ChiefLab

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

With no annotations, the description carries full burden. It explains the effect (enables executor to fire) but omits details like idempotency, error handling (e.g., invalid actionId), or required permissions. Adds some context but not comprehensive.

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

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

Description is a single, focused paragraph. It front-loads the purpose, then gives usage guidelines and parameter details. Every sentence contributes essential 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?

Given no output schema, the description covers key usage steps (get actionId, call tool) and the approval flow. It lacks return value details but is otherwise adequate for a simple approval tool.

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

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

Schema coverage is 100%, baseline 3. The description adds value by explaining how to obtain actionId from launch_response[].id and noting that id is a legacy alias, providing practical context beyond schema descriptions.

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 approves a ChiefMO publish/send action, naming the specific executors (publish_approved_post, send_approved_email). It distinguishes from other approval tools like approve_run by specifying the context of publish/send actions.

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 when-to-use scenarios: 'approve <channel>', 'approve all', 'ship it', 'go ahead', etc. It also instructs to use this IDE-native path instead of pushing user to reviewUrl, offering clear guidance on when not to use 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?

Discloses that it recovers all open business loops with literal user commands, and has the same handler and response shape as chieflab_status. No annotations provided, but the description adequately covers behavioral traits.

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

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

Three sentences, no fluff. Front-loaded with alias and primary usage. Every sentence adds value.

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 simplicity as a session-entry alias, the description explains its purpose, usage context, behavior, and relationship to sibling status tool. No output schema, but same shape as chieflab_status is noted. Sufficiently 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 100%, with one optional 'workspaceId' parameter described. The description does not add additional meaning beyond the schema; baseline score of 3 is appropriate.

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

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

The description clearly states it is an alias of chieflab_status, specifies its role as the first tool on session start, and explains it recovers open business loops. It distinguishes from the sibling chieflab_status by positioning as a session-entry pattern.

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

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

Explicitly states 'Use as the FIRST tool when an agent session starts on a workspace that already has activity', providing clear context. Does not elaborate on when not to use alternatives, but 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?

Describes the output content (launch count, channels, brand voice, rejections), implying a read-only operation. No annotations are provided, but the description covers behavioral traits well, though could explicitly mention read-only.

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?

Concise 3-sentence structure, front-loaded with usage triggers, efficient description of output, and pairing advice.

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, but description details output content. Parameters partially covered. Overall sufficient for understanding tool purpose and 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 has 2 params with 50% coverage (only tenantId described). The description does not add detail beyond the schema beyond implying workspaceId via 'per-workspace brain'.

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

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

Description explicitly states usage triggers ('USE WHEN'), specifies it returns a plain-English paragraph summarizing brain data, and distinguishes from sibling tool chieflab_brain_read.

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

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

Provides explicit when-to-use with example user queries and recommends pairing with chieflab_brain_read for raw data, offering clear alternatives.

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

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

With no annotations provided, the description carries full burden. It transparently describes the query conditions, return fields (actionId, runId, etc.), and notes the tool's relationship with cron-triggered post-launch review. Lacks mention of permissions or rate limits but is otherwise thorough.

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 purpose. The second sentence is dense but contains necessary details. Not overly verbose.

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, the description lists return fields. It explains triggers, filters, and relationship with sibling tool. Adequately complete 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?

Schema coverage is 100% with descriptions for both parameters. The description adds no additional semantic value beyond the schema, meeting baseline expectations.

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

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

Description clearly states it is a 'measurement queue inspector' with specific conditions (due <= now, measuredAt unset). It distinguishes from sibling chieflab_post_launch_review by noting this surfaces the same queue to a foreground agent rather than being cron-triggered.

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

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

Explicitly states 'USE WHEN the agent wants to know which executed actions are ready for their 24h readback'. Mentions pairing with another tool, providing clear context, though it does not explicitly list when not to use.

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

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

No annotations are provided, so the description carries full burden. It states returns status values (pending/connected/failed) but doesn't elaborate on auth needs, rate limits, or that it's a read-only operation. Adequate but could be more explicit.

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?

Single sentence with context, example queries, and key return values. No filler, perfectly front-loaded, 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?

Given the tool's simplicity (2 params, no output schema), the description gives enough context for its purpose. Could mention workspaceId's role, but overall it's clear for a status-check 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 has two parameters (workspaceId optional, connectionId required) but description only mentions connectionId. With 0% schema description coverage, this omission leaves workspaceId unexplained, reducing agent understanding.

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 checks connector status after OAuth, with specific user queries. It distinctly differs from sibling tools like chieflab_connect_connector by focusing on status confirmation.

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 specifies when to use ('just completed a connector OAuth flow') and gives example user questions. Could mention when not to use or alternatives, but provides strong 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?

Although no annotations exist, the description reveals that OAuth providers return an authorizeUrl and API-key providers return instructions for a set-key tool. It also mentions that without it, other tools return errors. However, it does not discuss whether it overwrites existing connections 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?

Three sentences, no fluff. Each sentence serves a purpose: what it does, when to use, and expected behavior per provider type.

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 simplicity (one parameter, no output schema), the description covers purpose, usage, behavioral differences, and prerequisites. It could mention idempotency or potential side effects, but overall it's 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?

With only one parameter and 100% schema description coverage, the description adds value by listing example providers and explaining behavioral differences (OAuth vs. API-key), which goes beyond the schema's basic enum.

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 connects a third-party provider to the workspace, lists specific providers (Zernio, Resend, GA4, etc.), and distinguishes it from siblings that handle reading, publishing, or other actions.

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

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

It explicitly says 'USE WHEN the user wants to wire up publishing, email sending, or analytics readback,' and differentiates between OAuth and API-key providers, including the expected return values. It also warns of consequences if not used before other 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 provided, so the description carries full burden. It explains why the tool exists (stateless agents, state managed by ChiefLab) and what it remembers, offering rich behavioral context beyond a simple statement.

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, informative, but slightly verbose with alias info. Still efficient and well-structured, front-loading the key 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?

Given the complexity of a stateful continuation tool, the description covers purpose, usage, and behavioral context. No output schema, but it lists possible actions, partially compensating. Lacks explicit return value info.

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 baseline is 3. The description mentions runId in context but does not add significant meaning beyond the schema. No additional parameter 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 states the tool resumes a ChiefLab launch loop from a runId, specifying the action and resource. It distinguishes itself from siblings by explaining it is a continuation tool after specific preceding calls.

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 the scenario: 'USE WHEN an agent has already called chieflab_get_users_after_build / chiefmo_launch_product.' It also enumerates the possible next actions it handles, providing clear guidance on when to use.

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

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

Describes creation of actionStore entry with status, inline draft, executionMatrix, and reviewer UI flow. With no annotations, this fully covers behavioral expectations 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?

Single paragraph, zero waste, front-loaded with purpose, then usage, then details. Every sentence is meaningful and well-structured.

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

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

Given no output schema and 4 params (one nested), description explains the workflow, resulting state, and integration into launch loop. Sufficient for agent understanding.

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% with descriptions; description adds context by linking parameters to the workflow (e.g., runId as originating, metrics as same as suggest). Adds value beyond 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?

Description clearly states it turns a Next Move suggestion into an approval-gated draft action, using specific verb and resource. Distinguishes from siblings like chieflab_suggest_next_move and chieflab_approve_action.

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 says 'USE WHEN you've called chieflab_suggest_next_move and the suggestion's kind is not 'wait' or 'noop'', providing specific conditions for use.

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

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

No annotations provided, so description carries full burden. It explains the creation of an open work request and the lifecycle including acceptance via chieflab_accept_offer. Does not detail permissions or side effects beyond the workflow sequence.

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 dense sentences with no fluff. Front-loaded with usage condition and examples. Each sentence adds value: condition, examples, definition, next steps.

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 complexity (7 params, nested objects) and no output schema, the description is fairly complete. It connects to sibling tools and explains the workflow. Missing return value description but acceptable for a creation 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 86% with good parameter descriptions. The tool description adds a useful hint about capabilityNeeded: 'call chieflab_search_capabilities first if unsure'. However, it does not elaborate on other parameters 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?

Description clearly identifies the tool as creating a work request when an operator hits a gap, with concrete examples ('demo video', 'design help'). It distinguishes from sibling tools by referencing the workflow chieflab_accept_offer.

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

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

Explicitly states 'USE WHEN' condition and outlines the workflow including offer submission and acceptance. Provides clear context but lacks explicit when-not-to-use statements or alternatives.

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

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

With no annotations, the description carries full burden. It discloses the sanity gate (re-classifies body), result capture, proof shape writing, and scheduling of 24h followup. It also mentions refusal reason for missing platforms. However, it lacks details on error handling, rate limits, or authentication requirements beyond the implicit 'execute after approve'. Still, it is highly transparent.

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

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

The description is verbose, containing multiple clauses and details. It is front-loaded with 'P71 — closed-loop executor wrapper' but then covers routing, sanity gate, proof shape, and scheduling in a dense paragraph. Could be broken into shorter sentences for 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?

Given the tool's complexity (6 params, no output schema, multiple connector types), the description covers key aspects: the execution flow, special cases (manual_handoff, blocked), and uniform return shape. However, it omits explicit structure of the return object and does not describe all possible error scenarios. Still, it is largely complete.

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

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

Schema coverage is 100%, but the description adds significant context: explains subject override for email, mediaUrls for social posts, platforms as Zernio descriptors with required condition, and recipients for email with stored list fallback. This goes beyond the schema's basic descriptions.

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 as a 'closed-loop executor wrapper' for approved actions with specific connector modes. It specifies the verb (execute), resource (approved action), and scope (oauth_connector or api_key_connector with connectorReady true). It distinguishes from sibling tools like chieflab_publish_approved_post and chieflab_send_approved_email by explaining internal routing.

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 when to use: after chiefmo_approve_action succeeds for actions with executionMatrix.mode oauth_connector or api_key_connector and connectorReady true. It also provides clear alternatives: for manual_handoff actions, call chieflab_record_manual_publish; for blocked actions, returns connector-wiring instruction. This gives precise usage 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?

In absence of annotations, the description discloses key behaviors: it forces a date into the past (destructive), requires admin token, refuses if already measured, and defaults to now-60s. It does not fully detail side effects like whether other system state changes, but covers essential traits for safe usage.

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 moderately sized and front-loaded with the core purpose. Each sentence adds value: definition, use cases, auth, refusal, and parameter hint. It could be slightly more concise, but overall well-structured and no fluff.

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, the description covers usage context, auth, and refusal condition. It explains when to use and default behavior. It could mention the response format, but the tool is simple and the information provided is sufficient for an admin to use it correctly.

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

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

Schema coverage is 100%, with already detailed descriptions for all parameters. The tool description adds minimal extra meaning beyond restating the dueAt default and origin of proofId. It does not enhance understanding beyond the schema.

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

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

The description clearly states the tool's purpose: 'Forces a proof_ledger row's measurement_due_at into the past so the cron's next tick picks it up.' It identifies itself as an admin escape hatch, differentiating from siblings like chieflab_check_measurement_due which likely only checks status.

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

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

Explicitly states when to use: 'smoke testing... chain without waiting 24h' or 'retry a stuck measurement.' Also notes refusal condition ('Refuses if the row is already measured') and authentication requirement ('Gated by CHIEFLAB_ADMIN_TOKEN header'), providing clear guidance on tool invocation.

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

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

Discloses that the tool returns a machine-readable profile including inputs, outputs, pricing, approval-required flag, and endpoint. While it implies read-only behavior (get), it does not explicitly state safety or error conditions. Given no annotations, this is reasonably transparent.

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

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

Two sentences, no redundant information. First sentence delivers the primary purpose and output, second sentence adds contextual usage guidance. Highly 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?

Covers the basic functionality and output components. Lacks details on return structure, potential errors, or authentication requirements. Given the simplicity of the tool (2-string input, no output schema), the description is mostly complete for 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?

The description mentions 'operatorId' and 'capability' as the required inputs but does not clarify their format, source, or constraints. With 0% schema coverage, the description adds only naming context, not deeper 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?

Description clearly states the tool retrieves a full machine-readable profile for a specific operatorId and capability, listing specific output fields (inputs, outputs, pricing, etc.). It distinguishes itself from the sibling chieflab_create_work_request by positioning as a pre-flight check.

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

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

Explicitly states the condition for use: having a specific operatorId and capability. It also provides context as a pre-flight check before chieflab_create_work_request. However, it does not mention when not to use it or alternative tools for other scenarios.

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 the burden. It declares the tool as a read operation ('read'), indicating no side effects, and lists the returned fields. However, it does not disclose any other behavioral traits (e.g., error scenarios, caching, or permission requirements). For a simple read tool, this is minimally sufficient.

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 long. The first sentence states the purpose and return shape. The second provides usage guidance. Every word adds value, and there is no redundancy or irrelevant information. It is front-loaded with the most critical information.

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

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

Given the tool's simplicity (single optional parameter, no output schema, read-only nature), the description covers the essential aspects: purpose, return structure, and usage context. It does not mention error handling or authentication, but for a straightforward profile retrieval, this is adequate. The sibling set includes a write counterpart, which helps in context.

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

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

The input schema has only one optional parameter ('workspaceId') with a description ('Optional workspace id.'). The tool description adds context by referring to 'the workspace's stored company profile', which indirectly clarifies the parameter's role, but does not add new semantic detail beyond what the schema provides. Since schema coverage is 100%, the baseline is 3, and the description aligns with that.

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'), the resource ('workspace's stored company profile'), and specifies the return structure ('Returns { profile, source, confidence, updated_at }'). It also provides a usage hint ('Use before launch to confirm what ChiefLab will ground in'), which differentiates it from sibling tools like 'chieflab_set_company_profile' (a write counterpart).

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 context for when to use the tool ('Use before launch to confirm what ChiefLab will ground in'). While it does not explicitly list when not to use it or compare with alternatives like 'chieflab_brain_summary', the guidance is useful and implies a specific use case.

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

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

Description states it is a read operation (P79) and returns specific data, but with no annotations provided, it lacks details on permissions, idempotency, or potential failure modes. More behavioral context would strengthen transparency.

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

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

Description is concise with one substantive sentence and a contextual example. Minor deduction for cryptic 'P79' prefix which may not be meaningful to an AI agent.

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

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

For a simple read tool with one optional parameter and no output schema, the description explains return format and provides a concrete use case. Lacks error handling or format details for returned fields, but is generally 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 has 100% description coverage for the single optional parameter. The tool description adds no new meaning beyond the schema's 'Optional workspace id.' Baseline score of 3 is appropriate.

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

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

Tool name and description clearly state 'read the workspace's stored publishing target defaults' with a specific verb and resource. The return format is specified. Description contrasts with sibling chieflab_set_publishing_targets by mentioning setting action, making differentiation explicit.

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?

Description explicitly advises use 'before launch' to check connectivity and proactively surface instructions. It names the alternative sibling for setting, providing clear when-to-use and when-not-to-use 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 provided, so description carries full burden. It details what the tool accomplishes: turning the product into a loop with launchPack, signed reviewUrl, approval-gated actions, 24h measurement, memory, next move, moneyLoop, and agentDependency. Also recommends prerequisite 'Gather repoContext first.' Lacks explicit mention of side effects like creation or mutation, but overall transparent.

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

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

Description is fairly long but front-loaded with purpose and usage. Each sentence adds value given the tool complexity. Could be slightly more concise but structure is logical and complete.

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 9 parameters, nested objects, and no output schema, the description covers prerequisites, usage triggers, outcomes, and guidance. It does not explain return values explicitly but mentions outputs like launchPack, reviewUrl, etc. Overall sufficient for the 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 coverage is 100%, and description adds significant meaning: defaults for channels, recommended nature of repoContext, clarification of outputMode values ('context | draft | full'), and relationship between productUrl and productDescription. Provides context beyond 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?

Explicitly states it is an outcome-named alias for chiefmo_launch_product, lists specific user queries that trigger it, and distinguishes from sibling tools like chiefmo_diagnose_marketing. The verb 'turn the product into' plus resource 'repo-grounded money/distribution loop' provides a specific 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?

Provides explicit when-to-use scenarios: 'USE WHEN a coding agent just built, shipped, or deployed something and the user asks...' and includes a list of example user queries. Also gives routing guidance: 'Route exactly like chiefmo_launch_product; do not use chiefmo_diagnose_marketing for a new product.'

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?

Since no annotations are provided, the description carries full burden. It states 'cheap, no side effects' and describes the return content. Lacks details on auth or error handling, but adequate for a simple, safe 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?

Three sentences, front-loaded with key purpose, 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, no-parameter tool without output schema, the description covers purpose, usage order, return elements, and alternatives, making it 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?

Input schema has zero parameters, baseline is 4. No additional parameter information needed.

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

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

The description clearly states it is the first tool to call, returns operator menu, workspace state, and next action, and distinguishes itself from sibling tools like chieflab_launch_product and chieflab_post.

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

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

It explicitly tells the agent to call this before anything else when unsure, and provides specific follow-up recommendations (launch or post).

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 provided. Description states it returns engagement events scoped to the workspace, ordered by recency, filterable by status. It does not mention read-only nature, pagination, or authentication requirements, but covers basic behavioral aspects.

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

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

Three sentences, front-loaded with examples, then purpose, then pairing instruction. No unnecessary 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?

Given 4 parameters, no output schema, no annotations, the description covers main purpose and filtering. It pairs with draft_reply for handling. Lacks return format details but is fairly complete for an inbox query tool.

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

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

Schema description coverage is 75% (3 of 4 params have descriptions). The description adds no extra meaning beyond the schema; workspaceId has no description but context implies workspace scoping. 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 uses clear examples ('what came in from my launches?', 'show me my replies') and specifies it returns engagement events for THIS workspace, ordered by recency. It distinguishes from sibling tools like chieflab_draft_reply.

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 starts with 'USE WHEN' and lists explicit user queries. It pairs with chieflab_draft_reply for handling, but does not explicitly mention when not to use this tool or alternatives among 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 bears full responsibility and does well by detailing internal composition of chieflab-post and chieflab-email, approval-gated publishActions, signed reviewUrl, and 24h measurement queue.

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 packing much information, but it lacks structure like bullet points or clear separation. It is moderately concise but could be better organized for 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?

Despite describing the high-level orchestration process, the tool has 10 parameters, nested objects, and no output schema. The description does not cover parameter usage or return values, leaving the agent underinformed for a complex tool.

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

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

The description provides no explanation of any of the 10 input parameters. Given 0% schema coverage, the agent receives no guidance on parameter meaning or usage, leaving a significant 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 description clearly states it is an orchestrator for an end-to-end launch loop, listing specific channels and steps. It distinguishes itself from the sibling tool chiefmo_launch_product by noting it is a spec-renamed alias, providing unique identity.

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 identifies it as the 'primary' operator and a reference implementation, giving implicit context for usage. However, it lacks explicit when-to-use or when-not-to-use guidelines compared to other 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?

No annotations provided, but description adequately describes behavior as listing specialists with kind and label. No mention of side effects or auth, but for a read-only list tool this is sufficient.

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

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

Two concise sentences that front-load the purpose and usage. No unnecessary information.

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

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

Given no parameters and no output schema, the description fully covers what the tool does, what it returns, and when to use it. No gaps.

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

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

Input schema is empty (0 parameters), so schema coverage is 100%. Baseline is 4. Description adds value by stating it returns kind + label, which helps caller pick the right specialist.

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

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

Description clearly states it lists specialist agents, enumerates categories (design, video, research, outreach, seo, analytics), and differentiates from sibling tool chieflab_request_specialist by indicating its use before calling 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?

Explicitly states when to use: when user asks what ChiefLab can do beyond launch posts, or before calling chieflab_request_specialist. Provides clear context and 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 provided, but the description discloses key behavioral traits: it flips the action to executed with metadata.markedManuallyAt and executedManually=true, and the next-move card disappears. Additional details like permissions or reversibility could be included, but overall sufficient.

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, well-structured, front-loaded with purpose and examples. Every sentence provides necessary context 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?

Given no output schema, the description does not mention return values or error conditions, but the effect is clear from the context. For a simple action-marking tool, completeness is high.

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% (all 3 parameters described). The description adds value for the 'note' parameter by explaining it's stored in metadata.markedManuallyNote for later use, which goes beyond the schema. For actionId and workspaceId, no further info needed.

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 marks next-move actions as completed when there is no URL, with specific examples like reply_to_commenters and dm_engagers. It also distinguishes from the sibling tool chieflab_record_manual_publish, providing clear differentiation.

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

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

Explicitly specifies when to use this tool (no URL to record) and when not to (use chieflab_record_manual_publish for URL-based moves), providing direct comparison with an alternative 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?

With no annotations, the description fully discloses behavior: it calls the Firebase API, writes to metadata.proof, auto-creates next-move actions based on outcome, is idempotent, and requires no API key. No contradictions.

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

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

The description is a single paragraph that front-loads the main action and then logically expands on usage and outcomes. It is slightly verbose but every sentence adds value; could be tightened.

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, the description explains the process and outcomes (writes to metadata.proof, auto-creates next-move actions). It lacks explicit return value description, but the tool's purpose is 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 coverage is 100% (all three parameters described). The description adds context beyond the schema, e.g., explaining that actionId is used to parse the item id from publishedUrl, which aids understanding.

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

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

The description clearly states it pulls HN post score, comments, and top replies via the public HN API and writes to metadata.proof. It specifies the action type (published HN action) and process. The tool is distinct from its siblings, which focus on actions like approval, boot, or publishing.

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

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

Explicitly states when to use: 'USE WHEN a published Hacker News action is ripe for 24h measurement' and explains the outcome-driven creation of next-move actions. Though it lacks explicit when-not-to-use instructions, the context is clear enough for an AI agent to decide.

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 bears full burden and discloses key behaviors: uses public API (no auth), auto-creates actions based on outcome, idempotent, and force parameter to re-measure. It lacks details on rate limits or error handling but is transparent enough for an 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?

A single paragraph with three sentences is concise and well-structured. It front-loads the main purpose, then adds conditional behavior, and includes a note on idempotency. 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?

Given no output schema, the description explains what is written (score, comments, etc.) and side effects (auto-creating actions). It covers idempotency and force. Missing exact structure of metadata.proof but sufficient for agent use.

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%, but the description adds value by explaining that actionId's publishedUrl is parsed for subreddit and post id, and that force allows re-measurement. This goes beyond the schema descriptions.

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 pulls a Reddit post's score, comments, upvote ratio, and top replies and writes them to metadata.proof. It explicitly starts with 'P115 — pull' and mentions the use case 'USE WHEN a published Reddit action is ripe for 24h measurement,' distinguishing it from siblings like chieflab_measure_hacker_news.

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

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

Provides explicit usage context ('USE WHEN a published Reddit action is ripe for 24h measurement') and describes behavior like idempotency and auto-creating next-move actions. However, it does not mention when not to use or compare with alternatives like chieflab_measure_hacker_news.

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 provided, but description fully discloses behavior: what tables are probed, fallback to in-memory, return value semantics (e.g., 'tableExists: false' means migration not run). No contradictions.

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

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

Well-structured with purpose first, usage guidelines in bold, and return format explained. Slightly verbose but each part adds value.

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

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

Covers tool's purpose, usage, and return format well given no output schema. Missing parameter explanation reduces completeness slightly.

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?

Description does not mention the single parameter 'workspaceId' at all. With 0% schema coverage, description should explain how this input affects the health check, but it is omitted.

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

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

Description clearly states it 'verifies the persistent stores' and specifies the tables probed (proof_ledger, publishing_targets, company_profiles). It distinguishes from sibling tools by its specific health-check focus.

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 lists use cases: after SQL migrations, before founder launch, debugging disappearance. No when-not-to-use or alternatives provided, but the context is clear.

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

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

No annotations are provided, so the description carries the full burden. It states the tool pulls engagement data and recommends next iteration, implying a read-only analytical operation. However, it does not disclose whether it modifies state, requires permissions, or has rate limits. The description adds context about closing the loop but lacks transparency on side effects or safety considerations beyond the implied read nature.

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 informative but slightly verbose. It front-loads the usage condition and then lists outputs efficiently. Every sentence adds value, though it could be trimmed slightly without losing clarity. Overall well-structured.

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

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

Given the complexity of post-launch analytics and the absence of an output schema, the description covers the inputs, outputs (per-account analytics, top posts, best-time-to-post, recommendation brief), and usage timing. It does not explain the recommendation generation or how the brief is structured, but it provides sufficient context for an agent to understand the tool's purpose and scope.

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?

All 4 parameters are described in the schema (100% coverage), so baseline is 3. The description adds context for runId (linked to launch) but does not significantly enhance understanding beyond the schema's own descriptions. No additional parameter semantics are provided.

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 explicitly states the tool is for post-launch review, specifies when to use it (≥24h after publish, user asking about launch performance), lists the metrics pulled (likes, comments, views, shares, follower delta), and describes the output (per-account analytics, top posts, best-time-to-post, next-move recommendation). It also notes it's an alias of chiefmo_post_launch_review, distinguishing it from other 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?

Clear usage condition is provided: 'USE WHEN ≥24h has passed since chiefmo_publish_approved_post fired and the user asks...' It defines the exact scenario and user query triggers, making it explicit when to invoke this tool over alternatives.

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

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

With no annotations, the description fully discloses behavioral traits: drafting a queue grounded in prior run data, output components, and that actions require human approval. No destructive actions mentioned.

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 (5 sentences) with front-loaded usage trigger. It packs necessary information but could be slightly more structured for easier parsing.

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

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

Given no output schema, the description adequately explains returns (draft queue, reviewUrl, agentGuide) with examples of queue contents. All parameters are described, and constraints are clear.

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 baseline 3. Description adds meaning by explaining runId as prior launch, horizon default, and channels defaulting to prior launch channels, providing context beyond schema.

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

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

The description clearly states the tool is for preparing a follow-up queue after a launch, using specific user queries. It distinguishes from general planning by focusing on post-launch actions, though it doesn't explicitly differentiate from sibling 'chiefmo_post_launch_review'.

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

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

Explicit 'USE WHEN' triggers and examples of user queries provide clear context. It explains inputs and the approval process, but lacks explicit when-not-to-use instructions.

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?

Although no annotations are provided, the description thoroughly discloses behavior: it requires approval (strict gate), returns a specific object if unapproved, and returns live URL when successful. Also flags it as 'money/external-system action', warning of consequences.

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 sentences front-loaded with alias info, followed by purpose, usage conditions, and behavior. No superfluous words; every sentence adds value.

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 (5 params, no output schema, no enums), the description covers the essential behavioral details (approval gate, returns, platforms). It also references related tools (chieflab_list_publish_accounts) to aid usage. No gaps for the task.

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% (all params have descriptions). The description adds context beyond schema: mentions that content is 'LLM rendered from brief', platform accounts come from chieflab_list_publish_accounts, and scheduleAt uses ISO 8601. This provides extra usage guidance, justifying a score above baseline 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 explicitly states it 'Publish an approved social post' across multiple platforms, with a specific verb and resource. It distinguishes itself from siblings by tying it to the approval workflow from chiefmo_launch_product, and notes it's an alias for chiefmo_publish_approved_post.

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

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

Provides clear when-to-use ('USE WHEN the user has approved a publishAction...') and when-not ('never call this without an approved actionId'). Also specifies prerequisites (approved actionId) and the fallback behavior for unapproved actions.

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, description fully carries the burden. It discloses return content (proof rows with artifact URLs, channels, execution modes, measurement state) and persistence behavior (persistent if Supabase wired, falls back to in-memory). This provides key behavioral context beyond the schema.

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?

Single focused paragraph with no wasted words. Front-loaded with core action and use case, then efficient details on return content and behavior.

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 moderate complexity (5 parameters, no output schema, no annotations), the description covers purpose, usage, return, and persistence well. Lacks details on error handling or authentication, but those are often implicit. A solid 4.

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 parameter descriptions are already clear. Description adds minor value by clarifying 'measurementDueOnly' usage ('find what's ready for 24h readback'), but overall the schema does the heavy lifting. Baseline of 3 is appropriate.

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

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

Description clearly states verb 'query' and resource 'persistent proof ledger'. Provides specific user-facing questions ('what has ChiefLab actually shipped?', 'show me the launch history') that differentiate it from sibling tools, which are primarily action-oriented (e.g., chieflab_approve_action, chieflab_launch_product).

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

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

Explicitly states 'USE WHEN' with concrete scenarios, giving clear context for invocation. Does not explicitly mention when not to use or name alternative tools, but the use cases are specific enough to guide selection.

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 provided, so description carries full burden. It details that it reads metadata.proof.artifactUrl, writes metrics and measuredAt, auto-classifies outcomes with channel-specific thresholds, and creates next-move actions. While it doesn't mention auth or rate limits, it sufficiently describes the mutation 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?

Single paragraph packed with essential details, front-loaded with purpose. Every sentence adds value, though it could be slightly more structured for readability. Still concise relative to information density.

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, description explains that it returns the metric schema for the channel, enabling callers to render form fields. Covers all key aspects: preconditions, actions, auto-classification, and follow-up. Comprehensive for a manual metrics tool.

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

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

Schema coverage is 100% with descriptions for all parameters. Description adds value by listing channel-specific metric keys (e.g., x, linkedin, email) and explaining the 'force' parameter's re-recording behavior, enhancing beyond schema alone.

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

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

The description clearly identifies the tool as a manual metric fallback for channels without public-API measurement. It explicitly contrasts with automated measurement tools for hacker_news and reddit, distinguishing from sibling tools like chieflab_measure_hacker_news.

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

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

Explicitly states 'USE WHEN' conditions: when a published action's channel lacks a public-JSON endpoint (anything except hacker_news/reddit) and the founder is ready to paste numbers. Also provides guidance on calling without metrics to retrieve schema.

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 disclosing behavioral traits. It describes the three main effects (status update, proof persistence, metrics queuing) but omits details on authorization, idempotency, or rate limits. The description is adequate but not comprehensive.

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

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

The description is well-structured with a leading 'USE WHEN' statement. It is concise but covers necessary details in a few sentences. Could be slightly shorter, but no redundant sentences.

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

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

Given no output schema, the description does not explain return values. It does adequately describe the side effects (status change, proof asset, metrics queue). With 5 parameters and high schema coverage, the description is mostly complete but lacks output documentation.

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%: all 5 parameters have descriptions in the input schema. The description adds context by explaining the purpose of 'actionId' and 'publishedUrl' and notes that 'notes' persist into the P9 brain, but this information largely overlaps with the schema descriptions. Baseline 3 applies since schema already covers parameters well.

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

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

The description clearly identifies the specific action: recording a manual publish URL back to ChiefLab. It details the exact operations (status flip, proof persistence, metrics queuing) and distinguishes from sibling tools like chieflab_use_manual_fallback and other record 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 begins with 'USE WHEN' and explicitly states the prerequisite (manual post to a channel from chieflab_use_manual_fallback). It explains why the tool is necessary ('Without this tool, manually-posted channels are lost'), providing clear context. However, it does not mention when not to use the tool or alternative scenarios.

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 full responsibility for behavioral disclosure. It details that the tool pulls the existing brief, merges the instruction, returns an updated brief, increments revision, and preserves the original in history. It also mentions the post-processing step (call record_rendered_copy). It lacks explicit disclosure of auth needs or rate limits but covers core side effects 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?

The description is dense but efficient, starting with usage triggers and then explaining the flow and side effects. It could be slightly more concise, but every sentence adds value. The structure is front-loaded with important usage signals.

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 presence of an input schema (75% coverage) and no output schema, the description explains the return value as 'an updated brief your LLM should render' but does not specify the format (e.g., JSON structure). The workflow is reasonably complete, but the lack of output format details and the omission of potential error conditions (e.g., invalid actionId) leave room for improvement.

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 75% (3 of 4 params described). The description adds meaning beyond the schema by explaining that 'instruction' is plain-English and 'body' is optional for pre-rendered content. It also links actionId to chiefmo_launch_product, providing context not in the schema. The undocumented workspaceId parameter lacks description but is likely inherited context.

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

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

The description clearly states the tool's purpose: redrafting published briefs when users request changes like 'make this more casual' or 'shorter'. It specifies the verb ('redraft'), resource ('published draft or brief'), and the action of merging instructions. The description distinguishes it from sibling draft tools (e.g., chieflab_draft_outbound) by targeting existing published drafts rather than creating new ones.

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 lists trigger phrases ('USE WHEN the user says...') and provides step-by-step flow, including the requirement to call chieflab_record_rendered_copy after rendering. It does not explicitly state when not to use or name alternatives, but the trigger conditions are clear enough for an agent to decide. The multi-turn flow note adds 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 provided, the description carries the full burden of behavioral disclosure. It explains that the tool reads the original launch's brief, calls produceVisualAsset with new parameters, returns updated asset shape (assetId, dataUrl, dimensions, prompt, mode), and does not auto-persist. This is transparent about inputs, process, and outputs, though it could mention authorization requirements or rate limits.

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 effectively front-loaded with purpose and usage, then details behavior and return shape, and ends with a persistence note. It is thorough but not overly verbose; every sentence contributes useful information. Minor redundancy (e.g., 'regenerate a single visual asset' repeated) prevents a perfect score.

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

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

Given the complexity (12 parameters, nested object, no output schema), the description covers the workflow, input/output details, and behavioral constraints. The return shape is described. While error handling or failure modes are not mentioned, the description is largely sufficient for an agent to invoke the tool correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explaining how parameters interact: e.g., 'model defaults to the brief's fidelity selects', 'assetId allows swap in place', and the general flow of overrides. This contextual information helps the agent understand parameter relationships.

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 starts with a specific verb and resource: 'regenerate a single visual asset', lists common examples (LinkedIn graphic, X graphic, landing hero), and explicitly states the usage scenario: 'USE WHEN a reviewer hits Regenerate on a launch image in the Launch Room.' This clearly differentiates it from sibling tools like chieflab_launch_product or chieflab_redraft.

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 explicit when-to-use guidance ('USE WHEN a reviewer hits Regenerate...') and states what the tool does NOT do ('Does NOT auto-persist back to the run — caller decides whether to swap...'). It does not explicitly mention alternative tools, but the context of the Launch Room and the clear scoping make it unambiguous.

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

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

With no annotations, the description carries full burden. It mentions that specialists are deterministic stubs (transparently swapped later) and that each request creates an approval-gated action. However, it omits details about error handling, permissions, or idempotency.

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 'USE WHEN' and each sentence adds value. It is slightly verbose but efficient for the information 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?

Given 5 parameters, no output schema, and complexity, the description covers main use cases, output format (structured briefs/plans), and action creation. It lacks details on approval process and error handling but is generally 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 100%, so baseline is 3. The description adds little beyond the schema descriptions, though it contextualizes kind-specific briefs and the purpose of runId and autoCreateAction.

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 'delegate to a specialist agent' and enumerates specific use cases (design, video, research, etc.), making the tool's purpose distinct from siblings like list_specialists or create_work_request.

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 'USE WHEN the launch needs more than copy' and lists kinds, providing clear context for when to use. However, it does not explicitly describe when not to use or directly compare to 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 provided, so description carries full burden. It discloses that the tool checks readiness, returns whether ready or still blocked with connect path. It implies a non-destructive check. It does not mention permissions, side effects, or rate limits, but for a simple retry check it is adequate.

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 at 2-3 sentences, front-loaded with the main purpose, and structured logically: action, return, usage condition. Every sentence adds value with no redundancy.

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

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

The description covers return values (ready or still blocked with path) and suggests next steps. With no output schema, this is helpful. However, it lacks mention of prerequisites (e.g., action must exist) or error cases. Given the tool's simplicity, it is fairly complete.

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

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

The description adds no parameter information beyond the schema. The schema describes `actionId` but not `workspaceId`. With 50% schema description coverage, the description should compensate for the missing `workspaceId` description but fails to do so. No additional meaning or format details are provided.

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

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

The description clearly states the tool's purpose: 're-check a blocked publishAction's connector readiness.' It specifies the resource (blocked publishAction) and verb (re-check). It distinguishes from siblings by being specific to retrying a blocked action after a provider connection, not just general connector status.

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 'USE WHEN the user has just connected a missing provider and the agent needs to know if the action can fire.' This provides clear context for usage. It also suggests follow-up actions (re-fire specific tools). However, it does not explicitly state when not to use or list alternatives.

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

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

With no annotations provided, the description fully carries the behavioral burden. It explains the read-modify-write flow: reads the stored visualAssets list, updates the matching asset's status, persists to run metadata, and affects future loads and channel-media render. It also distinguishes from the companion tool's behavior (no persistence).

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, followed by the use case, then technical details. Every sentence adds value without repetition or fluff.

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

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

The tool has 5 parameters and no output schema. The description covers purpose, trigger, behavior, and relationship to a sibling tool. It could mention error handling or what happens if the asset ID is not found, but for the given complexity, it is largely complete.

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

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

Schema coverage is 100% and each parameter has a clear description. The description adds additional context: for 'reason' it says 'Stored in metadata for audit + future brain memory', and for 'decision' it clarifies 'Updates the asset's status field.' This goes beyond the schema alone.

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 starts with a clear verb+resource: 'approve or reject a single visual asset', and specifies concrete asset types (LinkedIn graphic, X image, etc.). It explicitly differentiates from the sibling tool chieflab_regenerate_visual_asset, which produces an updated asset shape without persisting.

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 the trigger: 'USE WHEN a reviewer hits Approve or Reject on a launch image in the Launch Room.' It mentions the companion tool for regeneration but does not explicitly list cases when not to use this tool, though the context is clear enough.

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

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

No annotations are provided, so the description must disclose behavioral traits. It states the tool returns active records ranked by recency, implying a safe read operation. The metaphor 'discovery embryo' reinforces its non-destructive nature. However, it could be more explicit about being read-only and what 'active' means, but overall sufficient.

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: three substantive sentences plus a tagline. The first sentence front-loads the usage case, the second explains output, and the third provides pairing context. No filler or redundancy. Every sentence earns its place.

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

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

With no output schema, the description does not detail the return fields of a capability record, which would help agents parse results. However, it covers the core functionality, usage scenario, and pairing, making it largely complete for a simple search tool. The missing field detail prevents a perfect score.

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 does not add meaning beyond the schema's parameter descriptions (tags, limit, capability). It only provides usage context, not parameter details. Therefore, no extra value is added.

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

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

The description clearly states the tool's purpose: finding operators with capabilities outside the current operator's scope. It provides concrete examples like 'find me a demo-video capability' and explains the return type (active capability records ranked by recency). This distinguishes it from similar tools like chieflab_match_capabilities.

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 begins with 'USE WHEN' and explicitly tells the agent when to invoke this tool (when an operator needs a capability beyond its scope). It also pairs the tool with chieflab_create_work_request for subsequent work requests, providing a clear workflow. No alternatives are mentioned, but the guidelines are direct and actionable.

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 takes full responsibility for transparency. It discloses that this is a money/external-system action, that emails cannot be unsent once sent, and that 'from' must be on a verified Resend domain. It also notes single recipient or short list (≤50). This provides critical behavioral context beyond what the schema shows.

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

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

The description is concise, using three sentences to cover the alias, purpose, usage condition, domain requirement, recipient limit, and irreversibility. It front-loads the most critical information (alias and purpose) and earns every sentence.

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?

Despite no annotations or output schema, the description provides a complete picture: the tool's role in the approval workflow, domain verification, recipient limits, and the irreversible nature of the action. It addresses all key behavioral and usage aspects for a high-stakes email sending tool.

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

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

Schema description coverage is 86%, so the schema already documents most parameters well. The description adds value by clarifying that 'from' must be on a verified Resend domain, 'to' can be single or array (≤50), and 'actionId' is required for the approval gate. This contextualizes parameters without repeating schema 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 states it's an alias for chiefmo_send_approved_email, used to send an approved launch email via Resend. It specifies the exact use case: when a user has approved an email publishAction from chiefmo_launch_product. This distinguishes it from siblings like chiefmo_send_outbound or chiefmo_publish_approved_post.

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 'USE WHEN the user has approved an email publishAction... and you need to fire the send.' It also references a strict approval gate and warns to check verified Resend domain. While it doesn't list when not to use, the context is clear enough for correct tool selection.

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 burden. It reveals that the profile is persistent, updates are partial, and null removes fields. However, it doesn't explicitly state that nested objects shallow-merge (only implied in schema) or what happens on overwrite. The description is good but could be more explicit about merge 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 front-loaded with the main purpose and efficiently packs background, usage guidelines, and field list. Every sentence adds value, though it could be slightly trimmed without losing 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?

Given the complexity (4 params, nested object, no output schema), the description explains the tool's role in the broader system (P88 feature) and provides sufficient detail on usage and fields. It doesn't describe the return value, but for a set operation that is acceptable.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by listing the profile fields and explaining usage patterns (partial updates, null removal). This goes beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: 'set the workspace's persistent company profile'. It provides context about the P88 feature, distinguishing this set operation from the sibling tool chieflab_get_company_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 explains when to use the tool: 'store the company / product facts ONCE and every subsequent launch grounds in them'. It also gives usage guidance: 'Pass partial maps to update specific fields; pass null for a field to remove it'. While it doesn't explicitly state when not to use it, the context makes it 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. It discloses encryption at rest, workspace scoping, and the BYOK vs billing model. It lacks details on overwrite or error behavior, but for a simple store operation, this is largely sufficient.

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?

Five sentences with no filler. Front-loaded purpose, followed by rationale, provider list, security, and revocation. 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 key storage tool, the description covers purpose, use case, security, revocation, and scoping. Lacks detail on duplicate keys or error handling, but is largely complete given the simplicity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds marginal value by explaining provider usage (e.g., gemini for image gen) and BYOK context, but does not add much beyond the schema descriptions.

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 'Store a provider API key for THIS workspace,' with a specific verb (store) and resource (provider API key). It lists the supported providers and distinguishes itself from specialized setters like chieflab_set_resend_key by covering all providers.

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 explains when to use BYOK (to avoid margin) and mentions the alternative of using ChiefLab's own key. It refers to chieflab_revoke_provider_key for removal. However, it does not explicitly differentiate from specialized setters or provide explicit 'when not to use' scenarios.

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 fully explains persistence behavior (persistent store or in-memory Map) and return value. It doesn't mention authorization or error conditions, but given the tool's role, the provided behavioral details are adequate.

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 paragraph with essential information, but starts with an internal ID 'P79' that is irrelevant to the agent. Otherwise, it is well-structured and front-loaded with the 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?

The tool is moderately complex with nested parameters and no output schema. The description covers the key aspects: purpose, usage, parameter format, persistence, and return value. Missing details like error handling, but overall sufficient for an agent to understand and use it correctly.

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

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

Schema coverage is 100%, baseline 3. The description adds clarity by providing examples for channelTargets, explaining partial updates, and the effect of passing null. This adds significant value beyond the schema's brief descriptions.

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 sets per-workspace publishing target defaults, distinguishing it from sibling tools like chieflab_approve_action which uses these defaults. It specifies the resource (publishing targets) and the action (set defaults).

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 this is a one-time setup per workspace and reduces the need to pass IDs on approve_action calls. It doesn't explicitly list when not to use it, but the context and sibling tools imply it's for initial configuration rather than per-use overrides.

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 fully discloses behaviors: creates workspace, returns apiKey, URL expires in 1 hour, single-use, IP-rate-limited. It also describes the agent-first signup flow, ensuring the agent understands the side effects and constraints.

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 paragraph that front-loads the trigger condition, then covers main action, preferred flow, and fallback. It is concise but dense; could be more structured, yet every sentence adds value.

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

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

Given 3 optional parameters, no output schema, and the signup context, the description covers purpose, trigger, flow, return values (apiKey, deliveryUrl), and constraints. It is complete for an agent to select and invoke without gaps.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that agentName logs for support, contactEmail for future notifications, and workspaceLabel defaults to 'default'. This provides context beyond schema descriptions.

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 creates a new workspace and returns an API key, triggered by a 401 error when the user has no API key. This is a specific verb+resource and distinguishes from sibling tools like chieflab_brain_read.

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 says 'USE WHEN' with the condition (no API key and 401 error). Provides a preferred flow (write to MCP config) and a fallback (deliver URL), giving clear guidance on when and how to use.

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

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

No annotations provided, so description covers behavioral traits: flips action to rejected, adds metadata.skipReason, and impacts brain learning. Thorough disclosure of consequences.

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 purpose, each sentence essential. No redundancy or 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?

Covers purpose, effect, and learning mechanism. Lacks return value description, but no output schema exists. Minor gap for error handling or response format.

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 baseline is 3. Description adds context for reason (brain avoids similar moves) but does not significantly extend beyond schema descriptions.

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

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

Description clearly states the tool rejects a next-move recommendation without executing it, capturing a reason for learning. Distinguishes from siblings like approve_action and execute_approved_action.

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?

Describes when to use (founder rejects move) and the effect on the system. Lacks explicit when-not-to-use or direct sibling comparisons, but context is clear.

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

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

Without annotations, the description fully discloses the tool's behavior: it recovers session state even after lost conversations, aggregates multiple business metrics, and is re-summonable.

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 thorough but not excessively long; key points are front-loaded. It could be slightly trimmed but is 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?

Despite no output schema, the description fully explains the return value structure ('bootCard with headline, priority, cards[]... next') and lists all aggregated data sources, making it complete for a complex, stateful 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?

Only one optional parameter exists, and the schema already describes it ('Optional workspace id.'). The description does not add extra meaning beyond the schema, which has full 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 explicitly states the tool's purpose: 'FIRST CALL when a session starts...' and details its output as a command-center bootCard with specific fields, clearly distinguishing it from sibling 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?

It provides explicit when-to-use guidance ('when a session starts and the user mentions launch / users / growth...') and what to do next ('agents should surface them BEFORE drafting a new launch').

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?

Despite no annotations, the description discloses key traits: deterministic pure function, no LLM, same input same output. Lists output structure. Could mention side effects or auth requirements but is already transparent.

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

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

Concise and front-loaded with purpose. Every sentence adds value. Slightly unstructured block but still efficient. Minor improvement: separate usage and output sections.

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?

Explains output structure in detail despite no output schema. Covers relationship with sibling tool and deterministic nature. Could elaborate on error handling or required prerequisites, but sufficient for typical use.

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 baseline is 3. Description does not add extra meaning beyond schema; it briefly mentions reading metrics and brief but no parameter-specific 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 states the tool's purpose: 'suggests next move' after measurement. It uses specific verbs and enumerates the possible suggestion kinds, distinguishing it from sibling tools like chieflab_create_next_move_action.

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

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

Explicitly states when to use: 'USE WHEN measurement just came in' and references the automatic trigger or manual call. Also provides pairing guidance with chieflab_create_next_move_action.

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 provided. Description mentions approval requirement and returns a brief with tracking template, but does not disclose side effects, permission needs, or whether it modifies state.

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

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

Three sentences, front-loaded with usage condition and output. Could be slightly trimmed but is 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?

Given no output schema, description explains return components (text, checklist, URL, measurement template) and integrates with other tools (chiefmo_record_proof). Provides sufficient context for agent use.

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 covers 100% of parameters. Description adds qualitative context (e.g., 'Override the channel', 'ChiefLab pulls the brief, channel hints, and brand voice'). Adds value beyond 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?

Description clearly states the tool is for blocked manual-only channels, returns a paste-ready brief. Differentiates from siblings like chiefmo_publish_approved_post (automated) and chieflab_launch_product (general launch).

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 says 'USE WHEN a launch action is blocked because the channel has no automated publish path' and 'Approval-gated upstream.' Provides clear context but does not explicitly list alternatives or when not to use.

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

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

With no annotations, the description fully compensates by detailing the return object fields (ok, mcpReachable, toolsDiscovered, workspaceReady, hasOpenLoops, blockedConnectors, summary) and explaining how to handle the summary. It also describes error recovery when ok is false, noting a nextStep field. No contradictions with annotations since none are present.

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

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

The description is well-structured, starting with the core purpose and timing, then detailing the return format and usage instructions. It is slightly verbose but every sentence adds necessary information, making it efficient for the agent.

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

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

Given the absence of an output schema and annotations, the description comprehensively covers all relevant aspects: when to invoke, what the response contains, how to present the summary to the user, and error handling. No missing information is apparent.

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 only parameter, workspaceId, is described in the schema as optional with a brief description. The description adds value by stating it is 'inferred from bearer auth,' reducing ambiguity about when to provide it. With 100% schema coverage, this extra context is beneficial.

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

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

The description clearly states it is a 'POST-INSTALL HEALTH CHECK' with a specific action ('Call this exactly once after the agent has wired ChiefLab into the runtime'). The verb 'verify install' and resource 'health check' are unambiguous, differentiating it from other tools like chieflab_boot or chieflab_status.

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

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

Explicit timing is given ('Call this exactly once after the agent has wired ChiefLab into the runtime') with a reference to a bootstrap protocol. It also includes a specific instruction to not list all tools and to render only the summary. However, it does not explicitly mention when not to use it or provide alternatives.

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