ChiefLab

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

No annotations provided, so description carries full burden. It explicitly states the tool is a 'primary read' and lists the data it returns, assuring the agent it is a read-only operation. While no side effects are described, the purpose as a read tool is clear and consistent.

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 efficiently communicates purpose, data returned, alias, and related tools. However, it could be better structured for quick scanning (e.g., bullet points for the data list).

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 partially compensates by listing what is returned, but lacks detail on structure, pagination, or error conditions. The tool returns multiple complex objects, and more guidance would improve completeness.

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

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

The input schema has 2 parameters (tenantId, workspaceId) with 0% description coverage, and the description provides no guidance on their values or format. This forces the agent to infer usage from context, which is insufficient for correct invocation.

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

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

Description clearly identifies the tool as the primary read for the per-tenant company brain, enumerating the data categories returned (brand voice samples, repo facts, proof assets, etc.), and distinguishes it from sibling record tools and the aliased chiefmo_company_brain.

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 states it 'drives cross-launch grounding' suggesting when to use, and mentions record tools separately implying they handle writes. However, it does not explicitly contrast with other read tools like chiefmo_check_claims or provide when-not-to-use conditions.

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

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

With no annotations, the description must disclose behavior fully. It explains the OAuth flow, return of an authorize URL, and the lifecycle (user grants access → connector becomes live → real data replaces mock). It does not explicitly state whether the tool is read-only or destructive, but the described effect (creating a connector) implies mutation. This is adequate but could be clearer.

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 extremely concise—two sentences. The first sentence immediately states the purpose and use case. The second provides a crucial limitation note. No filler or redundant 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 (2 params, no output schema), the description covers the key points: what it does, what it returns, and the effect. The v1 limitation adds important context. However, it does not mention error conditions (e.g., invalid provider) or what happens if OAuth fails. This is acceptable for a straightforward 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%, but the description adds value: it provides example provider values (hubspot, stripe, etc.) and explains that workspaceId defaults to the API key's workspace, information not present in the schema. This helps an agent understand parameter usage 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 connects third-party data sources via OAuth and returns an authorize URL. It lists examples (HubSpot, Stripe, etc.). However, it does not explicitly distinguish itself from sibling tools like chieflab_connector_setup_steps or chieflab_connect_provider, which could create ambiguity for an agent.

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' providing explicit context. It instructs to surface the URL to the user for OAuth grant. The note about v1 limitations ('only chieflab_connect_publish_account is fully wired') informs when not to rely on live data for other connectors. More explicit alternatives or conditions for using siblings could improve this.

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 cover behavioral traits. It describes the output (prerequisites + ordered steps + verification commands) but does not disclose whether authentication is required, whether it is read-only, or any rate limits. It leaves 'JIT' unexplained. With no annotations, a 3 is appropriate – adequate but not 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 with no superfluous words. The first sentence states purpose and providers; the second provides usage context. 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?

For a simple tool with one parameter and no output schema, the description covers purpose, usage trigger, and output content. It lacks explanation of 'JIT' and error handling, but overall it is sufficiently complete for an agent to use 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% for the single parameter 'provider', and the description lists the allowed values in parentheses, echoing the schema. It does not add new meaning beyond what the schema provides. Baseline for high coverage is 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 clearly states 'Get step-by-step JIT setup instructions for a connector' and lists the allowed providers (zernio, resend, ga4, etc.). It specifies the return content (prerequisites, ordered steps, verification commands). This distinguishes it from sibling tools like chieflab_connect_connector, which likely performs the actual connection.

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 tells when to use the tool: 'Use when a launch pack reports a connectorBlocker — pipe these steps to the user.' This is a specific use case. It does not explicitly say when not to use or mention alternatives, but the guidance is clear 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?

No annotations are provided, so the description carries full burden. It discloses the return value format and that it's a read operation for checking status. However, it does not mention error handling, authentication requirements, or what happens if the connectionId is invalid, leaving some behavioral gaps.

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

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

The description is a single sentence with a clear usage prefix and example. No unnecessary words, front-loaded with key 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 output schema and few parameters, the description covers the essential purpose and usage. It lacks details on error cases or edge behaviors, but for a simple status check it is largely sufficient.

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 with 0% description coverage. The description mentions 'for the given connectionId' but does not explain workspaceId at all. Given the low coverage, the description should provide more clarity on workspaceId's role and usage.

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 OAuth status, gives example queries, and lists possible return values. It distinguishes itself from siblings like chieflab_connect_connector and chieflab_connector_setup_steps by focusing on post-OAuth 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?

The description explicitly begins with 'USE WHEN' and provides specific user queries that trigger this tool. It does not mention when not to use or alternatives, but the context is clear enough for an 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?

No annotations provided, so description carries full burden. Discloses that for OAuth providers it returns an authorizeUrl for the user, and for API-key providers it returns instructions for a separate set-key tool. Implies the tool is a prerequisite for other actions. Reasonably transparent, though could mention if it modifies workspace state permanently.

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, starts with the main action, then usage guidance, then behavioral specifics. All sentences earn their place. No fluff. Could be slightly more concise by splitting into bullet points, but appropriate for current format.

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, so description explains the two possible return types. Covers error scenario ('configure first' errors). Lists all provider options. Sufficient for an agent to use this tool correctly. Minor omission: doesn't specify if the tool checks connectivity or just saves configuration.

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 a description for 'provider' parameter. The description adds value by grouping providers into OAuth vs API-key categories and explaining the behavioral difference, which helps the agent choose the right response path. This goes beyond the simple enum list in the schema.

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

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

Clearly states 'Connect a third-party provider to this workspace' and lists specific providers (Zernio, Resend, GA4, etc.). Distinguishes from siblings by describing the overall purpose, though could explicitly contrast with chieflab_connect_connector. The USEWHEN clause helps clarify scope.

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 use-case statement ('USE WHEN the user wants to wire up publishing, email sending, or analytics readback'). Categorizes providers by auth type (OAuth vs API-key) and tells agent what to do with the response. Also warns about prerequisite errors for 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?

With no annotations, the description carries full burden. It discloses that the tool returns a Zernio OAuth URL, requires a profileId (with failure consequences), and defaults redirectUrl. It does not cover idempotency or rate limits, but the core behavioral traits are 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 effectively structured with a clear USE WHEN statement upfront. It is concise while covering purpose, flow, and a critical note. Minor minor redundancy (repeated mention of profileId requirement) but overall well-organized.

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

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

Given no annotations and no output schema, the description is fairly complete: it explains the tool's role in the workflow, required parameters, and a common failure case. It does not describe the exact structure of the returned authUrl (e.g., protocol), but that is implied.

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

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

Schema coverage is 100%, and the description adds meaningful context: it explains the role of each parameter in the OAuth flow, especially the critical profileId and the default redirectUrl. 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 explicitly states the tool's purpose: connecting a social account for posting via chiefmo_publish_approved_post. It lists supported platforms and clearly distinguishes from sibling tools like chieflab_list_publish_accounts by focusing on the connection step.

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 standard agent flow (call this with platform, get authUrl, surface to user, then confirm with list_publish_accounts and pass accountId to publish). It also advises on obtaining profileId, which is critical. This is explicit guidance on when and how to use the tool.

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

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

No annotations are provided, so the description must cover behavioral traits. It states that each tenant gets its own brand context, voice samples, memory, and is scoped under the workspace. However, it does not mention idempotency, error handling, or what happens on duplicate tenantId.

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, each sentence serves a purpose. The first provides the usage signal, the second explains the outcome. 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?

The description explains the purpose and outcome but does not mention the return value (e.g., whether the created tenant object is returned), error conditions, or prerequisite operations. Given no output schema, more details on the result would improve completeness.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds context that tenantId is a 'short slug used in MCP calls' and that domain is for 'auto-context extraction', which adds slight value 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?

Description clearly states the verb 'create' and resource 'tenant', and differentiates from sibling tools like chieflab_list_tenants and chieflab_set_tenant_context by specifying it's for creating a new tenant for isolated brand context.

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 starts with 'USE WHEN' and describes scenarios like managing multiple end-users and setting up a tenant for a new customer. Does not explicitly mention when not to use or alternatives, 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?

Describes the tool as listing items, implying read-only behavior, but no explicit mention of non-destructive nature or output format. With no annotations, description carries burden; it's adequate but minimal.

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

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

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

Lacks details on output format (e.g., fields returned) and does not explain limit or workspaceId parameters. For a list tool, more context is needed for proper invocation.

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

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

Only bucket parameter has schema description (enum values), and the description merely repeats 'Filterable by bucket'. Limit and workspaceId are not explained beyond schema type, adding no value.

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?

Clearly states it lists blocked launches, missing-approval items, connector failures, and due followups for triage. Distinct from sibling tools which focus on other domains.

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 to triage what's stuck' and mentions filterability by bucket, providing clear usage context. No exclusion or alternative guidance needed as no sibling overlaps.

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 only states 'return the exact next tool' without disclosing any behavioral traits like side effects, permissions, or return format. This is insufficient for a tool that likely influences subsequent actions.

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, each carrying essential information. The first sentence states core functionality, the second provides usage context. 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?

No output schema exists, so the description should explain return values. It says 'return the exact next tool' but does not specify the return format (e.g., tool name as string or object). It also omits error handling or edge cases. For a simple tool, it's mostly adequate but missing some details.

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

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

The single parameter 'channel' has a schema description, and the tool description adds clarification ('Channel id from the catalog... or a legacy alias'), which adds value beyond the schema. Schema coverage is 100%, making this a strong addition.

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

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

The description clearly states the tool's purpose: returning the next tool to call for an unblock action, and lists two specific sibling tools (chieflab_connect_provider and chieflab_use_manual_fallback). This distinguishes it well 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?

The description explicitly says 'USE WHEN the user clicks an unblock button or the agent decides which channel to wire next,' providing clear usage context. It does not mention when not to use or alternatives, but the context is sufficient for this decision.

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 details the tool's behavior: it sets up a launchPack with approval-gated actions, measurement, memory, etc. It also mentions prerequisites ('Gather repoContext first'). The description effectively communicates the tool's role as an 'agent-dependency tool' without contradicting any annotations.

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

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

The description is front-loaded with the core purpose and structured logically: alias declaration, use cases, what the tool does, prerequisites, and routing. While somewhat verbose, every sentence adds value and is necessary for agent understanding. The length is justified by the tool's complexity.

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

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

Given the absence of output schema and annotations, the description provides a comprehensive overview including the tool's role, output artifacts (launchPack, reviewUrl), lifecycle details (24h measurement, next move), and explicit usage context. It leaves no ambiguity about when and how to invoke the tool.

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

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

The input schema has 100% description coverage, so the baseline is 3. The description adds minimal additional parameter guidance beyond the schema, except for clarifying the productUrl/productDescription alternative and emphasizing repoContext. The schema itself is already fairly descriptive.

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 'Outcome-named alias for chiefmo_launch_product' and specifies the exact scenarios (after build, when user asks for users/customers/money). It distinguishes itself from siblings by explicitly noting it should not be confused with chiefmo_diagnose_marketing.

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 criteria: 'USE WHEN a coding agent just built, shipped, or deployed something and the user asks...' along with specific user queries. It also gives routing instructions and a clear alternative ('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?

No annotations are provided, so the description carries full burden. It discloses internal composition (post, email under one runId), approval-gating, review URL generation, and 24h measurement queue. However, it does not mention authentication needs, rate limits, or destructive nature beyond implied mutations.

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 single-paragraph but front-loads the operator context. However, it is dense and uses spec-revision language ('Spec v0.1', 'spec-renamed alias') that may be verbose without aiding utility. Every sentence contributes but structure could be cleaner.

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 (10 parameters, nested objects, no output schema, no annotations), the description explains the orchestration flow but lacks specifics on input requirements, error handling, prerequisites, or return value format. Leaves gaps for an AI agent to select and invoke correctly.

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

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

Schema description coverage is 0% despite 10 parameters (some nested). The description hints at channels (linkedin, x, etc.) and image briefs, but does not explicitly map to schema properties like goal, brand, productUrl, or idempotencyKey. High complexity with minimal compensation.

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 an end-to-end launch orchestrator with a specific verb and resource ('launch product'). It mentions composed sub-tools (chieflab-post, chieflab-email) and distinguishes from them, but the dense spec-wording may reduce immediate clarity.

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 states it is the 'primary' operator and a spec reference, but does not explicitly say when to use this tool vs. alternatives like chiefmo_launch_product or other launch-related siblings. It notes backwards compatibility with chiefmo_launch_product, leaving confusion about which to invoke.

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 that the tool lists channels and returns a grouped catalog with capabilities and providers. It implies a read-only operation and adds value by outlining the output structure. Minor omission: does not mention if data is real-time or cached, 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?

The description is concise but packs substantial information: the list of channels, output structure, and usage guidance. The 'P13 —' prefix slightly hinders clarity but is a minor flaw. Overall, it is 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 no output schema, the description adequately explains the return format (grouped by category with capabilities and providers). The single parameter is well-documented. The tool's purpose is clear in the context of siblings, making the description complete for agent selection and invocation.

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

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

The input schema has 100% coverage for the single optional 'category' parameter, which is described as filtering by category. The description reinforces this by mentioning grouping by category. The schema already provides sufficient meaning, so the description adds minimal extra value.

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

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

The description clearly states the tool lists every channel ChiefLab can ship to, enumerates specific platforms, and explains the output grouping by category with capabilities. It distinguishes itself from siblings like 'chieflab_list_channel_readiness' by focusing on the full catalog.

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 tells when to use the tool: 'USE WHEN the user asks what channels can ChiefLab post to? or before calling chiefmo_launch_product to pick the right channels.' This provides clear context and an alternative 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, and description does not disclose behavioral traits like side effects, idempotency, or authorization needs. Only states what it returns, lacking transparency beyond that.

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 front-loaded with purpose and includes useful usage context. Slightly verbose with internal label 'P13' but overall 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 no output schema, description lists possible states and applications. Lacks mention of prerequisites or authentication, but sufficient for a read-only list 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?

Input schema has 100% coverage on one optional parameter. Description adds context by referring to 'THIS workspace' but does not significantly extend schema meaning.

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

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

The description clearly states that the tool returns readiness states for every channel in a catalog for the current workspace, listing possible states and specific use cases. It distinguishes itself from siblings like 'chieflab_list_available_channels'.

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

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

Explicitly tells when to use (e.g., user asking about publishable channels, before chiefmo_launch_product). Does not mention when not to use, but provides clear 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?

No annotations provided, so description bears full responsibility. Describes output as 'all connectors across all operators with OAuth-live status,' implying read-only behavior. Lacks details on authorization requirements, pagination, or any 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?

Two sentences, front-loaded with usage scenario, then output description. No wasted words, 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?

For a simple list tool with no parameters, description covers purpose and key output detail (OAuth status). Could mention returned fields or format, but not critical. Adequate given sibling tools cover specific actions.

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

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

Input schema has zero parameters, so description does not need to add parameter information. Schema coverage is 100% trivial. Baseline for no parameters is 4.

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

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

Description clearly states the tool lists third-party connectors for the workspace with OAuth-live status. It distinguishes from siblings like 'chieflab_connector_status' by focusing on listing all connectors rather than a single one.

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' phrase provides clear context for which scenarios to use the tool, including example queries. Does not explicitly mention when not to use, but the context is sufficient given the sibling tools.

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

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

No annotations provided, so description carries full burden. It states the tool returns verified sender domains, indicating a read-only operation. Mentions API key requirement but lacks details on error handling or quota limits. Adds moderate 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?

Two sentences, front-loaded with use case and prerequisite. No redundancy or 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 low complexity (one optional param, no output schema), description adequately covers purpose, usage trigger, and key prerequisite. Lacks details on return format or failure modes, but sufficient for a simple list 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 one optional workspaceId parameter. Description does not add meaning beyond schema; it only references 'the workspace' which is already in schema. Baseline 3 applies.

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

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

Description specifies the tool lists verified Resend sender domains for the workspace, with explicit use cases like 'list my verified email domains' and 'which sender can I use?'. This distinguishes it from sibling tools focused on sending emails or managing keys.

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?

Starts with 'USE WHEN' and provides example queries. Also notes prerequisite: requires RESEND_API_KEY either from env or set via another tool. Gives clear context for 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?

No annotations provided, so description carries full burden. It discloses sorting order and relationship to another tool, but does not mention read-only nature, authentication needs, or error conditions.

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

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

Two sentences, front-loaded with purpose and sorting order. Efficient and to the point without 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 no output schema and 0% schema coverage, the description is incomplete. It does not explain what the response contains (e.g., template objects), nor details on parameters. It covers sorting and usage link but lacks full 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?

Schema coverage is 0%, so description should explain parameters. It does not describe 'limit' or 'workspaceId' explicitly, only implies workspace scope. Missing 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 lists launch templates for the workspace, sorted by most recent first. It also distinguishes from siblings by referencing chiefmo_launch_product, giving 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?

It explains when to use the tool (to list templates) and how to use the output with another tool. However, it does not explicitly state when not to use it 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?

No annotations provided; description carries full burden. The verb 'list' implies a read-only, non-destructive operation. While it doesn't explicitly state no side effects, the context of listing playbooks is clearly a safe query. Adequately 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: first explains what the tool does, second explains usage context. No wasted words, front-loaded with key information.

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

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

For a simple list tool with no parameters and no output schema, the description fully covers what it returns (playbook details) and when to use it. No missing 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?

Input schema has zero parameters, so no parameter description needed. Baseline of 4 is appropriate since the description does not need to add meaning 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 lists launch playbooks with specific fields (channel mix, angle, ICP, etc.) and explicitly differentiates its use from sibling tools like chiefmo_launch_product. It gives a precise verb+resource description.

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 context: 'before calling chiefmo_launch_product' and when auto-detection is wrong. Implicitly guides to use chiefmo_launch_product after. Could be slightly more explicit about when not to use, but overall 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 must carry the burden. It discloses that the tool returns connected accounts with accountIds, implying a read-only operation with no side effects. It does not, however, mention error behavior (e.g., missing API key), which would improve 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?

The description is remarkably concise: two sentences. The first sentence front-loads the purpose and usage context. Every word adds value; there is no fluff or repetition.

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

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

The tool has no output schema, so the description should explain the return value fully. It states the tool returns 'connected Zernio accounts ... with the accountIds', which is adequate for a simple list. However, it does not specify the structure (e.g., list of objects with platform type, account name) or whether pagination exists. For a tool with one optional parameter, the description is minimally 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 1 optional parameter (workspaceId) with full description in the schema. Schema description coverage is 100%, so the description adds no extra meaning beyond restating that it's optional and defaults to the workspace from the API key. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: retrieving accountIds for publishing. It specifies the use case ('USE WHEN you need accountIds before publishing'), lists the social platforms (LinkedIn, X, Threads, etc.), and explains the output's downstream usage (passed to chiefmo_publish_approved_post). This distinguishes it from sibling tools that handle publishing, approval, 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?

The description provides explicit usage context with 'USE WHEN' and example queries. It mentions a prerequisite (ZERNIO_API_KEY via environment or previous tool call). However, it does not state when not to use this tool or offer alternatives among siblings, so it loses one point.

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

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

With no annotations provided, the description carries the full burden. It discloses the default behavior (active tenants) and filtering options for paused/archived. However, it doesn't mention output format, pagination, or side effects, which is adequate for a simple read operation 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?

Two well-structured sentences, each sentence earning its place. Clearly front-loaded with purpose and usage examples, 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 the simplicity of the tool (single optional parameter, no output schema, no nested objects), the description covers the essential behavior. It lacks details like pagination or return structure, but for a basic list tool, this is complete enough.

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% (one parameter described). The description merely rephrases the schema's 'active | paused | archived (default: active)' as 'pass status='paused'|'archived' for others,' adding minimal new information. 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?

Description clearly states the tool's purpose: listing end-users/tenants configured in the workspace, with example natural language queries. It distinguishes itself from sibling tools like chieflab_create_tenant and chieflab_set_tenant_context by focusing on reading existing tenants.

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 ('USE WHEN you want to see...') and how to use (default active, pass status for others). Lacks explicit 'when not to use' or alternatives, but the context is clear and sufficient for basic usage.

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 fully disclose behavioral traits. It mentions 'readback' but does not explicitly confirm read-only nature, state authentication requirements, rate limits, or side effects. The description lacks sufficient behavioral transparency beyond the high-level operation.

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

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

The description is extremely concise, with two short sentences that front-load the core purpose and alias information. No unnecessary words or repetition.

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

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

Given two parameters, no output schema, and no annotations, the description is incomplete. It does not explain input requirements, output structure, or any constraints. The tool's complexity (multiple data sources, structured metrics, brief) is not fully addressed.

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

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

Schema description coverage is 0%, and the description does not explain any of the parameters (runId, workspaceId). It only describes the output data sources and format without clarifying what the inputs represent or how they affect execution.

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's purpose as a 24h post-launch readback that pulls specific data sources (Zernio engagement, GA4 traffic, Search Console queries) and returns structured metrics plus a next-iteration brief. It also identifies itself as a spec-renamed alias, providing full clarity.

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

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

The description implies usage context (24h post-launch) but does not explicitly state when to use or not use this tool versus alternatives. There is no guidance on when to prefer this over chiefmo_measure_launch_results or 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?

With no annotations, the description discloses that it is approval-gated, returns specific outputs, and falls through to the launch handler. It could mention failure modes or rate limits, but overall it is informative.

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

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

The description is concise and well-structured: operator, function, inputs/outputs, usage, behavioral notes. 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?

Provides a good overview but lacks details on all 8 parameters and precise return types. Given no output schema or annotations, more parameter documentation would improve completeness.

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

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

Schema coverage is only 13% (only channel has description). The description vaguely mentions 'product context' but does not detail other parameters like goal, brand, or tenantId. It fails to compensate for the low coverage.

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

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

The description clearly states it's a single-channel publish loop that returns a draft, publishAction, and reviewUrl. It distinguishes itself from the 'full launch loop' but does not explicitly differentiate from sibling tools like 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 explicit usage scenarios ('when user says post this to linkedin...') and notes when the full launch loop is overkill. Lacks a when-not-to-use condition but gives clear 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?

Despite no annotations, the description fully discloses behavioral traits: it flips the publishAction status, persists a proof_asset, and queues metrics readback. This gives the agent a clear understanding of side effects and consequences of not using the tool.

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

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

The description is a single, well-structured paragraph. It front-loads the usage condition and explains functionality and consequences concisely. No unnecessary text, but could be broken into multiple sentences for 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 5-parameter schema and no output schema, the description provides sufficient context: usage scenario, side effects, and importance. It does not describe return values, but the tool's purpose implies it completes an action, and the side effects are well explained.

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

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

Input schema has 100% coverage with good descriptions. The tool description adds overall context but does not significantly enhance individual parameter meaning beyond the schema. However, it references the fallback tool for actionId and publishedUrl, slightly elevating utility.

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: to record the live URL of a manually posted channel back to ChiefLab. It specifies the verb 'record' and the resource 'manual publish', and distinguishes from siblings by tying to chieflab_use_manual_fallback and explaining the closed loop continuation.

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', explicitly stating the condition (user manually posted using chieflab_use_manual_fallback) and the motivation (feed back URL for closed loop). It also warns that without this tool, manually-posted channels are lost, 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?

With no annotations, description fully discloses behavior: it writes to reviewUrl, closes the loop, is idempotent ('second call replaces prior rendered body'). Does not mention potential side effects like overwriting, but idempotency implies safe replacement.

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. Key directive first. Every sentence adds value. Well-structured and front-loaded.

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

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

Given no output schema, description adequately explains purpose, usage, and idempotency. Does not detail return values, but tool is simple write operation. Sufficient for selection and invocation.

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

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

Schema coverage is 100%, but description adds meaningful context: body is 'final rendered copy', 'Markdown is fine', 'replaces brief on reviewUrl'. assetId has origin hint. renderedBy explained. 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?

The description starts with a clear 'USE WHEN' statement specifying the exact scenario. It distinguishes the tool from siblings by explaining the orchestration loop (ChiefLab returns briefs, LLM renders, this tool writes). It uses specific verbs: 'rendered copy', 'writes', 'reviewers approve real text'.

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 context on when to use ('when your agent's LLM has rendered the final copy'). Describes the orchestration flow and where to get the assetId. Lacks explicit 'when not to use' or alternatives, but the context is clear enough.

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

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

No annotations are provided, so the description carries the full burden. It explains that the tool stores examples per tenant and creates a 'rejection becomes memory loop', implying persistent state changes. However, it lacks details on side effects like overwriting behavior, data limits, or authentication requirements.

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

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

The description is concise and front-loaded with the key use case, though the last sentence ('The 'rejection becomes memory' loop.') adds a metaphorical flourish that could be considered non-essential.

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

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

For a tool with 5 parameters, no output schema, and no annotations, the description covers purpose and usage but lacks details on how the stored data is later accessed, size constraints, or error handling. It is adequate but not comprehensive.

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 80%, and the description adds value by explaining the 'feedback' parameter with an example ('too corporate, make it more founder-led'). However, it does not meaningfully expand beyond the schema's own descriptions for 'kind', 'content', or 'channel'.

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

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

The description clearly identifies the tool's purpose: storing approved/rejected examples for learning. It uses specific verb+resource ('stores approved/rejected examples per tenant') and distinguishes from siblings by focusing on voice learning, not channel preference or brand voice recording.

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 the user approves or rejects a draft and you want the next run to learn', providing explicit usage context. However, it does not mention when not to use it or suggest alternatives among the many 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 are provided, so the description bears full responsibility for behavioral disclosure. It states that data persists into the 'P9 brain', implying a write and storage operation. However, it does not specify whether preferences are overwritten or appended, or how workspaceId (an optional parameter) affects behavior. This is adequate but not rich.

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

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

The description is very concise: two sentences front-load the purpose and example, with no extraneous information. Every sentence contributes meaning.

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

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

Given the tool's specific role in recording preferences for chieflab_launch_product, the description is largely complete. It explains the input, persistence, and downstream consumer. However, it lacks details about default behavior when workspaceId is omitted or whether existing preferences are replaced. Still, it is sufficient for an agent to understand when and how to use the tool.

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

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

Schema description coverage is 67% (two of three parameters have descriptions, but workspaceId's is empty). The tool description provides an example for the 'preference' parameter, adding some value beyond the schema's 'Free-form preference object' label. However, it does not clarify the 'channel' parameter's format or the role of 'workspaceId'. Overall, it compensates moderately for the schema 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 that the tool records a per-job preference for a workspace, with concrete examples like 'prefers LinkedIn over X for B2B launches'. The verb 'record' and the specified resource (workspace per-job preference) are unambiguous. This differentiates it from sibling tools like chieflab_launch_product, which reads these preferences.

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 that future runs of chiefmo_launch_product read these preferences, indicating the tool should be used before launching. It does not explicitly list when not to use it or provide alternatives, but the context of persisting preferences for a specific workflow 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 provided, so description carries full burden. It states the return value (ready or still blocked), but does not disclose side effects (e.g., whether any state is modified) or error conditions. 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?

Two concise sentences with no filler. Front-loaded with action and purpose, followed by usage guidance. 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 no output schema, the description provides return value context. Covers the main use case, but lacks details on error handling or exact response format. Still sufficient for the tool's 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 describes actionId but not workspaceId (50% coverage). The tool description adds no additional meaning for parameters; workspaceId remains unexplained. The description does not compensate for the schema 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?

Clearly states the verb 're-check' and the resource 'blocked publishAction's connector readiness'. Explicitly distinguishes from sibling tools like chiefmo_publish_approved_post and chiefmo_send_approved_email by mentioning they can be re-fired after retry.

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 scenario: 'USE WHEN the user has just connected a missing provider and the agent needs to know if the action can fire.' Also suggests subsequent actions, but does not mention when not to use it or alternative tools.

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

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

No annotations provided, so the description partially compensates by stating what is not saved (productUrl and assets). However, it does not disclose potential side effects, permissions needed, or whether existing templates are overwritten. More behavioral context is needed.

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

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

Two sentences front-loaded with the purpose. Each sentence adds essential information: first defines the action and resource, second provides usage context and exclusions. 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?

No output schema, and the tool has 10 parameters with nested objects. The description gives a high-level understanding but lacks details on error handling, uniqueness constraints, or return value. Sibling listing tool exists for retrieval, but overall completeness is moderate.

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 40% (label, description, sourceLaunchId, productType have descriptions). The description adds meaning by listing captured fields (channels, angle, brand, default repo context) and clarifying sourceLaunchId's role. But it does not cover all 10 parameters, especially nested objects like icp and brand.

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?

Clearly states the tool saves a launch's shape (channels, angle, brand, default repo context) as a reusable template, specifying verb and resource. Also explicitly excludes productUrl and assets, distinguishing its scope from the template listing sibling.

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

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

Explicitly states when to use ('AFTER a launch performs well') and provides a rationale (skip re-asking for next launch). Does not mention alternatives or when not to use, but the guidance is clear 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 carries full burden. It specifies that the tool re-scores on six dimensions, which is a read-like operation. It does not mention side effects or safety, but the action (scoring) implicitly suggests it is non-destructive. Slightly more explicit behavioral disclosure would improve clarity.

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: first states purpose and lists dimensions, second gives usage context. No redundant or extraneous information. Every sentence is essential and efficiently conveys the needed 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?

The description lists the six quality dimensions, so the agent knows what the output will cover. It also provides a prerequisite. However, there is no output schema and the description does not explicitly describe the return format, such as whether it returns scores or a full report. A slight gap in output expectations.

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 description adds value beyond structural details. For runId, it explains that it is the launchId from chiefmo_launch_product and that ChiefLab pulls the latest assets and actions to rescore. This context helps the agent understand parameter purpose 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 rescored a launch pack on six named quality dimensions. It distinguishes from siblings by referencing chieflab_record_rendered_copy and chiefmo_launch_product, making the specific action and resource unambiguous.

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

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

Explicitly instructs to use AFTER chieflab_record_rendered_copy populates rendered bodies, and explains why the original chiefmo_launch_product score is insufficient. This provides clear when-to-use and contextual rationale.

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-gating and uses Resend, but does not disclose behavioral traits like side effects (email sending), idempotency, error conditions, or rate limits. Critical behavioral aspects are missing.

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 efficient sentences front-load the purpose and include useful alias information. No unnecessary words, but could be slightly more structured with parameter guidance.

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

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

Despite moderate complexity, description lacks details on return values, error handling, idempotency, and parameter semantics. Without output schema, the description is insufficient for an agent to fully understand the tool's 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 coverage is 0%. Description only explains actionId briefly ('from a prior launch's publishActions'). runId and idempotencyKey are not explained at all. The description fails to compensate for the lack of schema documentation.

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 sends an email via Resend, is approval-gated, and is an alias of another tool. It specifies the verb 'send' and resource 'email', distinguishing it from sibling tools like chieflab_measure or 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?

Explicitly instructs to pass actionId from a prior launch's publishActions after user approval. Provides clear context for when to use, but does not explicitly exclude other scenarios or mention alternatives beyond the alias.

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 must fully disclose behavior. It mentions encryption and source for the key but does not cover overwrite behavior or additional 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, front-loaded with usage condition, no filler. Every sentence adds 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 output schema and two parameters, the description is fairly complete for a key-setting tool. Could mention default workspace behavior but 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?

Schema has 100% coverage with descriptions; description adds context (API key format, encryption) and explains the role of workspaceId. 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?

The description clearly states the tool stores a Resend API key for per-workspace email sending, using specific verbs and distinguishing from sibling tools that consume the key.

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 starts with 'USE WHEN' to indicate the scenario, and mentions related tools that use the key. Lacks explicit when-not guidance, 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?

With no annotations provided, the description carries the full burden. It discloses that the operation is an upsert (create or update) and that it stores specific brand context fields. It also notes that all operator outputs for the tenant will ground against this context. However, it omits details like authentication requirements, rate limits, or side effects beyond grounding.

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 extremely concise—three sentences with bullet-style listing of stored fields and a clear usage directive. No unnecessary words or redundancy. 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 complexity (9 parameters, no output schema, no annotations), the description covers the core purpose, usage, and stored fields. It lacks explanation of return values (but no output schema is defined) and some parameters like 'category' are not explicitly mentioned. Still, it is largely complete for an upsert 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 only 33%, so the description must compensate. It lists the stored fields (brand name, audience, voice, pillars, competitors, content angles, positioning risks), which maps to most parameters but misses 'category'. The description adds context that the parameters define the brand context, but does not explain each parameter in detail.

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 (upsert/set), the resource (tenant brand context), and the specific use cases (onboarding new tenant, refreshing brand). It provides example queries and distinguishes itself from the sibling tool chieflab_create_tenant by suggesting to pair with it.

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

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

The description begins with 'USE WHEN' and gives explicit scenarios (onboarding, refreshing) with example natural language requests. It also recommends pairing with chieflab_create_tenant, guiding the agent on when to use this tool versus alternatives. However, it does not explicitly state when NOT to use it.

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

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

The description says 'Add or update' and 'Returns the updated member record,' but lacks details on idempotency, permissions required, side effects, or behavior for duplicate members. With no annotations, the description should compensate.

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, front-loaded sentence (19 words) that conveys the core action, allowed values, and return value without unnecessary detail.

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

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

Given no annotations, no output schema, and 50% param coverage, the description provides the core behavior and return value but omits error handling, permission context, and update semantics. Adequate but not thorough.

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

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

Schema description coverage is 50%. The description adds clarity by listing allowed roles and noting that userId is typically a Supabase auth.uid or email. However, email and workspaceId parameters lack descriptions in both schema and description.

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

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

The description clearly states the action: 'Add or update a workspace member's role' and lists the allowed roles. It distinguishes from sibling tools like chieflab_workspace_members (list) and chieflab_create_tenant (different scope), though no explicit differentiation is provided.

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

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

No guidance on when to use this tool versus alternatives. It does not explain when to add vs. update, nor mention prerequisites like workspace existence or membership status.

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 encryption at rest and side effects (other tools use this key). No annotations exist, but description adequately covers behavioral traits for a credential-setting 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 usage guidance, efficient with no wasted words.

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

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

Fully covers tool's purpose, usage context, encryption behavior, and downstream impact. No output schema needed; description is complete for this simple 2-param tool.

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

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

Schema description coverage is 100%, so baseline is 3. Description adds minimal extra meaning beyond schema (e.g., pattern hint 'sk_live_...'), but does not significantly improve 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?

Clearly states the tool stores a Zernio API key encrypted for a workspace, with explicit usage condition (user bringing own account). Distinguishes from sibling tools by its specific function.

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 user wants to bring their own Zernio account instead of default'), and lists downstream tools that depend on this key, providing clear 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, description thoroughly explains behavioral traits: workspace creation, key return, config writing, delivery URL expiration (1 hour), single-use, IP rate limit. 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?

Concise single paragraph with front-loaded usage condition. Could benefit from bullet points for the two flows, but still clear and efficient.

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

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

No output schema, but description explains the return value (apiKey) and covers both users' ability to write config or use delivery URL, including expiration and rate limits. Complete for a signup 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 100% coverage with good parameter descriptions. The description repeats the same info without adding new semantics beyond the schema.

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

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

The description clearly states it creates a new workspace and returns the API key, distinguishing it from other tools that involve reading, connecting, or other operations.

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 condition: 'when the user has no ChiefLab API key yet and you've gotten a 401 error'. Provides two clear flows (preferred and fallback).

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 carries the full burden. It discloses the output format, the approval gate, and the conversion of a blocked channel into a manual ship. However, it does not explicitly state whether the tool has side effects or mutates state, which would be useful for a tool that 'converts' something.

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 but effectively front-loads the usage condition with 'USE WHEN'. It is slightly verbose but all sentences contribute meaningful information. Could be more structured with bullet points, but current form is acceptable.

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

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

Given the tool has 5 parameters, no output schema, and no annotations, the description provides sufficient context: trigger condition, output components, prerequisite, and even a future measurement template. It lacks error handling details but is otherwise complete for an agent to 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 the baseline is 3. The description adds context for the overall tool but does not provide additional meaning beyond the schema descriptions for parameters like channel, actionId, subreddit, etc. The schema already adequately describes each parameter.

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

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

The description clearly states the tool's purpose: to provide a manual-publish brief when a launch action is blocked due to lack of automated publish path. It specifies the exact output and differentiates from sibling tools like 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?

The description starts with 'USE WHEN' and explicitly states the condition for using the tool (blocked automated publish path). It also mentions the prerequisite that the originating publishAction must be approved, providing clear guidance on when to call this tool.

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

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

No annotations exist, so the description carries full weight. It correctly implies a read-only operation but omits details like whether workspaceId is optional, response structure, or pagination. Basic transparency is present 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?

Two concise sentences, with the core action front-loaded. No wasted 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?

For a simple, single-parameter tool with no output schema, the description covers the basic functionality and a use case. However, it lacks explanation of the parameter and does not mention any defaults or limitations, leaving the definition barely 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 0% schema description coverage, the description should explain the workspaceId parameter. It mentions 'workspace members' in the description but never names or describes the parameter, leaving the user to infer that a workspaceId is needed or potentially optional.

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

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

The description clearly states the action ('List'), the resource ('workspace members'), and the specific roles included. It also provides a use case, making the purpose unambiguous and distinguishing from sibling tools like 'chieflab_set_workspace_member'.

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 suggests using this tool for team management and identifying approvers. However, it does not explicitly contrast with alternative tools (e.g., chieflab_workspace_metrics) or state when not to use it. The context is clear but lacks exclusions.

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

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

No annotations are provided, so the description carries the full burden. It implies read-only behavior but does not explicitly state that it is non-destructive or describe any other behavioral traits like authentication needs or rate limits. However, the list of returned metrics provides some 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?

The description is a single sentence followed by a usage hint, front-loaded with the main purpose. Every word earns its place; 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?

For a simple read tool with one parameter and no output schema, the description adequately lists the metrics returned and gives a clear use case. Nothing essential is missing.

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

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

Schema coverage is 100% (one optional parameter). The description adds minimal value beyond the schema: it confirms the parameter is for a workspace but does not provide additional context or constraints. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: getting scale metrics for a workspace, and lists specific metrics. It distinguishes itself from sibling tools which are largely operational or content-related.

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 recommends using this tool for 'the scale dashboard or how is this account doing checks', providing clear context. It does not mention when not to use it or alternatives, but the use case is well-defined.

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 the approval enables execution but does not address idempotency, error handling, or side effects. Given the action is simple, it provides adequate but not exhaustive 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?

The description is well-structured, front-loaded with purpose, then usage and parameter details. Every sentence adds value; 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 the approval flow with sibling executors, the description covers the entire usage scenario: user intent, mapping to channel and actionId, calling the tool, and its role in the pipeline. It mentions sibling tools and explains the IDE-native path. No output schema is needed as return is straightforward.

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 significant value: explains actionId is preferred, id is legacy, and details how to derive actionId from launch response via channel mapping and agentGuide.renderInChat. This exceeds schema info.

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

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

The description clearly states that the tool approves a ChiefMO publish/send action, enabling its executor (publish_approved_post / send_approved_email) to fire. It distinguishes from sibling tools like chiefmo_approve_run (which operates on runs) and the executors themselves.

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: when the user says 'approve <channel>', 'approve all', 'ship it', etc. Also explains the IDE-native approval path and that no push to reviewUrl is needed, providing a clear when-not.

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 fully disclose behavioral traits. It only states that the tool approves a run, but does not explain side effects, required permissions, what happens after approval, or any constraints. This is insufficient for a mutation tool.

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

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

The description is a single sentence, making it concise. However, it is overly minimal and lacks structure (e.g., no bullet points or separated sections). It could be improved by adding more details without becoming 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 the simplicity of the tool (1 required param, no output schema, no annotations), the description is too sparse. It fails to provide complete context for an agent to use the tool correctly, especially regarding parameter meaning and behavioral consequences.

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 0% description coverage for the single parameter 'id'. The description does not explain what 'id' refers to (e.g., run ID) or provide any additional meaning beyond the schema.

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

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

The description clearly states the verb 'Approve' and the resource 'ChiefMO run' with the context 'awaiting approval'. It differentiates from sibling tools like chiefmo_reject_run by specifying the action type.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, conditions for approval, or when not to use it. The sibling list includes chiefmo_reject_run, but no comparison is made.

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 discloses the tool's non-destructive scanning behavior, the process of checking against company brain, and the output format with three statuses. Does not mention any side effects or limitations.

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

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

Concise description of three sentences, front-loaded with core action ('Scan a draft... for unsupported claims BEFORE publish'). 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?

No output schema, so description adequately covers return values (ranked list with status and fixes). Could mention more about output structure or edge cases, but sufficient for a tool of this complexity.

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

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

Input schema has 100% description coverage for all parameters. Description adds context about checking claims against company brain and optional pre-extraction, but does not significantly enhance parameter understanding 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 it scans draft content for unsupported claims before publishing, specifying content types (post/email/blog/ad/landing copy) and output (ranked list with status and fixes). Distinguishes from siblings by being a dedicated claim-checking publish gate.

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 'BEFORE publish' and 'Run as a publish gate', providing clear context for when to use. However, does not explicitly mention when not to use or compare with alternatives like diagnostic 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 description carries full burden. It implies a read operation by stating it 'returns' data but does not explicitly state it is non-destructive or mention authentication needs, rate limits, or other behavioral traits. Adequate but could be improved.

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: first describes output and components, second lists use cases. No extraneous information. Efficiently communicates purpose and usage.

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?

Tool returns a complex aggregated object. Description lists all components (brand voice, repo facts, etc.) and provides use cases, giving a good sense of return structure. No output schema, but description partially compensates. Could mention that it returns a single object.

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 3 parameters with 33% coverage (format has description). The description does not add meaning to tenantId or workspaceId beyond their names. It mentions 'workspace' but not format or tenantId. Parameter semantics are not enhanced by the description.

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

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

Description clearly states the tool returns the full rendered company brain for a workspace, lists components (brand voice, repo facts, etc.), and provides specific use cases (inspect, debug, pipe into prompt). This clearly distinguishes it from sibling tools that extract individual components.

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 says 'Use to inspect what ChiefLab knows, debug grounding, or pipe into a custom prompt.' This gives clear context for when to use the tool, though it does not mention when not to use or provide 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 the description carries the full burden. It discloses the tool's behavior: resumes a loop, surfaces reviewUrl, executes approved actions, waits for measurement, measures results, or prepares the next move. It also explains the design rationale (stateless agents). Missing explicit disclosure of side effects or safety, 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?

Two sentences: first defines action and parameter, second provides usage context. Front-loaded with core purpose. The second sentence is somewhat lengthy listing multiple actions, but overall efficient and clear.

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 implies what the tool does (surfaces reviewUrl, etc.) but doesn't explicitly state return value. It covers when, what, and why. For a state-management tool, this is reasonably complete. Could mention output 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 covers both parameters with descriptions. The description adds context: runId is the launchId returned by specific tools, workspaceId is optional and usually supplied by hosted auth. This adds value beyond the schema, though schema coverage is 100%.

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 'Resume' and resource 'ChiefLab launch loop from runId'. It lists specific use cases (surface reviewUrl, execute actions, etc.) and distinguishes this tool from siblings by explaining its role in continuing a stateful loop after other tools have been called.

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 an agent has already called chieflab_get_users_after_build / chiefmo_launch_product and needs the exact next action'. It also lists what the tool does (surface reviewUrl, execute approved action, etc.) and explains why this tool exists (stateless agents, ChiefLab remembers state).

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 present, so the description must fully disclose behavioral traits. It only states 'Create,' implying mutation, but offers no details on side effects, idempotency, or what happens if a run already exists.

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

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

The description is a single, concise sentence. It is front-loaded with the key action, but could include more detail without being verbose. 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?

The description lacks essential context for a create operation. It does not explain what a marketing run is, what the output (e.g., run ID) will be, or any constraints. Given the simple schema, more completeness is expected.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds minimal value by restating 'from a business goal,' which aligns with the goal parameter, but does not enrich understanding of workspaceId or provide additional 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 action (Create), the resource (ChiefMO marketing run), and the input (from a business goal). It effectively distinguishes the tool from siblings like chiefmo_get_run and chiefmo_list_runs.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or comparison with other run-related 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 description covers model used, cost per image, and auto-application of brand context. Lacks details on failure modes or output format but adequate for an image generation 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: purpose and model, usage guidance, cost. Each sentence is informative and front-loaded. 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?

Covers core purpose, usage, cost, and parameter behavior. Missing output spec (e.g., what the response contains) but acceptable for a tool with no output schema and clear usage 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?

Schema covers all 4 parameters with descriptions. Description adds value by stating brand context is auto-applied if tenantId or brand is passed, 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?

Clearly states it generates social/launch graphics using Gemini 2.5 Flash Image. Distinguishes from sibling tool chiefmo_launch_product that includes graphics inline.

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 trigger phrases ('launch images, social graphics, hero images') and specifies when not to call (use chiefmo_launch_product for inline graphics). Clear usage boundaries.

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 must cover behavioral traits. It lists output components but does not disclose side effects, permissions, or whether it saves data. The 'converts' verb implies a read-like operation, but details are missing.

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 that are front-loaded and efficient. The first sentence immediately states the verb and deliverable, the second adds timing and mode details. 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 and 2 params, the description adequately explains the tool's purpose and high-level outputs. However, it does not specify the return format or behavior when connected to external services, leaving some ambiguity.

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

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

Schema coverage is 100%, so the schema already describes both parameters. The description adds minimal extra meaning (ties 'goal' to the marketing question). BrandUrl remains underutilized. Baseline 3 is appropriate.

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

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

The description clearly states 'Convert a marketing question into a runnable experiment' and lists specific outputs (hypothesis, variants, metrics, etc.). It distinguishes from sibling tools like chiefmo_create_run by specifying a design phase before shipping.

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 advises 'Use BEFORE shipping a new test so you have a stop rule.' Mentions manual vs. connected mode, providing clear context. Lacks explicit exclusions, but the usage case is well-defined.

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 clarifies that the tool diagnoses and proposes actions without executing them. It does not claim any side effects, but could more explicitly state read-only behavior.

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

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

The description is a single focused sentence with an example, no wasted words. It front-loads the purpose and is easy to parse.

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

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

Given the simplicity (2 params, no output schema), the description adequately explains input context and output nature. It lacks output format details, but the output is implied as a list.

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 an example input but no additional meaning beyond the schema descriptions for the parameters.

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

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

The description clearly states the tool is for metric anomaly explanation, gives a concrete example, and indicates it ranks causes and proposes actions. It distinguishes itself from sibling 'chiefmo_diagnose_marketing' by focusing on anomalies.

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

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

The description explicitly says to use when there is a metric drop or spike, providing clear context. It does not mention alternatives or when not to use, but the condition is specific enough.

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

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

With no annotations provided, the description carries full burden. It discloses key behaviors: returns evidence, drafted assets, proposed actions, signed reviewUrl, per-tenant memory, and is 'Approval-gated'. It also explains cost/accuracy trade-offs for passing brand context. However, it does not explicitly state side effects like memory writes or secondary consequences of the approval gate.

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 approximately 5 sentences, front-loaded with purpose and guidance. It is efficient and avoids fluff, but the last sentence about approval-gated could be integrated earlier for tighter flow. Overall well-organized for quick comprehension.

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 (9 parameters, nested objects, no output schema), the description covers returns, usage context, cost optimization, and approval gating. It lacks a detailed return structure but provides sufficient context for an agent to understand the tool's purpose and behavior. Some minor gaps exist in explaining how approval-gating works in practice.

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% with detailed descriptions for each parameter, so the baseline is 3. The main description does not elaborate on parameter semantics beyond listing return types, but the schema descriptions themselves are rich. The main description adds no additional meaning for individual parameters beyond what the schema already provides.

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

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

The description explicitly states 'Diagnose an EXISTING marketing program' and contrasts with 'not a new launch — use chiefmo_launch_product', providing a clear verb and resource. It specifies when to use with concrete examples ('why is X not working' or 'iterate on this funnel') and lists what the tool returns, distinguishing it effectively from siblings.

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

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

The description gives explicit when-to-use criteria ('user already has live campaigns and asks why is X not working') and when-not-to-use ('For first launches, prefer chiefmo_launch_product'), including the exact name of the alternative tool. This provides clear usage boundaries without ambiguity.

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 behavioral aspects: draft creation with specific fields, two modes (manual vs direct commit), and tracking dependencies. Missing details on auth requirements, rate limits, or error handling, but overall 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?

Two concise sentences: first lists all output components, second covers the two modes and tracking. Front-loaded with essential info, no redundant 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 and modes adequately for a draft tool. Lacks details on output format specifics, error behavior, or what happens upon failure. No output schema, but description doesn't explain return structure.

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

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

Schema coverage is 100%, so baseline is 3. The description enriches parameters: 'repoContext' is explained as increasing grounding, and 'brandUrl' as optional context. This adds value 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?

Clearly states it drafts a full repo-aware blog post with specific elements (title, slug, meta description, body, internal links, OG image prompt). Explicitly distinguishes from siblings like chiefmo_generate_social_posts by focusing on 'repo-aware blog post' and mentioning modes.

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 modes (manual vs GitHub) and tracking conditions, providing context on when each mode is used. However, no explicit guidance on when NOT to use this tool vs alternatives like chiefmo_generate_landing_copy or chiefmo_generate_email_sequence.

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 shoulders the burden. It describes what the tool returns but does not disclose side effects, authentication needs, rate limits, or error behavior. It implies a read-only operation but doesn't confirm.

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 usage context, no redundancy. Every word earns its place.

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

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

For a single-parameter extraction tool, the description covers the key return fields and usage context. It lacks details on error handling or behavior for invalid inputs, but these are minor gaps given the tool's simplicity.

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

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

The input schema has one parameter with 100% description coverage. The description repeats the schema's documentation ('URL or brand name') without adding new semantic detail, so it meets the baseline but does not exceed.

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 extracts brand DNA from a URL or brand name, listing specific outputs like category, audience, voice, etc. It distinguishes from siblings by noting it should be the first call for any brand work.

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 starts with 'USE WHEN' and provides example triggers, and advises using it as the first call for brand tasks. It lacks explicit when-not guidance, 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 full burden. It mentions 'emit a list ready to persist' but does not disclose whether the tool is read-only, whether it persists data itself, any side effects, or required permissions. The behavioral profile is under-specified.

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

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

The description is a single sentence with no wasted words. It front-loads the purpose. Could be slightly more structured, but remains efficient.

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

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

With 3 parameters (one required), nested objects, and no output schema, the description provides only a high-level output ('list ready to persist'). It lacks detail on the return format or what constitutes a 'product fact'. Adequate but not 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 67% (goal and repoContext have descriptions; brandUrl lacks one). The description adds no extra detail beyond what the schema already provides, and does not compensate for the missing brandUrl description. Baseline score is appropriate.

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

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

The description clearly states the verb ('Pull'), resource ('structured product facts'), and source ('repoContext'). It distinguishes from siblings like chiefmo_extract_brand_dna by specifying 'product facts' and the specific input shape.

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

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

The description implies usage context (extracting facts from repoContext) but provides no explicit guidance on when to use it versus alternatives, no when-not-to-use conditions, and no exclusions.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns ad variants (angles, headlines, primary text, CTAs) and a small test plan, and notes 'drafts only'. However, it does not detail any side effects, authentication needs, 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 extremely concise: two sentences total. The first sentence front-loads usage with clear examples, and the second summarizes output and constraints. 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 tool with 2 parameters and no output schema, the description covers primary inputs, expected output, and a key limitation (drafts only). It could mention prerequisites (e.g., brand context) but is largely complete for its 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 context by specifying the output (ad variants + test plan) and platforms (Meta, Google Ads), which goes beyond the schema's parameter 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 generates paid-ad copy for Meta and Google Ads, with specific examples like 'write Google Ads variants' and 'split-test 5 headlines'. It distinguishes itself from sibling content generation tools by focusing on paid ads.

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 provides explicit examples of user requests that trigger this tool. It also mentions that the output is drafts only and publishing requires approval, giving clear context on when to use and what to expect.

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 output (4-email skeleton with subject line ladder, timing, segmentation notes) and disclosure that it only drafts and requires approval and connected ESP for sending. No annotations to contradict.

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 context, then output details, then limitations. 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?

No output schema, but description explains the return format and dependencies. Covers purpose, usage, output, and constraints. Complete for a draft-generation 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?

Both parameters have schema descriptions (100% coverage). The description adds examples for the goal parameter and implies context for brandUrl, but doesn't significantly enhance 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 generates an email-only sequence, with specific examples (welcome, win-back, nurture). It distinguishes from sibling tool chiefmo_launch_product by noting that tool is for full launches.

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 concrete examples and directs to alternative tool for launch emails. Provides clear context for when to use this tool.

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

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

With no annotations, the description discloses output format and a key behavioral constraint (approval + web tool needed for publish). It does not explain side effects like data storage or authentication requirements, but for a generation tool this is acceptable.

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 'USE WHEN', and no wasted words. Every sentence adds value: usage guidance, output format, and a key constraint. Exceptionally concise.

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

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

Given the simple tool (2 params, no output schema), the description covers purpose, usage, output format, and a critical prerequisite. It lacks info on authentication or idempotency, but remains 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 both parameters have descriptions. The tool description adds minimal extra value beyond the schema, only providing context for 'goal' as what the page sells/converts. Baseline 3 is appropriate.

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

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

The description clearly states the tool generates landing-page copy with specific examples of user requests (e.g., 'write a landing page for X', 'rewrite my hero'). It also details the output (markdown sections) and unique prerequisite, distinguishing it from sibling copy 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 explicitly starts with 'USE WHEN' and lists common user intents, providing clear when-to-use guidance. However, it does not mention when to avoid using it or compare to alternative copy tools like generate_social_posts, which would strengthen guidelines.

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 output includes platform-native hooks, captions, hashtags, and posting cadence, and that it only produces drafts with no scheduling. Additional side effects like data persistence or mutation are not mentioned but less critical for a generation 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 concise sentences with front-loaded usage conditions, no unnecessary words. Every sentence earns its place: usage condition, exclusion with alternative, and output description with limitation.

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 adequately explains what the tool returns (hooks, captions, hashtags, cadence) and its limitation (drafts only, need approval for publishing). For a simple generation tool with 2 parameters, this is 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 descriptions for both parameters (goal and brandUrl). The tool description does not add extra meaning beyond the schema; it only states that brandUrl triggers Brand DNA discovery, which is already implied by schema description. Baseline 3 is appropriate.

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

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

The description clearly states the tool generates social-only drafts, distinguishing it from the sibling tool chiefmo_launch_product that handles full launches. The verb 'generate social posts' and scope 'drafts only' are specific.

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' for social draft requests and 'NOT for new product launches', naming the alternative tool chiefmo_launch_product. Also notes that publishing requires approval via chiefmo_publish_approved_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 are provided, so the description must disclose behavioral traits. It only says 'get by id' without mentioning authentication requirements, response format, error handling, or any side effects. This is minimally informative.

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 very concise (one short phrase), but it is under-specified. It does not earn its place by providing essential additional context beyond a tautological restatement of the name.

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 simple input (one required string) and no output schema, the description should clarify what an 'action' is and what the response contains. It does not, leaving the agent with insufficient context to understand the tool's full purpose or output.

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

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

Schema coverage is 0% (the 'id' parameter has no description in the schema). The description adds no information about the id parameter, such as format, constraints, or examples. It fails to compensate for the lack of schema documentation.

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

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

The description clearly states the verb 'Get', resource 'one ChiefMO action', and method 'by id'. It is specific but does not differentiate from siblings like chiefmo_get_run or chiefmo_get_asset, which have similar purposes.

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

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

No guidance is provided on when to use this tool versus alternatives (e.g., chiefmo_list_actions, chiefmo_approve_action). The description implies usage when an action ID is known but does not state any conditions or exclusions.

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

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

With no annotations provided, the description carries the full burden. It states it gets an asset, implying a read operation, but does not disclose any behavioral traits such as absence of side effects, authorization needs, or error behavior.

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

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

The description is very short (one sentence), which is efficient for a simple getter. However, additional context could enhance completeness without sacrificing conciseness.

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

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

Given the lack of output schema and no explanation of return values or error handling, the description is incomplete. It does not clarify what constitutes a 'ChiefMO generated asset' or how missing IDs are handled.

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 0% description coverage and only one parameter 'id' with no format or type details. The description adds no additional meaning, leaving the parameter under-specified.

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

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

The description clearly states the verb 'Get' and the resource 'one ChiefMO generated asset by id,' but does not differentiate from sibling tools like chiefmo_list_assets, which also deals with assets.

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

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

No guidance is provided on when to use this tool versus alternatives like list_assets or other getters. The description fails to specify the context or prerequisites.

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

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It only repeats the schema constraint ('by id') and does not mention potential errors (e.g., missing id), authentication needs, rate limits, or any side effects. For a read operation, more transparency is needed.

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, short sentence that is efficiently front-loaded. It avoids redundancy and gets straight to the point. However, it could be slightly improved by adding context on what a 'run' is, but overall it is appropriately concise.

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

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

Given the simplicity of the tool (one parameter, no output schema), the description minimally covers the functionality. However, it lacks clarification on return values, error handling, and differentiation from sibling getters like chiefmo_get_action. In the context of many sibling tools, more completeness would help.

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

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

Schema description coverage is 0%, and the description adds no meaning beyond what the input schema already provides. The parameter 'id' is described only as a string, but the description does not clarify its format, source, or context. With low coverage, the description should compensate but fails to do so.

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 'Get one ChiefMO run by id' which includes a specific verb ('Get'), the resource ('ChiefMO run'), and the uniqueness criterion ('by id'). This effectively distinguishes it from sibling tools like chiefmo_list_runs (list multiple) and chiefmo_create_run (create).

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 no explicit guidance on when to use this tool versus alternatives, such as chiefmo_list_runs for retrieving multiple runs or other getters like chiefmo_get_action. It does not state prerequisites, exclusions, or context for use. The only implied usage is that it retrieves a single run by id, but this is not elaborated.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions running an agent and optional inputs, but does not disclose whether the operation is synchronous or asynchronous, what the return value is, what side effects occur (e.g., state changes), or any authentication/rate limiting requirements. More detail would be helpful for safe invocation.

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 with no unnecessary words. It is front-loaded with 'Advanced.' which indicates the target user, and the rest is concise. It could be slightly more structured but remains effective.

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

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

The description explains the use case and parameters adequately given the complexity. However, without an output schema or annotations, the agent lacks information about return values, error conditions, and side effects. The description could include more details to make the tool fully understandable without external 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 description adds some context beyond the schema, such as explaining that inputs are optional and are otherwise pulled from prior handoffs. However, the schema already covers agentId and gtmRunId with descriptions (agentId lists allowed values). Coverage is 67%, so the description provides minor additional value but not significant depth.

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 runs a specific GTM agent in isolation, bypassing the orchestrator. It distinguishes itself from sibling tools like chiefmo_gtm_run_start by specifying it is for re-runs and diffs after upstream handoffs have been collected.

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 'Use when you've already collected the upstream handoffs and want to invoke a single agent for re-runs / diffs.' This provides clear guidance on when to use this tool versus the normal orchestration flow, and it names the circumstance explicitly.

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

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

The description states it is a read operation and mentions ordering. No annotations exist, but for a simple list tool, it adequately conveys non-destructive behavior. Does not cover auth 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?

Two sentences with no redundant words. The purpose is front-loaded, and 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?

For a simple one-parameter list tool with no output schema, the description covers purpose, ordering, and use cases. It is mostly complete, though parameter context is missing.

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

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

The single parameter runId is not explained in the description or schema (0% coverage). The description assumes knowledge of 'GTM run' but does not clarify how to obtain runId.

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

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

The description clearly states the tool reads handoffs emitted in a GTM run, in order, and provides use cases like debugging skipped agents. It distinguishes itself from sibling tools by specifying the resource (handoffs) and action (list).

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

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

The description mentions useful scenarios (command center timeline, debugging), but does not explicitly compare with alternatives like chiefmo_gtm_run_get or chiefmo_gtm_run_list. It lacks '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?

Without annotations, the description carries the burden. It correctly indicates the tool is read-only and returns a key-value snapshot. However, it does not disclose potential errors, rate limits, or whether the data is guaranteed fresh.

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 action, and every sentence adds value. No redundant or 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?

For a read-only tool with no output schema, the description covers the main purpose, key structure, and return type. Minor gaps like absence of pagination or concurrency info are acceptable for this complexity.

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

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

Schema coverage is only 33% (key described). The description adds context about key namespacing and examples, but does not explain tenantId or workspaceId beyond their names. It partially compensates for the low coverage.

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

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

The description clearly states it reads shared memory for a workspace+tenant, explains the key naming convention with examples, and contrasts with write operations like set. It effectively distinguishes the tool's role among siblings.

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

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

The description provides no guidance on when to use this tool versus alternatives such as chiefmo_gtm_memory_set or chiefmo_gtm_memory_diff. It does not mention prerequisites, limitations, or exclusion criteria.

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

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

No annotations are provided, so the description must disclose behavioral traits. It only states 'write' without discussing idempotency, overwrite behavior, authorization, or side effects. This is insufficient.

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

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

Three sentences, each serving a distinct purpose: definition, caution, and use cases. 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?

With 5 parameters, 40% coverage, and no output schema, the description is incomplete. It does not explain return values, parameter constraints, or what happens on subsequent writes.

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