actions, re-test baselines or trends'). No explicit when-to-prefer-this-over get_agent_picks_test/run, so it stops short of 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/idempotentHint, so the safety profile is covered. The description still adds real behavioral context beyond that: status no_settings/no_test states, the rule that re-tests are never shown, and that 'a re-test, unfinished test or another project's test is not found'. That error/edge-case disclosure is genuine value on top of the annotations.

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

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

Roughly 600 words delivered as a single unstructured paragraph. Although no output schema exists so much return-field explanation is unavoidable, the content is not grouped or front-loaded, and dense sentences repeat concepts repeatedly, making the critical usage rules hard to extract.

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 this description carries the full burden of describing the response and it does so exhaustively: rate/rate_low/high (Wilson), stated_picks, installs, install_rate and its hidden reason, built_its_own, no_provider, outcome, is_own, frameworks, agents, environments, cells, coverage, incomplete_cells, status, job, and open_loss_actions are all explained.

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

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

Schema coverage is 100%, so the baseline is 3. The description goes further by clarifying filter effect ('Pass agent and/or intent to narrow every number to those runs; filters echoes what was applied') and the intended source and scope of test_id, adding semantics the schema alone does not convey.

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 opening sentence gives a specific verb, resource and scope: 'Read Agent Picks: which tool coding agents ... installed when asked to add one in the project's category, from install-verified test runs.' An agent immediately knows the domain. However, it never contrasts itself with the very similar siblings devtune_get_agent_picks_test and devtune_get_agent_picks_cells, so differentiation relies on 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?

The description routes to devtune_get_agent_picks_history for the test_id parameter and lays out the data-selection precedence (own non-directional > own directional > cloned public > latest public). But it gives no explicit when-to-use/when-not guidance relative to the other agent-picks siblings, leaving the choice among them mostly implied.

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

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

Annotations already declare readOnly/idempotent/non-destructive, but the description adds substantial domain behavior: credit-to-micros conversion, that failed and pre-start-cancelled runs are free, that post-start cancellations pay recorded spend, that credits return at test end, and the directional=true exclusion semantics. This is real value beyond the safety profile, though the charge formula is dense and unverified.

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 first sentence is well front-loaded and names the returned fields, but the body then dumps several billing edge cases (unknown-spend formula, cancellation charging rules) that read more like documentation than selection criteria. It is overstuffed rather than lean.

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 return values, and it does name the estimate fields and per-run table plus the directional flag meaning. Given the tool's complexity, it is largely complete, though it could clarify the shape/structure of the per-run table.

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 takes zero parameters, so the schema offers nothing to clarify; baseline for a no-param tool is 4. No parameter-level guidance is needed or missing.

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

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

States a specific verb (read) and resource (latest paid Agent Picks test status) and enumerates the returned quantities (estimate, reserved/settled/refunded micros, per-run table). It partially differentiates from siblings by routing install rates to devtune_get_agent_picks_summary and noting it cannot start a test, though other close siblings (get_agent_picks_run, get_agent_picks_history) go unmentioned.

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?

Usage is implied (read the latest paid test's rollup) and one alternative is named for install rates, plus the negative constraint 'cannot start a test'. But there is no explicit when-to-use vs the many adjacent siblings like get_agent_picks_run or list_agent_picks_runs, leaving selection partly to inference.

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

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

No annotations are provided, so the description carries the burden. It discloses meaningful behavior: draft output only appears after the run completes, superseded runs retain output, and supersededByRunId/supersededActions relationships are explained. It doesn't mention auth or rate limits, but the lifecycle disclosure is solid.

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 tight sentences, each adding distinct content (purpose, completion condition, supersession semantics) with no filler. Front-loaded with the core action.

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

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

For a simple one-param read tool with no output schema, the description covers the notable lifecycle edge cases but omits where a runId comes from and what the full response shape includes, leaving an agent to infer the entry point.

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

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

Schema description coverage is 100% and the single runId param is fully documented in the schema, so the baseline is 3. The description adds no further constraint or format detail about runId 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?

States a specific verb+resource ('Get one managed-agent run') and clarifies what it returns (draft output, only after completion). It doesn't explicitly name the sibling devtune_list_agent_runs it differs from, though 'one' vs 'list' implicitly distinguishes it.

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 explains output semantics but gives no when-to-use/alternative guidance. It never says to use devtune_list_agent_runs to find a runId, or when this is preferable over listing runs.

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 and does disclose substantial behavior: coverage-gap semantics, that noSiteVisitOccurrences are not gaps, that GA4 key events are omitted, that visitorShare is null (never 0) with a reason, partial/priorPartial flags, and that sessions are not people. It omits any auth or permission context, but the semantic edge cases are unusually well documented.

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 purpose sentence is front-loaded, but the remainder is an enormous run-on packed with undefined jargon (windowComplete, unattributedSessions, priorShare) in a single dense paragraph. It is over-specified and hard to parse rather than tight, so conciseness suffers.

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 complex analytics tool with no output schema and no annotations, the description goes a long way toward explaining the returned shape and null/edge-case handling. What remains missing is pagination/auth framing, but the return-value semantics are otherwise well covered.

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 cursor, pageSize and windowDays are already fully documented in the schema. The description adds only the 'same fixed window' concept, which is marginal beyond what the schema provides, making the baseline 3 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 first sentence states a specific verb (Get) and resource (AI-referral landing pages, overview, engines breakdown) with the fields it returns. It is clearly about AI-referral analytics, but it never names or contrasts with siblings like devtune_get_traffic_summary or devtune_get_page_metrics, so differentiation is left 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?

There is no explicit when-to-use or when-not-to-use guidance, and no alternatives are named. The dense field-level explanation implies this is the AI-referral reporting tool, but an agent gets no routing help against the many traffic/visibility siblings.

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

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

No annotations exist, so the description must carry the behavioral burden. It does: clarifies stored crawl output, that it does not reproduce a renderer, does not verify unseen images, and can return unavailable/stale. It doesn't mention mutation/side effects, but 'Read' plus stored data implies a safe lookup.

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?

Four tight sentences cover purpose, required inputs, return values, edge cases, and limitations with no redundant filler. Easy to parse and highly actionable.

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 read-only retrieval tool with no output schema or annotations, the description covers inputs, outputs, staleness, and key limitations. It doesn't detail failure response formats or prerequisites for audit findings, but the essentials are all present.

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 has 0% descriptions, so the description must compensate. It names all five required parameters and tells the caller where they come from ('from audit findings'). It doesn't define each hash or the locator pattern, but the names and schema patterns are identifiable and the provenance guidance is a meaningful addition.

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

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

States a precise read operation on a single artifact ('extracted content for one section finding'), names the five identifying inputs, and enumerates the exact return payload. This clearly sets it apart from broader get/list/update 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?

Explicitly tells the caller to supply the five fields from audit findings and sets expectations for unavailable/stale results. Could name sibling tools for routing, but the provenance instruction is strong enough.

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

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

With no annotations, the description carries the transparency burden and does well: it discloses pagination (including independent pagination for section assessments), the source object, the categories of data included, and explicitly states that these findings do not alter numerical scores. It does not cover auth requirements or failure modes, but for a getter this is a strong disclosure.

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 purpose, with no filler. The scope and the critical non-scoring behavior are stated compactly.

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 high-level purpose is clear, but with no output schema and no annotations the description should also convey return shape, pagination parameter behavior, and what includeDismissed means. It partially describes returned contents, but an agent still lacks enough detail to invoke it confidently.

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 only 33% because only pageSize has a description, and the description does not compensate by explaining offset or includeDismissed. It repeats the notion of pagination but adds no semantics for the undocumented parameters beyond what the schema already 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 names a specific verb and resource: 'Get paginated audit findings and independently paginated section assessments from the published Audit rollup.' The detail about exact excerpts, remedies, versions, and coverage states disambiguates it from the sibling devtune_get_audit_summary and other get_* 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 gives context (published Audit rollup, findings vs. numerical scores) that implies when to use it, but it never explicitly states when-not-to or names an alternative like devtune_get_audit_summary. The 'do not change numerical scores' note is an indirect exclusion rather than an explicit routing instruction.

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

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

No annotations are provided, so the description must carry behavioral disclosure. It clearly explains date boundaries, UTC vs. Pacific, partial evidence, and the rule not to call partial data current, nor pad GSC values. This is a rich behavioral contract for the tool.

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

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

The description is dense but not overly long; it packs essential caveats into a few sentences. It front-loads the core purpose and then adds important accuracy constraints. No fluff, but could be slightly more structured.

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

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

Given the single optional parameter and no output schema, the description explains key context: date formats, timezone boundaries, partial coverage, verification states, and GSC limits. An agent has enough information to call it correctly and avoid misrepresenting data.

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

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

Schema description coverage is 100% for the only parameter, windowDays, which is well documented in the schema with enum values. The description does not add new meaning about the parameter beyond the schema, so baseline 3 is appropriate.

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

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

The description clearly states that the tool returns audit summaries and trends, with specific field semantics. It distinguishes it from related tools like get_audit_findings by focusing on summary-level trends. However, it does not explicitly name a sibling tool as an alternative.

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

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

The description implies usage context through coverage and date handling rules, but does not explicitly state when to use this tool versus alternatives. It gives guidance on interpretation (e.g., when isCurrent false), but the when-to-use vs. when-not-to-use is absent.

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 does add meaningful context: the tool is a backward-compatible alias, and it explains the coverage-state semantics (empty list is not a measured zero). However, it doesn't disclose pagination behavior, rate limits, or what 'citation analysis' specifically returns beyond top pages. The alias relationship is useful but the behavioral detail is thin.

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 at three sentences and front-loads the most important fact (backward-compatible alias) before the usage directive. The coverage-state sentence is slightly dense but earns its place by explaining a non-obvious semantic. No wasted words.

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

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

For a simple list tool with 100% schema coverage and no output schema, the description is mostly adequate. It explains the alias relationship and the coverage-state caveat, which are the non-obvious parts. However, it doesn't describe the return shape or how the alias differs behaviorally from the preferred tool, and with no annotations, an agent might not know whether this is a read-only operation (though the name suggests it is).

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 (cursor, search, pageSize, windowDays). The description adds no parameter-specific meaning beyond the schema, but the baseline of 3 is appropriate because the schema does the heavy lifting. The description's mention of 'coverage state' relates to windowDays but doesn't add syntax or format details.

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 this as a backward-compatible alias for top citation pages in AI search results, with a specific verb ('get') and resource ('citation analysis' / top citation pages). It distinguishes itself from the preferred sibling devtune_get_top_citation_pages, though it doesn't fully explain what 'citation analysis' means beyond that alias relationship.

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 says to prefer devtune_get_top_citation_pages for new workflows, which is a clear usage directive. It also explains the coverage-state semantics (unavailable window not rolled up yet), which helps an agent interpret results. However, it doesn't state when to use this alias over the preferred tool (e.g., legacy workflows only) or mention alternatives like devtune_get_top_citation_domains.

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 of behavioral disclosure. It adds a valuable nuance about the coverage state, clarifying that an unavailable window has no stats rather than a measured zero. However, it does not mention side effects, permissions, or other behavioral traits, though for a get operation these may be assumed.

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

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

The description is two sentences with no unnecessary words. The primary purpose is front-loaded in the first sentence, and the second sentence adds a meaningful behavioral nuance. Every sentence earns its place.

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

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

For a simple get tool with one parameter and no output schema, the description is fairly complete. It covers the return content and a key behavioral detail. It does not explicitly mention the default window or that it operates on the current project, but these are either in the schema or implicit. Overall, adequate for the tool's complexity.

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 fully describes the single parameter 'windowDays' with its enum values and default. The description adds no additional meaning beyond the schema, so the baseline of 3 is appropriate given 100% schema coverage.

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

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

The description clearly states the verb 'Get' and the resource 'aggregate citation statistics' with a specific list of metrics (total citations, unique pages, unique domains, average position, top-three count, citation class totals). It is specific and informative, but it does not explicitly differentiate from sibling tools like devtune_get_citation_analysis, which may overlap in purpose.

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

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

No guidance is provided on when to use this tool versus alternatives such as devtune_get_citation_analysis or devtune_get_top_citation_domains. The description only explains what the tool does, not when to choose it over siblings, leaving the agent to infer usage context.

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 and delivers: it discloses auth requirements ('Requires project.read and MCP code mode'), what is included and excluded (completed/rejected/uncertain operations, no code/arguments/results), and a critical safety semantic ('a running receipt may survive a controller failure; never infer that its write did not happen'). That uncertainty/idempotency warning is exactly the kind of behavioral context an agent needs for a mutation-adjacent read.

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

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

Roughly four sentences, front-loaded with purpose followed by output contents, safety warning, and workflow/prerequisite notes. Every sentence contributes, though the sentence about reusing code in execute is slightly tangential to a pure read operation.

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 1-parameter read tool with no annotations and no output schema, the description supplies purpose, returned data scope, prerequisite permissions, and the crucial uncertainty caveat. It could say more about the receipt's structure, but the essential context an agent needs to invoke it correctly is present.

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 0% for the single executionId parameter, so the description must compensate. It adds meaning by calling it 'the original executionId' and tying it to one credential/project's execution and to re-invoking execute, but it does not clarify format or matching behavior beyond what the schema's uuid type already implies.

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 gives a specific verb ('Read'), a precise resource ('operation receipts'), and an explicit scope ('for one code-mode execution made by this same credential and project'). No sibling among the 60+ tools covers code-mode execution receipts, so the purpose is unambiguous and separable.

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 real usage context: reuse the original executionId and code in execute to retrieve a recorded response 'without repeating changes,' which tells the agent when this tool is the right choice. It stops short of stating exclusions or naming a directly competing tool, so it is clear context rather than full when/when-not 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 zero annotations the description carries the full burden, and it does disclose real quirks: the default aggregates only active project platforms, totals cover every platform in the window, timeSeries silently omits sub-daily-cadence platforms (e.g. weekly Claude/Grok) unless they are the only ones requested, and platformCadences can be null. It stops short of permissions or error behavior, but the output/aggregation gotchas are genuinely useful.

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?

Purpose is front-loaded in the first sentence and every subsequent clause carries information rather than filler. The second half is dense and jargon-heavy (shareOfVoice, primaryShareOfAllCitations, platformCadences), but it is compact for the amount of return-shape detail conveyed.

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, so the description usefully explains what each returned series/metric includes and when platforms are omitted from timeSeries. Combined with the fully-documented parameter schema, an agent has enough to call and interpret the tool, though which parameters select which metric is left implicit.

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 five parameters are already documented and the baseline is 3. The description adds little parameter-specific detail beyond hinting that inactive platforms are excluded by default (relevant to includeHistoricalPlatforms), without clarifying windowDays/grouping interactions.

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

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

States a specific verb and resource ('Get competitive positioning data') plus the data scope (primary, competitor, other citations), so the agent knows what it returns. However, it never differentiates itself from the closely-named sibling devtune_get_positioning or from citation tools like get_citation_stats, leaving sibling disambiguation 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?

The description explains what the metrics mean but never says when to call this tool versus alternatives such as get_positioning or get_citation_analysis. There is no explicit when/when-not guidance; the closest thing is a default-behavior note about active platforms, which is behavioral rather than 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 provided, the description carries full responsibility for behavioral disclosure. It reveals important semantics: existingMeasurementId does not imply a claim, and path filters affect list totals while summary covers the full window. These are non-obvious behaviors that an agent needs to interpret results correctly. However, it does not mention error handling, rate limits, or whether the operation is read-only (though 'List' implies read-only). The provided details are valuable and go beyond typical descriptions.

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 three sentences with no filler. It front-loads the core purpose, then adds essential behavioral details. The structure is logical: what the tool returns, the meaning of existingMeasurementId, and scoping behavior. It is concise yet information-dense, though it could be slightly more structured with bullets, but overall it is efficient.

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 there is no output schema and no annotations, the description should ideally describe the response format, error conditions, or authentication requirements. It does explain the key output elements (claim tokens, IDs) and window behavior, but it does not mention pagination behavior beyond cursor (which is in schema), error handling, or any side effects. For a low-complexity list tool, this is moderately complete but leaves some gaps an agent might need.

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 descriptions cover 75% of parameters (cursor, pageSize, windowDays), leaving 'path' undocumented. The description compensates by explaining path filter behavior ('Path filters apply to list totals; summary covers the full window') and window semantics ('Fixed reporting windows; 14-day comparisons'). This adds meaning beyond the schema's enum and default descriptions, particularly for path and the interpretation of windowDays.

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 verb 'List' and the resource 'detected content changes', and specifies the returned items (manual claim tokens and measured-action IDs). This makes the purpose unmistakable. It does not explicitly name a sibling alternative, but the content type (content changes with claim tokens) is distinct from other get_* tools like get_content_gaps or get_actions, so an agent can infer differentiation without explicit naming.

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 operational constraints (fixed reporting windows, 14-day comparisons, path filter behavior) but does not explicitly state when to use this tool over alternatives or when not to use it. There is no mention of prerequisites or context like 'use when you need to audit content changes for a page'. The guidance is implied from the purpose but not explicit, so it earns a mid-range score.

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?

Given no annotations, the description carries full burden. It discloses the fixed window rollups, the exclusion logic, that counts are citations not mentions, and that coverage is measured against availableDays not the requested window. This prevents incorrect interpretation of results and is highly transparent about the data's limitations.

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 dense but organized: it starts with the main purpose, then exclusions, then data semantics, then contract scope. Every sentence adds critical information for correct usage. It is somewhat long but every part is necessary given the tool's complexity, so it earns a 4.

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 inherently complex with subtle data semantics (rollups, exclusions, citation vs mention). The description covers all the essential details an agent needs to correctly interpret results. No output schema exists, but the description explains what the data represents (citations, coverage days, asOfDate), making it complete for correct usage.

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

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

Schema coverage is 100%, so schema already documents all parameters. The description adds context about gapType meaning but doesn't go into detail about each parameter beyond what schema provides. It does clarify that windowDays only accepts 30/90 and defaults, which is in schema. The description's contribution is minimal beyond the schema, so a 3 is appropriate.

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

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

The description clearly states the tool measures specific content gap types (topic, source, brand) from fixed 30/90-day rollups. It distinguishes itself by specifying what is excluded and that it reports citations split by primary/competitor. This clearly differentiates from siblings like devtune_get_citation_analysis and devtune_get_citation_stats.

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 states what is excluded (owned sources/brands, topics with majority citations) and what is not part of the contract (pipeline scores, summaries). This guides an agent to know when to use this tool vs others in the same domain, though it doesn't name alternatives directly.

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

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

No annotations are provided, so the description carries the full burden. It states the operation is a read ('Read'), that events are 'persisted', and that it returns an 'ordered page'. This discloses it is a non-mutating, paginated read. However, it does not mention error behaviors, ordering specifics, or what happens for invalid thread IDs. Basic transparency is present but incomplete.

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

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

The description is a single, tightly written sentence that front-loads the action and resource. There is no fluff or redundant wording. It earns its place but could have been expanded with more behavioral detail without losing conciseness.

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

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

For a tool with three parameters and no annotations or output schema, the description is too thin. It does not specify what constitutes a 'Dex reply' or 'lifecycle event', how pagination works (cursor vs offset), or what the response shape is. An agent would need to infer too much about ordering, filtering, and usage. The description is insufficient for reliable invocation.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'ordered page' and 'conversation thread', which hints at pageSize and threadId, but it does not explain the 'after' parameter (presumably a cursor) or explicitly map parameters to their roles. The description adds minimal value over the schema's type constraints, leaving the agent to guess parameter 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?

The description clearly states the action (read), the resource (persisted Dex reply and lifecycle events), and the scope (one project-scoped conversation thread). It is specific enough to distinguish from the many sibling get_* tools, though it doesn't explicitly name an alternative. The verb+resource combination is unambiguous.

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

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

There is no guidance on when to use this tool versus other tools like list_dex_threads or get_agent_run. The description only explains what it does, not when it should be chosen. It implies usage for reading events for a thread but lacks any explicit exclusions or alternative 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 provided, the description carries the full burden of behavioral disclosure. It explains that the data comes from 'fixed-window rollups', is 'complete-population', includes 'bounded page details', and emphasizes the non-additive nature of lift. This provides meaningful context about how the tool behaves without relying on structured metadata.

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

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

The description is two sentences with no redundancy. The main purpose is front-loaded, and the second sentence adds an essential nuance about the nature of the lift metric. Every phrase earns its place without any 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?

The description explains the domain-specific concepts of 'Direct lift' and clarifies its relationship to measured referrals, which is crucial for correct usage. However, since there is no output schema, it does not describe the return structure beyond 'bounded page details', which is a minor gap given the overall clarity.

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 both parameters (pageLimit, windowDays) already have descriptive text in the schema. The description adds no extra meaning to the parameters beyond what the schema provides, only indirectly referencing 'bounded page details' and 'fixed-window rollups'. 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 uses a specific verb 'get' and names the exact resource: 'complete-population GA4 Direct traffic and resolver-owned AI-correlated lift classifications'. It clearly distinguishes this from siblings by explaining it is 'correlated evidence, not attributed traffic' and 'never added to measured referral totals', which sets it apart from tools like get_ai_referrals or get_traffic_summary.

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

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

The description provides a clear when-not by stating the output is 'never added to measured referral totals', implying it should not be used for referral attribution. It also clarifies the semantic of 'lift' as correlated evidence, which guides selection. However, it does not name any specific sibling tools or give explicit conditions for when to use this 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?

No annotations are supplied, so the description bears full responsibility for disclosing behavior. It only states the source and categories of retrieved data; there is no information about side effects, read-only expectation, computation involved, rate limits, or output fidelity. For a read-style tool this is minimal and leaves behavioral assumptions to the agent.

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, efficient sentence that front-loads the verb and clarified resource. It is not redundant and earns its words. It loses a point because the three data categories are packed together without any formatting that could make them scan more clearly.

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

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

With no output schema and no annotations, the description must carry return-format expectations. It lists the three data categories being returned, which provides a basic notion of output content. However, it leaves terms like 'confirmed transitions' and 'sitemap submission evidence' undefined, and does not specify the structure or aggregation, so completeness is just adequate.

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 provides full coverage of the two parameters with descriptions, defaults, constraints, and enums. The description's phrase 'fixed-window facts' minimally reinforces the windowDays parameter but adds no meaningful insight beyond the schema. Baseline 3 is appropriate when schema covers 100% of 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 uses a specific verb and identifies three concrete data resources: Google eligibility dimensions, confirmed transitions, and sitemap submission evidence. It also names a source scope, fixed-window facts, which distinguishes it from sibling analytics tools. However, it does not explicitly contrast it with a sibling or scope out what it does not return.

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 the many other devtune_get_* siblings. The description gives no selection criteria, prerequisites, or examples of contextual use. An agent is left to infer its purpose from the name alone.

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 behavioral burden. It discloses that presence-rate values are ratios from 0 to 1 and can be null when data is unusable, which is useful output context. However, it does not state whether this is a read-only operation, whether it requires specific permissions, or any rate limits or 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 two sentences, front-loaded with the core purpose and followed by a crucial data caveat. It is efficient and wastes little space.

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

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

For a tool with no annotations, no output schema, and two optional parameters, the description is adequate but not complete. It explains the forecast output format (ratios, nulls) but omits usage context, read/write nature, and how the window and horizon parameters affect results. An agent can call it, but with some uncertainty about the semantics.

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 description adds no additional parameter meaning beyond what the schema provides – it doesn't explain how windowDays and horizonDays interact or the implications of different values.

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 (forecast) and resources (short-term primary citations and presence-rate direction) using recent project visibility trends. It distinguishes itself from siblings like devtune_get_citation_analysis or devtune_get_visibility_summary by being predictive rather than descriptive. However, it doesn't explicitly contrast itself against those 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?

There is no explicit guidance on when to use this tool versus alternatives. The phrase 'short-term' is the only temporal hint, but no conditions or exclusions are provided. An agent must infer that this is for forecasting, but when to choose it over other intelligence or citation tools is unclear.

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?

There are no annotations provided, so the description must disclose behavior. It says 'Read' which implies read-only, but does not mention side effects (e.g., whether it marks proposals as viewed), nor any privacy or scoping details (e.g., whether it requires a project ID or works on the current project). No output format is described.

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

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

The description is a single sentence, concise and front-loaded with the main action and resource. It is not overly verbose, though it could list the categories more clearly.

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 the tool has no output schema and annotations are absent, the description leaves gaps: pagination (limit/cursor) is not fully explained, and the response structure is unknown. However, the tool is fairly simple, so a 3 is appropriate.

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 50% (cursor is described, limit is not). The description does not explain 'limit' or 'cursor' further, but the schema already covers cursor. For 'limit', the agent has to infer it is pagination size. The description adds little 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 verb 'Read' and the resource 'Knowledge Profile', and explicitly mentions the three categories of proposals (pending, approved, dismissed). This is specific and distinguishes it from sibling tools like devtune_review_knowledge_proposal and devtune_update_knowledge_profile.

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

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

The description gives no explicit guidance on when to use this tool versus its siblings. However, the context of 'read' and the mention of proposals implies it is for viewing state before making decisions, but this is not stated. No alternatives or exclusions are mentioned.

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 and does so extensively: fixed 14-day before/after comparison windows, overlappingActions scope with and without interventionId, available/unavailable comparison states, the 'shared page movement cannot be attributed to this action alone' caveat, and the redirect404 state machine including counter_changed nulling and coverage requirements. This is unusually rich behavioral disclosure.

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 ~450-word wall of text with no paragraph breaks, front-loading the purpose but then burying it under redirect404 minutiae, state enumerations, and edge-case handling. Much of the redirect-404 detail is implementation trivia rather than selection/invocation guidance, so many sentences do not earn their 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?

There is no output schema, so the description must explain return values, and it does so thoroughly (evidence fields, overlappingActions, comparison states, analytics-attributed conversions, redirect404 evidence). It is nearly complete for a complex read tool, missing only usage/routing context relative to siblings.

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 83%, so the schema already documents cursor, source, pageSize, and status. The description adds genuine meaning for interventionId ('covers that measurement's 7, 14, 30 and 90 day comparisons, even outside the selected ledger cohort') and residual window behavior, but most parameters are fully covered by the schema, keeping this near the baseline.

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 opening sentence gives a specific verb and resource: 'Get measured actions' with their authoritative result categories, evidence, and a conclusive-measurement improvement rate. It is distinctive from generic reads like devtune_get_actions or devtune_get_what_works, though it never names or contrasts those siblings directly.

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 when-to-use guidance, no when-not-to-use statement, and no reference to the many sibling tools that also surface action/outcome data (devtune_get_actions, devtune_get_what_works, devtune_get_direct_lift). Window/filter behavior is described, but nothing tells the agent when this ledger is the right call over an 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?

No annotations are provided, so the description carries the full disclosure burden. It does reveal the pagination trait and a 'bounded' data source, but omits return format, what 'bounded' concretely means, rate limits, and any consistency guarantees. For an unannotated read tool this is thin coverage.

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 efficient sentence with no filler, and the key trait 'paginated' is front-loaded. It is concise rather than under-specified, though it could have used its brevity to carry more useful context.

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 read-only getter this is minimally adequate, but the description never clarifies what 'guard-rail classifications' are or what the 'bounded rollup' refers to. With no output schema and no annotations, an agent gets little beyond the fact that results are paginated.

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 only 50% (offset is entirely undocumented, while pageSize has defaults). The description does not compensate: 'paginated' implies the offset/pageSize pair but adds no meaning beyond the schema, leaving the offset parameter's semantics unexplained.

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

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

The description uses a specific verb ('Get') and resource ('page guard-rail classifications') and correctly discloses pagination. However, among roughly fifty devtune_get_* siblings, it offers no differentiation — 'from the same bounded rollup used by DevTune' is vague since DevTune is the product itself, making the qualifier circular rather than distinguishing.

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 select this tool versus the many sibling getters (get_page_metrics, get_content_changes, etc.). The phrase 'same bounded rollup used by DevTune' hints at a shared data source but names no alternative and gives no exclusion conditions, leaving the choice entirely to inference.

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

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

With no annotations provided, the description carries the full burden. It discloses that data may be partial, that isCurrent may be false, and that coverage ranges should be reported. It explains the semantic meaning of the returned data, such as asOfDate and source dates. This is transparent about the data's nature, though it doesn't cover pagination or error 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?

The description is a single dense paragraph, not front-loaded with the most critical info (though it starts with the core purpose). It packs many details about data semantics and conditions, which is valuable but makes the text long and somewhat difficult to parse quickly. It could benefit from structured bullet points, but every sentence contributes meaning.

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

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

Given the tool's complexity (6 params, no output schema, no annotations), the description thoroughly explains data freshness, coverage, and validity conditions, giving the agent a clear picture of what the response represents. It hints at response fields (isCurrent, asOfDate, GSC columns). However, it lacks any guidance on pagination or sorting via cursor and sortKey, and it doesn't describe the full response structure.

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

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

The schema has descriptions only for pageSize and windowDays (50% coverage). The tool description adds no information about any parameter, including cursor, sortKey, category, and sortDirection, which are undocumented. With 50% coverage, the description should compensate but does not, leaving parameter semantics incomplete.

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

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

The description clearly states the tool retrieves unified page-level metrics (search, citation, referral, citability, guard-rail) from Audit rollups. This is specific and distinguishes it from other metric tools in the sibling list by focusing on page-level and Audit rollup origin, though it does not explicitly name a contrasting 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?

The description provides detailed guidance on data freshness (D-3 Pacific source dates), coverage caveats (published Audit snapshot may lag), and when page GSC columns are valid (only when availability is measured or verified_zero). It instructs to report partial coverage and compare periods only when verified. This gives clear usage context, though it doesn't explicitly point to alternative tools.

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

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

No annotations exist, so the description carries the full burden and does well: it declares 'Read-only', states 'Requires outcomes.read', clarifies that tracking capacity does not block outcome measurement, and explains that allowanceStatus counts are null when not measured. It does not cover pagination or the shape of the returned URL list.

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?

Purpose is front-loaded, but the middle sentences are dense and mix concerns (plan_limit meaning, upgrade remedy, plan determining crawl allowance) in one clause-heavy line. With no output schema the field-level detail is defensible, yet the passage could be tighter and better structured.

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

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

With no annotations and no output schema, the description supplies the key missing context: read-only safety, required scope, allowanceStatus values (at_limit/beyond_plan) and their null behavior, and remediation. The return shape of the saved-URL list itself 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?

The single actionId parameter has 0% schema description coverage, but the description compensates: it ties actionId to 'a completed action' and explicitly says to omit it for project-wide capacity and allowance status. That is meaningful semantics beyond the bare uuid type.

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

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

States a specific verb (read) and precise resources: saved published URLs, page-tracking capacity, and crawl availability for a completed action. An agent can distinguish it from get_page_metrics or get_page_guard_rails by the tracking/allowance focus, though no sibling is named 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?

Gives concrete conditional usage: 'Omit actionId for project-wide capacity and allowance status' tells the agent when to scope to a project vs a single action, and 'Never ask the customer to resubmit a saved URL' plus the plan_limit upgrade remedy define how to act on the result. It stops short of explicitly contrasting with alternative 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 only cover the safety profile (read-only, idempotent, non-destructive), and the description adds substantial context beyond that: current_set_id null/zero semantics, per-point competitor_set_id and is_current_set, the rule to keep different sets separate, null shares below ten assignments, denominator exclusions, and prior_status/prior_window comparability logic. This is rich disclosure an agent could not derive from structured fields.

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 purpose is front-loaded in the first sentence, and the remaining sentences carry real information about return semantics rather than filler. It is dense and slightly run-on, but every sentence earns its place given the absence of an output schema.

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 does the heavy lifting on return values: set identity, trend-point fields, null-share thresholds, and prior-window comparison rules are all covered. Input parameters, however, remain unexplained, leaving a modest 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 0% for three parameters. The description indirectly explains the window (30/90 days) but says nothing about the 'intent' or 'platform' parameters, so it only partially compensates for the coverage gap.

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

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

States a specific verb (read) and resource (positioning match counts and share) with an explicit scope (30 or 90 days). It is clear what the tool returns, though it never distinguishes itself from adjacent siblings like devtune_get_competitive_position or devtune_list_positioning_statements.

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 when-to-use guidance and no mention of alternative tools for positioning or competitive data. The description focuses entirely on interpreting results, not on selecting this tool over siblings.

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 behavioral burden extremely well: it explains that a paused project starts no probes, agents, crawls, audits or data syncs, drops new AI Traffic, grants read-only credentials, and refuses writes until resumed. It also covers credit settlement timing, null semantics for trackedPages and deletion timestamps, deletion cancellation via resume/resubscribe, and universal credential availability.

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 primary purpose is front-loaded, followed by dense field-level semantics and paused-project behavior. At eight sentences it is long, but nearly every sentence adds behavioral nuance that compensates for the absent output schema, so it remains structurally justified.

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 no annotations and no output schema, the description must carry all context and does: status, per-figure periods, paused side effects, credit accounting, null semantics, deletion scheduling and cancellation, and auth availability. Nothing essential for correct invocation or interpretation is missing.

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

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

The tool takes no parameters, so the baseline is 4. There are no parameter semantics to add, and the schema coverage figure is vacuous for a zero-parameter schema.

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

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

The description states a specific verb ('Read') and a clearly scoped resource ('whether the project is paused and its usage'), then enumerates the exact figures returned (active prompts, credits, AI Traffic events, tracked pages). This project-level usage/pause resource is distinct from the many per-metric siblings in the list.

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

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

It offers no explicit when-to-use guidance, no named alternatives among siblings such as devtune_get_page_metrics or devtune_get_traffic_summary, and no exclusions. Usage is implied by the purpose and field list, which is the minimum-viable level for this dimension.

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

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

With no annotations, the description carries the burden of disclosing behavior. It does say this is a listing operation and mentions the returned information, which suggests a read-only action. It does not address pagination, ordering, empty results, authorization, or whether 'existing and suggested' are returned as separate categories.

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 short sentences with no filler. The main operation is front-loaded, and the return-value sentence adds useful information without redundancy.

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

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

For a single-parameter read-only tool, the description covers the core purpose and a brief return summary. However, there is no output schema and no annotation context, so the vague 'brief readiness' and the lack of any mention of alternatives or parameter details leave minor but real gaps for an agent deciding whether and how to invoke it.

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 0%, so the description must compensate for the undocumented promptId parameter. The tool name and the phrase 'includes a prompt' make the parameter's role reasonably clear, but the description never explicitly states that promptId selects the prompt whose actions will be listed, leaving some inference to the agent.

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

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

The description states a specific verb ('List'), a precise resource ('existing and suggested actions whose stored evidence includes a prompt'), and the return content ('action status and brief readiness'). This filter by prompt evidence distinguishes it from siblings like devtune_get_actions, so an agent can tell what this tool is for.

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 is implied: call this when you have a promptId and need actions whose evidence involves that prompt. However, there is no explicit guidance about when NOT to use it or how it compares with alternatives such as devtune_get_actions or devtune_get_action_brief.

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

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds significant context: it never starts analysis, settles credits, or applies proposals (reinforcing side-effect-free), requires visibility.read (auth), details pagination via supplying the returned run.id, and notes Slack execution is unsupported. This goes well beyond the annotations.

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

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

The description is three concise sentences with no repetition. It front-loads the core purpose, then adds behavioral constraints and prerequisites. Every sentence contributes distinct information, making it efficient and well-structured.

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

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

For a read-only tool with 3 optional parameters and no output schema, the description covers purpose, side effects, auth requirements, pagination, and environment limitations. An agent has enough information to call it correctly, including how to navigate subsequent pages via run.id.

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 0%, so the description must compensate for all parameters. It only mentions run.id for pagination and does not explain offset or pageSize at all. Since these are optional and self-explanatory as pagination controls, the lack of explicit guidance is a notable gap given the low coverage.

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

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

The description clearly states the tool reads project-scoped analysis state, assessments, proposals, and credit estimate/balance. It specifies the verb 'Read' and the resource, and explicitly distinguishes it from actions like starting analysis or applying proposals, separating it from sibling tools such as devtune_start_prompt_set_analysis.

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

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

The description gives clear context: it is a free read, never starts analysis, settles credits, or applies proposals, implying when to use it for read-only needs. It also mentions requiring visibility.read and that Slack execution is unsupported. However, it does not name specific alternative tools, only implies them, so it lacks explicit when-not 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?

No annotations are provided, so the description carries the full burden. It extensively defines the meaning of output fields (e.g., verified_event_count, unknown_no_address_count), which adds real semantic transparency. However, it omits operational behavior such as read-only nature, required permissions, rate limits, or 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 the core purpose and then defines each count. While dense and lengthy, every sentence defines a field that is otherwise undocumented (no output schema), so the length is justified. Structure is clear despite being a single paragraph.

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 no output schema and no annotations, the description does the heavy lifting by explaining the return record structure and each count's meaning. It is largely complete for interpretation, though it could clarify the overall response format or empty-state behavior.

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 fully documents both parameters (limit and windowDays). The description adds no parameter-level meaning beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description states a specific verb ('Get'), resource ('answer-fetch hourly records'), and scope ('up to 20 latest ... in the selected UTC window'). It also clarifies the unit of return ('one rollup record, not an individual event or a whole-window total'), but does not explicitly differentiate itself from any sibling 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?

There is no explicit guidance on when to use this tool versus alternatives. The description explains what the records contain, but never states conditions, exclusions, or sibling tools to prefer. Usage is only implied by the tool name and scope.

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 of behavioral disclosure. It thoroughly explains fallback to last published rollup, stale data semantics, never-published null totals, GSC date boundary, coverage metadata, and pagination rules. It even details output nuances like measurement.availability and pagination.generation. This is far beyond average, though some phrasing (e.g., 'Retained evidence is partial with isCurrent false and status unmeasured') is cryptic and could be clearer, preventing a perfect score.

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 paragraph with no section breaks, bullets, or front-loading of the most critical information. It repeats concepts (e.g., 'Always report the returned source window' and 'Report that returned finality boundary' are nearly redundant). While every sentence carries information, the lack of structure makes it harder for an agent to parse quickly. It would benefit from segmentation by topic.

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 there is no output schema and no annotations, the description does an impressive job covering edge cases (stale, never-published, partial coverage), pagination continuation, date boundaries, and the meaning of availability indicators. It even warns about GSC storedEvidence not verifying continuous coverage. However, it leaves some concepts vague (e.g., 'per-contributor coverage metadata' and the generation parameter's purpose) and does not define what 'unavailable coverage' precisely implies beyond null totals. Still, for a complex tool it is remarkably 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 coverage is only 40%, so the description must compensate. It clarifies that limit and offset apply only to analytics paginated rows, which is a key semantic the schema does not convey. It also maps the source enum to human-readable names. However, it does not explain the generation parameter at all, and windowDays is left entirely to the schema (which already has a clear description). The description adds some value but leaves notable gaps, justifying a 3.

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

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

The description opens with a specific verb ('Get') and a precise resource ('materialized fixed-window Search Console, legacy GA4, or selected-provider analytics aggregate totals'). It clearly distinguishes this tool from the many sibling get_* tools by naming the exact data domain and its scoped window. Even without reading the schema, an agent knows what data this returns and for which sources.

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 substantial context about when the tool is applicable: it handles stale or missing freshness pointers, never-published projects, and covers all three sources. However, it never explicitly states 'use this when you need aggregate totals' versus alternatives, and it doesn't name any sibling to rule out. The guidance is implicit in the resource description and edge-case behavior, not an explicit when-to-use directive.

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

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose some useful operational traits, like that agent picks facts ignore topic filters and that external facts have no platform keys, but it omits basic behaviors such as pagination, ordering, or authorization requirements. Much of the text is spent on output payload shapes that duplicate what the description never proves is an output schema, since none is declared.

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 an enormous wall of schema details with no front-loaded summary beyond one clause. Sentences run to 100+ words, and the payload field lists bury the single most actionable constraint (windowDays is not even mentioned).

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, so some return-shape explanation is warranted, but the description goes far beyond what is needed and still omits essential call behaviors like pagination and ordering. It is complete about payload fields but not complete about how to use the tool correctly.

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

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

Schema description coverage is 100%, so the schema already fully documents the single windowDays parameter and its 30/90 enum. The description adds no additional parameter guidance, which matches the baseline 3 when the schema does the work.

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 opening sentence says it retrieves project timeline events as context for Visibility trends, which is a specific verb+resource. However, the vast majority of the description dives into exhaustive payload schemas rather than clarifying the tool's scope, and it does not differentiate itself from sibling timeline-adjacent tools like devtune_get_action_events or devtune_get_dex_events.

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 frames usage loosely as 'context for interpreting Visibility trends' but gives no explicit when-to-use, when-not-to-use, or alternative tool routing. An agent looking at the ~80 siblings has no guidance on why it would pick this over get_action_events or get_visibility_summary.

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 discloses cursor-based pagination, ordering by citation count, and crucially explains the coverage state (unavailable window not rolled up, empty list not a measured zero). This is valuable interpretive context beyond the basic read operation. It does not detail the exact response fields, but the provided semantics are sufficient for safe use.

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 purpose is front-loaded, and the coverage-state caveat is placed at the end as an important interpretation note. Every sentence earns its place and the structure is clear and efficient.

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 should fully explain the response shape. It mentions cursor-paginated domain rows and a coverage state, but does not specify the fields within each row or the exact structure of the coverage state. This leaves some ambiguity about how to consume the output. Given the tool's moderate complexity (5 optional params), a bit more detail would improve completeness.

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 description adds minor value by giving an example of sourceVertical ('community' covers specific platforms), but this is already implied in the schema's enum description. No new meaning is added for cursor, pageSize, or windowDays beyond what the schema already states.

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

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

States a specific verb and resource: 'Get top citation domains in AI search results.' It clarifies that it returns domain rows, not pages, distinguishing it from sibling devtune_get_top_citation_pages. The ordering by citation count is also explicit, making the purpose unambiguous.

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 context on how to use parameters (e.g., pass sourceVertical to scope) but does not explicitly state when to use this tool versus alternatives like devtune_get_top_citation_pages or devtune_get_citation_analysis. No exclusions or alternative routing are mentioned, so the agent must infer usage from the name and description.

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

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

With no annotations, the description carries full responsibility. It discloses cursor-paginated rows, ordering by citation count, and the important nuance that an empty list may reflect an unavailable window rather than a measured zero. This goes beyond a basic description and prevents a common misunderstanding.

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, each earning its place: purpose, pagination/ordering, sourceVertical usage, and coverage state. No fluff, front-loaded with the core 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?

For a read-only list tool with 5 optional parameters and no output schema, it explains pagination, filtering, window selection, and a key semantic nuance (coverage state). It doesn't describe the exact structure of returned page rows, but for a cursor-paginated list that is often sufficient; the main purpose and behavior are well covered.

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

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

The schema already describes all parameters (100% coverage), so the baseline is 3. The description adds meaningful semantics for sourceVertical (explains it scopes to a vertical with an example) and clarifies the cursor-pagination model. It doesn't add much for other parameters, but the added context justifies a 4.

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

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

The description states a specific verb and resource: 'Get top citation pages in AI search results.' It clearly distinguishes this from sibling tools like devtune_get_top_citation_domains by specifying 'pages', and further describes pagination and ordering criteria.

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 specific usage guidance for sourceVertical with an example ('for example the community platforms AI cites') and explains the coverage state to prevent misinterpretation of empty results. However, it does not explicitly mention alternative tools or conditions for when not to use it, which is a minor gap.

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

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

No annotations are provided, so the description carries the full burden, and it discharges most of it: it defines the row semantics and precisely unpacks five event counters, including the arithmetic relationship (totalEvents minus the five counts = before-verification remainder). It does not cover permissions, rate limits, or ordering, but for a read-only analytics tool the metric semantics are the behavior that matters and they are disclosed well.

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 purpose, followed by dense but necessary counter definitions. Every sentence adds distinct meaning and there is no filler. The counter glossary is long but justified given there is no output schema to carry those semantics.

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 is the only source of return semantics, and it does explain the row grouping and every counter plus the derived remainder. It still omits the possible values of platform/traffic type/bot class and any sorting or pagination behavior, which a fully complete definition would include.

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 parameters (domain, windowDays) are already documented with their enum and default in the schema. The description's phrase 'over a fixed rolling window' restates windowDays without adding enumeration values, format, or edge cases. Baseline 3 is appropriate.

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

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

States a specific verb (Get) and resource (LLM traffic) plus the grouping dimensions (AI platform, traffic type, bot class) and the fixed rolling window. An agent can tell it produces grouped bot-classified rows. However it never distinguishes itself from closely-named siblings like devtune_get_traffic_summary or devtune_get_ai_referrals, so routing is left 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?

There is an implicit hint about why grouping matters (training crawls and answer fetches as separate rows), but no explicit when-to-use, when-not-to-use, or which sibling to pick instead. With ~40 sibling analytics tools, the absence of any routing guidance is a real gap.

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 and does real work: it discloses null semantics for edgeSensorSdk ('null means 0.8.0 or earlier', 'null when the reports could not be read'), the 14-day reporting window, and upgrade availability. It still omits read-only confirmation, auth/permission needs, and rate limits, so it is strong but not complete.

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 core purpose is front-loaded in sentence one, and the two follow-on sentences are dense but each earns its place by pinning down scope and null behavior. It is long and jargon-heavy, but with no output schema this detail is load-bearing rather than 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?

For a complex multi-sensor summary with no output schema and no annotations, the description supplies the key return semantics (traffic totals, bot/referral subsets, per-sensor Active state, edgeSensorSdk version semantics). It stops short of enumerating the full response shape, but covers enough to call 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 takes zero parameters, so there is nothing for the schema to document and the baseline is 4. The one wrinkle is that the description references a 'domain filters the traffic totals' condition even though no domain parameter exists, which could momentarily mislead an agent into hunting for a filter argument.

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

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

States a specific verb ('Get') and resource ('website traffic' totals/subsets plus sensor state), and enumerates exactly what is included (LLM bot, AI-referral subsets, accepted-measurement Active state across six sensors). It is clear on its own, but it never distinguishes itself from overlapping siblings such as devtune_get_ai_referrals or devtune_get_traffic_platforms, so an agent cannot fully rule those out.

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?

Usage is implied by the enumerated scope (a current, project-level traffic overview), and the caveat that sensor state is project-wide even when a domain filters traffic helps interpretation. However, there is no explicit when-to-use/when-not, and no named alternative sibling to route to.

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 behavioral burden and does well: it discloses that a null return means the execution is outside the project, that retained=false means no nonempty public answer or ad evidence was captured, and that nulls do not prove no ads were shown. It stops short of stating auth requirements, rate limits, or pagination 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?

Front-loaded with the core action, then dense but purposeful edge-case semantics. Every clause (null scope, retained=false, nulls-not-proof-of-no-ads) carries information, though the middle section reads as a run-on sequence of caveats.

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?

No output schema and no annotations, so the description must stand alone; it covers the action, the input source, the return shape (sponsored ads labelled separately from organic answer), and the important null/retained semantics. Only the exact response fields and permission requirements are left implicit.

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 0% for the single executionId parameter, so the description must compensate. It adds real value by stating the provenance of the id (from the response history or recent responses list), which the schema's bare uuid format does not convey.

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

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

States a specific verb and resource ('Read one AI execution') and clarifies the scope ('using an executionId from the response history or recent responses list'). It also names the sibling it is not ('Use visibility summary for rollup-backed advertising rates'), so an agent can distinguish it from the rollup and list tools without opening any 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?

Tells the agent where the required input comes from (response history / recent responses list) and routes rollup use cases to a named alternative (visibility summary). Both the selection condition and the alternative are explicit, leaving little to inference.

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

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

With no annotations, the description carries the disclosure burden and does well: it explains excluded onboarding platforms, dataStatus=no_data semantics, null rules for citationPresence/shareOfVoice, and the 0–1 vs 0–100 ranges. It does not state read-only/auth behavior, but for a summary read tool it supplies substantial context about what is excluded and how to interpret edge cases.

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 purpose is front-loaded, but the remainder is a dense, run-on set of caveats that would be easier to scan as a short list. Many sentences earn their place, but the lack of structure and high density hurt readability.

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?

Because there is no output schema and no annotations, the description must carry return-value semantics, and it largely does: it covers platform reporting scope, data-status meanings, organic percentage ranges, citation-presence nulls, and advertising metric separation. Some details remain implicit, but it is substantially complete for a complex read tool.

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