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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the agent's capabilities ('reason, plan, and build workflows'), multi-turn conversation handling ('pass the session_id... to maintain context'), and integration with the platform ('access to the same planning tools... as the in-app chat'). It doesn't mention rate limits or authentication requirements, but provides substantial operational context.

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 and efficiently organized: it starts with a clear purpose statement, follows with specific usage guidelines in bullet points, adds important behavioral context, and concludes with a concrete example. Every sentence serves a distinct purpose without redundancy or unnecessary elaboration.

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 conversational AI tool with no annotations and no output schema, the description provides substantial context about capabilities, usage patterns, and multi-turn conversation handling. It could be more complete by describing response formats or error conditions, but it adequately covers the tool's primary function and integration within the workflow automation 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 description coverage is 100%, providing solid baseline documentation for both parameters. The description adds minimal parameter-specific information beyond the schema - it mentions session_id for 'multi-turn conversations' and provides an example showing message usage, but doesn't elaborate on parameter constraints or formats. This meets the baseline expectation when schema coverage is complete.

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: 'Send a message to the AgentLed AI agent and get a response.' It specifies the verb ('send a message') and resource ('AgentLed AI agent'), and distinguishes itself from sibling tools by focusing on conversational AI interaction rather than direct workflow manipulation tools like create_workflow or update_workflow.

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 scenarios with a bulleted list: 'Use this tool when you want to: - Build a workflow from a high-level description... - Get recommendations... - Ask questions... - Iterate on workflow design...' It clearly differentiates when to use this conversational tool versus direct workflow manipulation tools among its siblings.

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

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it explains plan-based limits (Pro=2, Teams=10, Custom=50), error behavior ('Returns an error with limit info if the snapshot limit is reached'), and the manual nature of the snapshot. It doesn't mention authentication needs or rate limits, but covers the most critical operational constraints.

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

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

Three sentences with zero waste: first states purpose, second provides usage context, third explains constraints and error handling. Every sentence earns its place by adding distinct value, and the description is appropriately front-loaded with the core functionality.

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

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

For a mutation tool with no annotations and no output schema, the description does well by explaining the 'why' (checkpointing), constraints (plan limits), and error behavior. It could mention what the return value contains (e.g., snapshot ID) or confirmation message, but covers the essential operational context given the complexity.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (workflowId and optional label), meeting the baseline expectation when schema does the heavy lifting.

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

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

The description clearly states the specific action ('Create a manual config snapshot') and resource ('workflow's current state'), distinguishing it from sibling tools like 'list_snapshots' or 'restore_snapshot'. It goes beyond the tool name by explaining what a snapshot represents.

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 this tool ('save a checkpoint before making changes') and why ('so you can restore later if needed'). It also provides guidance on what to do when limits are reached ('delete old snapshots first'), offering clear operational context.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a creation operation but doesn't mention permissions required, whether it's idempotent, what happens on failure, or what the response looks like. For a mutation tool with zero annotation coverage, this is a significant gap.

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 efficiently structured as a single sentence followed by a bulleted list of pipeline components. Each bullet point adds specific value about required/optional fields, with no wasted words. However, it could be more front-loaded by explicitly stating the tool's core purpose before diving into details.

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

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

For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like error handling, permissions, or response format. While it details the pipeline structure, it misses critical context needed for safe and effective use by an AI agent.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('pipeline' and 'locale'). The description adds value by detailing the structure of the 'pipeline' object (e.g., name, steps, context), but doesn't provide additional syntax or format details beyond what the schema implies. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the action ('Create a new workflow') and resource ('from a pipeline definition'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'import_workflow' or 'update_workflow', which also involve workflow creation/modification.

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 like 'import_workflow' or 'update_workflow'. It also doesn't mention prerequisites, such as needing a valid pipeline definition or when this operation is appropriate in a workflow lifecycle.

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 mentions the tool is for deletion, implying it's destructive, but doesn't disclose critical behavioral traits like whether the deletion is permanent, requires specific permissions, has confirmation prompts, or what happens on success/failure. This leaves significant gaps 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 appropriately sized and front-loaded with the core action. Both sentences earn their place: the first explains the tool and how to find IDs, the second provides usage context. There is zero waste or redundancy.

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

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

Given it's a destructive tool with no annotations and no output schema, the description is moderately complete but has gaps. It covers purpose and basic usage but lacks details on behavioral outcomes, error handling, or return values. For a deletion tool, this is adequate but not fully 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 100%, so the schema already documents both parameters ('workflowId' and 'snapshotId'). The description adds minimal value by referencing 'list_snapshots' for finding snapshot IDs, but doesn't provide additional semantics beyond what the schema states. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the verb ('Delete') and resource ('a specific config snapshot'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'delete_workflow' or 'discard_draft', which also perform deletion operations on different resources.

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

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

It provides clear context on when to use this tool ('Use list_snapshots to find snapshot IDs' and 'Useful for freeing up space when the snapshot limit is reached'), which helps guide the agent. However, it doesn't explicitly state when NOT to use it or mention alternatives like 'restore_snapshot' for managing snapshots.

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 irreversible nature ('cannot be undone'), which is a critical behavioral trait for a destructive operation. However, it lacks details on permissions, error conditions, or side effects (e.g., impact on related executions).

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 concise sentences with zero waste. The first sentence states the action and resource, and the second adds crucial behavioral context, making it 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 destructive tool with no annotations and no output schema, the description is minimally adequate. It covers the irreversible nature but lacks details on permissions, response format, or error handling, leaving gaps in context.

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

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

The input schema has 100% description coverage, so the baseline is 3. The description adds no specific parameter semantics beyond what the schema provides ('workflowId'), but since there is only one parameter, the description's focus on the action compensates adequately.

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 ('permanently delete') and the target resource ('a workflow by ID'), making the purpose specific and unambiguous. It distinguishes itself from sibling tools like 'delete_snapshot' by specifying the resource 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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., workflow must exist), exclusions (e.g., cannot delete active workflows), or related tools (e.g., 'discard_draft' for drafts).

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 discards a draft snapshot without affecting the live workflow, which is useful behavioral context. However, it lacks details on permissions needed, error conditions (e.g., if no draft exists), or side effects (e.g., whether the discard is irreversible), leaving gaps for a mutation tool.

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

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

The description is two sentences, front-loaded with the core purpose and followed by usage guidance. Every word contributes value, with no redundancy or fluff, making it highly efficient and 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?

For a single-parameter mutation tool with no annotations and no output schema, the description does well by clarifying the purpose, usage, and effect (live config unchanged). However, it omits details on return values or error handling, which could be important for an agent invoking it. The high schema coverage helps compensate, but some behavioral aspects remain uncovered.

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, with the single parameter 'workflowId' documented as 'The workflow ID'. The description does not add further parameter details, which is acceptable given the high schema coverage. With only one parameter, a baseline of 4 is appropriate as the schema sufficiently explains it.

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

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

The description clearly states the specific action ('Discard the draft snapshot') and resource ('for a live workflow'), distinguishing it from siblings like 'delete_snapshot' (which likely removes a saved snapshot) and 'promote_draft' (which applies draft changes). It explicitly mentions that the live pipeline config remains unchanged, which clarifies the scope of the operation.

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

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

The description provides explicit guidance on when to use this tool: 'Use this to abandon draft changes and go back to the current live version.' This directly contrasts with alternatives like 'promote_draft' (which would apply draft changes) or 'update_workflow' (which might modify the live version), offering clear context for selection.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it returns a 'self-contained WorkflowExport object' with specific contents (steps, context, metadata, pages), strips workspace-specific identifiers for portability, and implies a read-only operation (exporting rather than modifying). However, it doesn't mention error conditions, rate 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 efficiently structured in three sentences: the first states the core purpose and output, the second explains the portability feature, and the third provides usage context. Every sentence adds essential information with zero waste, making it 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?

For a single-parameter tool with no output schema and no annotations, the description does well by explaining the output format ('WorkflowExport object'), contents, and portability behavior. It could be more complete by detailing error cases or response structure, but it covers the essential context given the tool's complexity.

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

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

The input schema has 100% description coverage, with the single parameter 'workflowId' clearly documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating 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 specific action ('Export a workflow as portable JSON') and resource ('workflow'), distinguishing it from siblings like 'import_workflow' (for importing) and 'get_workflow' (for viewing). It explicitly mentions the output format and purpose for cross-environment transfer.

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 guidance on when to use this tool: 'Use this together with import_workflow to move workflows between environments (e.g. sandbox → prod).' It names the alternative tool ('import_workflow') and gives a concrete use case, clearly differentiating it from other workflow-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 are provided, so the description carries the full burden. It mentions the tool returns 'input parameters, output fields, and credit costs', which gives some behavioral insight into the response format. However, it lacks details on permissions, rate limits, error handling, or whether it's a read-only operation (implied by 'Get' but not explicit). For a tool with no annotations, this is a significant gap.

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

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

The description is two sentences, front-loaded with the core purpose and followed by usage guidance. Every sentence earns its place by adding value without redundancy, making it efficient and well-structured.

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

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

Given the tool has no annotations, no output schema, and a simple single parameter with full schema coverage, the description is moderately complete. It explains the purpose and usage but lacks behavioral details like response format specifics or error cases, which are important for a tool that returns schemas.

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, with 'appId' clearly documented as 'The app ID' with examples. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline of 3 for high schema coverage.

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

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

The description clearly states the specific action: 'Get detailed action schemas for a specific app' with verb 'Get' and resource 'detailed action schemas'. It distinguishes from sibling tools like 'list_apps' (which lists apps) and 'get_step_schema' (which might get step schemas), by focusing on app-specific action schemas with details like parameters, output fields, and credit costs.

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

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

The description provides clear context for when to use it: 'Use this to understand exactly what inputs an action needs when building workflow steps.' This implies it's for workflow construction, but it doesn't explicitly state when not to use it or name alternatives among siblings (e.g., 'get_step_schema' might be related).

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 this is a read operation ('Get', 'inspect') and specifies the return data structure (listing specific fields like displayName, logoUrl). However, it lacks details on permissions, rate limits, or error handling, which would be beneficial for a tool with no 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 in the first sentence, followed by return details and usage guidance. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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 (0 parameters, no output schema, no annotations), the description is largely complete: it explains the purpose, return values, and usage context. However, it could be more complete by explicitly mentioning that no parameters are needed or detailing potential errors, though this is minor for such 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?

The tool has 0 parameters with 100% schema description coverage. The description appropriately does not discuss parameters, as none exist. It earns a baseline 4 for not adding unnecessary param info, but not a 5 since it doesn't explicitly state 'no parameters required'—though this is implied by the 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 specific action ('Get') and resource ('workspace's whitelabel branding configuration'), distinguishing it from siblings like 'update_branding' (which modifies) and 'get_workspace' (which retrieves general workspace info). It precisely identifies what is retrieved without being vague or tautological.

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

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

It explicitly states when to use this tool: 'Use this to inspect the current client portal branding before making changes.' This provides clear context for usage (pre-modification inspection) and implicitly distinguishes it from the sibling 'update_branding' as an alternative for making changes.

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 this is a read operation ('Get', 'inspect'), implies it's non-destructive, and describes the return structure ('Returns hasDraft: true/false and the draft config if it exists'). However, it lacks details on permissions, error handling, or rate limits, which are important for a tool with no 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 efficiently structured in three sentences: the first states the purpose, the second provides context, and the third explains the return value. Every sentence adds value without redundancy, making it front-loaded and 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 no annotations, no output schema, and a simple parameter schema, the description is fairly complete: it explains the tool's purpose, usage context, and return structure. However, for a tool with no annotations, it could benefit from more behavioral details like error cases or authentication needs, preventing a perfect score.

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

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

The input schema has 100% description coverage, with the parameter 'workflowId' documented as 'The workflow ID'. The description doesn't add any parameter-specific semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline 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 the tool's purpose: 'Get the draft snapshot for a live workflow' specifies the verb ('Get') and resource ('draft snapshot'), and it explains that drafts are created when updating live workflows. However, it doesn't explicitly differentiate from siblings like 'get_workflow' or 'promote_draft', which would require a 5.

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

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

The description provides clear context for when to use this tool: 'When you update a live workflow, changes go to a draft instead of modifying the live pipeline. Use this to inspect the current draft state.' This gives practical guidance on its purpose. It doesn't explicitly state when not to use it or name alternatives like 'get_workflow', so it falls short of a 5.

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

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

No annotations are provided, so the description carries the full burden. It describes the tool as a read operation ('Get full execution details'), which implies it's non-destructive, but doesn't explicitly state permission requirements, rate limits, or error behaviors. The description adds some behavioral context by explaining the structure of the output ('executionContent field maps stepId -> step output data'), which is valuable given no output 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?

The description is front-loaded with the core purpose in the first sentence, followed by specific usage contexts. Every sentence adds value: the first defines the tool, the second explains output structure, and the third provides usage scenarios. There is no wasted text or redundancy.

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

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

Given no annotations and no output schema, the description does a good job of explaining what the tool returns (execution details with step outputs) and its purpose. However, it lacks details on error cases, pagination, or authentication requirements, which could be relevant for a tool that retrieves execution data. It's mostly complete but has minor gaps.

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

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

Schema description coverage is 100%, so the schema already documents both parameters (workflowId and executionId) adequately. The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or relationships between parameters. This meets the baseline for high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('Get full execution details including results from each completed step') and distinguishes it from siblings like 'list_executions' (which likely lists executions) and 'get_workflow' (which likely gets workflow definitions). It explicitly mentions what the tool retrieves: execution details with step outputs.

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

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

The description provides clear context for when to use this tool: 'to inspect what a workflow produced, debug failures, or check intermediate results.' It doesn't explicitly state when not to use it or name alternatives (e.g., 'list_executions' for summaries), but the context is sufficiently detailed for an agent to infer usage scenarios.

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

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

No annotations are provided, so the description carries full burden. It discloses that it returns 'rows with their full rowData, plus count and totalCount for the list', which adds behavioral context beyond the input schema. However, it lacks details on permissions, rate limits, error handling, or whether it's read-only/safe (though 'fetch' implies read). For a tool with no annotations, this is minimal but not misleading.

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 zero waste: first states purpose and usage, second specifies return values. It's front-loaded with the core action and context, and every sentence adds value (e.g., clarifying output structure). Efficient and well-structured.

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

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

Given 2 parameters with full schema coverage, no annotations, and no output schema, the description is adequate but has gaps. It explains the purpose and output ('rows with full rowData, count, totalCount'), which helps compensate for missing output schema. However, for a data-fetching tool with no annotations, it could better address safety, permissions, or data freshness to be more complete.

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

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

Schema description coverage is 100%, so the schema fully documents both parameters (listKey and limit). The description adds no parameter-specific semantics beyond what's in the schema (e.g., it doesn't elaborate on listKey examples beyond 'investors, deals' or explain limit implications). Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the action ('Fetch sample rows') and resource ('from a knowledge list'), and specifies the purpose is to 'inspect actual data' with 'example payloads from investor/deal lists'. It distinguishes from sibling tools like 'get_knowledge_text' by focusing on row data rather than text content. However, it doesn't explicitly differentiate from 'list_knowledge_lists' which might list metadata vs. actual row data.

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 ('Use this to inspect actual data — see example payloads'), suggesting it's for data exploration rather than operational tasks. It doesn't provide explicit when-not-to-use guidance or name alternatives among siblings (e.g., 'get_knowledge_text' for text content or 'list_knowledge_lists' for metadata). The guidance is helpful but not comprehensive.

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 mentions fetching text entries but does not disclose behavioral traits such as authentication requirements, rate limits, error handling, or whether it's a read-only operation. The description is minimal and misses key operational details needed for safe and effective use.

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

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

The description is two sentences, front-loaded with the core purpose and followed by usage context. It is efficient with no wasted words, though it could be slightly more structured (e.g., separating purpose from examples).

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 incomplete. It lacks information on return values, error cases, permissions, or system behavior. For a tool with one parameter and high schema coverage, the description does not compensate for missing contextual details needed for full understanding.

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

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

The input schema has 100% description coverage, with the 'key' parameter documented. The description adds minimal semantics by specifying that the key is for 'text-type knowledge entry' and examples like 'feedback files, notes, or configuration text', but does not provide additional details beyond what the schema already covers. Baseline is 3 due to high schema coverage.

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

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

The description clearly states the action ('fetch') and resource ('text-type knowledge entry by key'), and specifies the type of content ('text-based knowledge like feedback files, notes, or configuration text'). However, it does not explicitly differentiate from sibling tools like 'get_knowledge_rows' or 'list_knowledge_lists', which might handle similar knowledge data but in different formats or scopes.

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 for accessing text-based knowledge stored in the workspace, but does not provide explicit guidance on when to use this tool versus alternatives (e.g., 'get_knowledge_rows' for non-text entries or 'list_knowledge_lists' for listing). It offers some context but lacks clear exclusions or named alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format ('structured records and a compact text format for prompt injection') and the purpose ('calibrate future scoring runs'), but it does not address potential limitations like rate limits, authentication needs, or data freshness. It adds some context but leaves gaps in behavioral traits.

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

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

The description is appropriately sized and front-loaded, with three sentences that each earn their place: the first states the purpose, the second details the return content, and the third provides usage context. There is no wasted text, and it efficiently conveys essential 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 moderate complexity (fetching historical data with two parameters), no annotations, and no output schema, the description does a good job of covering purpose, return format, and usage context. However, it could be more complete by addressing potential errors, data scope (e.g., time range), or integration details, leaving minor gaps in contextual coverage.

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

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

The schema description coverage is 100%, so the schema already fully documents the two parameters (entityName and limit). The description does not add any additional meaning or syntax details beyond what the schema provides, such as examples of entity names or clarification on the default limit. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the specific action ('Fetch scoring history'), the resource ('entities from the Knowledge Graph'), and the scope ('past scoring decisions with DMF scores and dates'). It distinguishes this tool from siblings by focusing on historical scoring data rather than current operations or workflow management.

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

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

The description provides clear context for when to use this tool ('to see how entities were previously scored and calibrate future scoring runs'), but it does not explicitly mention when not to use it or name specific alternatives among the sibling tools. The guidance is helpful but lacks explicit exclusions.

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

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

No annotations are provided, so the description carries the full burden. It effectively discloses key behavioral traits: it's a read-only operation (implied by 'Get'), returns structured schema data, and warns about automatic stripping of unknown fields on save. However, it doesn't mention potential errors, rate limits, or authentication needs, leaving some 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 front-loaded with the core purpose in the first sentence, followed by usage guidance and a critical warning. Every sentence earns its place: the first defines the tool, the second instructs when to use it, and the third explains a key behavioral consequence. 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 the tool's complexity (simple read operation with 0 parameters) and lack of annotations/output schema, the description is nearly complete. It covers purpose, usage, and a critical behavioral note. However, it could briefly mention the return format (e.g., JSON structure) or error cases for full completeness, as there's no output schema to rely on.

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

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

The tool has 0 parameters with 100% schema description coverage. The description doesn't need to explain parameters, but it correctly implies none are required by not mentioning any. It adds value by clarifying the tool's purpose and output, aligning with the baseline expectation for zero-parameter tools.

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

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

The description clearly states the specific action ('Get the full schema'), the resource ('workflow steps (PipelineStep type)'), and the output content ('field names, types, descriptions, grouped by category'). It distinguishes itself from siblings like 'get_workflow' or 'update_step' by focusing on schema metadata rather than workflow instances or step updates.

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

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

It explicitly states when to use this tool: 'Use this to understand what fields you can set on a step.' It also provides a critical exclusion: 'Unknown fields are automatically stripped on save — only fields listed here are persisted,' guiding users to consult this schema before setting step fields to avoid data loss.

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 that the tool returns detailed data including eventContent, status, metadata, and context, which helps understand the return structure. However, it doesn't mention error conditions, authentication requirements, rate limits, or whether the operation is idempotent, leaving behavioral gaps for a read 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 perfectly concise with two sentences that each serve distinct purposes: the first states the core functionality, and the second provides usage guidance. There's no redundant information, and it's front-loaded with the primary action.

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

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

For a read operation with 3 fully documented parameters and no output schema, the description provides good context about what data is returned and when to use the tool. However, without annotations or output schema, it could benefit from more detail about error cases or response format specifics to be fully complete.

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

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

Schema description coverage is 100%, providing complete parameter documentation in the structured schema. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain parameter relationships or provide examples). With full schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the specific action ('Get a single timeline by ID') and resource ('timeline/step execution record'), distinguishing it from sibling tools like list_timelines (which lists multiple) and get_execution (which focuses on execution-level data). It specifies that this tool retrieves detailed step-level information.

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

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

The description explicitly states when to use this tool ('to inspect a specific step's result in detail'), providing clear context for its application. It implicitly distinguishes from alternatives like list_timelines (for browsing) and get_execution (for broader execution data) by emphasizing single-item detail retrieval.

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 describes what is returned but does not disclose behavioral traits such as permissions needed, rate limits, error handling, or whether it's a read-only operation. The mention of draft info adds some context but is insufficient for a mutation-heavy environment with siblings like 'update_workflow'.

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 appropriately sized with two sentences that are front-loaded with core functionality and add draft-related details without waste. Every sentence earns its place by providing essential 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 complexity (single parameter, no output schema, no annotations), the description is complete enough for a basic read operation but lacks details on output format, error cases, or integration with sibling tools. It covers what is returned but not how to interpret or use the results effectively.

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

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

Schema description coverage is 100%, so the schema already documents the single parameter 'workflowId'. The description does not add meaning beyond what the schema provides (e.g., format or constraints for the ID), meeting the baseline of 3 for high schema coverage.

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

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

The description clearly states the verb ('Get') and resource ('workflow') with specific details about what is returned ('full details including all steps, context, metadata, and configuration'), and distinguishes it from siblings like 'list_workflows' (which likely lists workflows without details) and 'get_draft' (which focuses on drafts).

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 by specifying what details are returned, but does not explicitly state when to use this tool versus alternatives like 'get_draft' or 'list_workflows'. It mentions draft-related info, which hints at context but lacks clear when-to-use or 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what information is returned (company details and knowledge list summaries) but doesn't mention potential limitations like rate limits, authentication requirements, or whether this is a read-only operation. It adequately describes the scope but lacks operational context.

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 perfectly concise with three sentences that each serve a distinct purpose: stating what the tool does, detailing what it returns, and providing usage guidance. There's zero wasted language, and the information is front-loaded appropriately.

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 this is a 0-parameter tool with no annotations and no output schema, the description does a good job explaining what the tool returns and when to use it. However, without an output schema, the description could benefit from more detail about the exact structure of the returned data (beyond just listing categories).

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

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

The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and it focuses on the tool's purpose and usage instead.

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: 'Get workspace company info, offerings, and knowledge schema overview' with specific details about what information is returned. It distinguishes from siblings by focusing on workspace metadata rather than operations like 'get_knowledge_rows' or 'list_workflows', though it doesn't explicitly name alternatives.

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 guidance: 'Use this as a first call to understand what data the workspace has.' This clearly indicates when to use this tool versus alternatives, positioning it as an initial discovery/exploration tool before diving into specific data operations.

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 and adds valuable behavioral details: it runs a 'deterministic import preview,' creates in 'preflight draft mode,' stores an 'imported contract for review/approval,' and explicitly states it 'does NOT auto-apply scaffold.' This covers key operational traits like safety (draft mode) and limitations (no auto-scaffold), though it omits details like error handling or permissions.

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

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

The description is highly concise and well-structured: a clear purpose statement followed by a bulleted list of key behaviors. Each bullet adds essential information without redundancy, making it easy to scan and understand quickly.

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 (import with 4 parameters, nested objects, no output schema) and no annotations, the description is mostly complete. It explains the core behavior and limitations but could benefit from mentioning output format or error cases. However, it adequately covers the import process and draft creation for the 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 description coverage is 100%, so the schema already documents all parameters. The description adds no specific parameter semantics beyond implying 'n8nJson' is the import source and 'workflow' provides metadata overrides. This meets the baseline of 3, as the schema handles the heavy lifting without extra value from the description.

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

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

The description clearly states the specific action ('Create a new Agentled workflow from an n8n JSON import') and distinguishes it from siblings like 'create_workflow' (general creation) and 'import_workflow' (non-n8n import). It specifies the source format (n8n JSON) and target (Agentled workflow), making the purpose unambiguous.

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

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

The description implies usage context by mentioning 'preflight draft mode' and 'does NOT auto-apply scaffold,' suggesting it's for initial import/review rather than immediate deployment. However, it lacks explicit guidance on when to use this versus alternatives like 'preview_n8n_import' (preview only) or 'create_workflow' (manual creation), leaving some 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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: creates new workflows with fresh IDs, recreates associated pages, records import provenance in metadata, and works with WorkflowExport objects. However, it doesn't mention potential side effects like overwriting existing workflows 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 efficiently structured with three focused paragraphs: purpose statement, behavioral details, and usage guidance. Every sentence adds value with zero wasted words. The information is front-loaded with the core purpose stated immediately.

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

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

For a mutation tool with no annotations and no output schema, the description provides good coverage of what the tool does, how to use it, and its relationship to export_workflow. However, it doesn't describe the return value or error conditions, which would be helpful given the absence of an output schema.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds some context by mentioning 'exportJson' should be 'the full WorkflowExport object (from export_workflow)', but doesn't provide additional semantic meaning beyond what's in the schema descriptions. This meets the baseline for high schema coverage.

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

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

The description clearly states the specific action ('Import a workflow from an export JSON'), identifies the resource ('workflow'), and distinguishes it from siblings by specifying it works with 'export_workflow' output and creates new workflows with fresh IDs. It explicitly differentiates from 'import_n8n_workflow' by focusing on native workflow exports.

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 guidance on when to use this tool ('Use this together with export_workflow to move workflows between environments') and offers a practical tip about registering separate MCP servers for different environments. It clearly positions this as a companion to export_workflow for migration purposes.

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 the return format ('app names, descriptions, and action summaries'), which is useful, but lacks details on potential limitations like pagination, rate limits, or authentication requirements. However, it does not contradict any annotations, and for a read-only list tool with zero parameters, this level of transparency is adequate but not comprehensive.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by usage guidance and examples, with no wasted words. Every sentence adds value, such as clarifying the return format and providing practical examples, making it efficient and well-structured for quick understanding.

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 (0 parameters, no output schema, no annotations), the description is mostly complete. It explains what the tool does, when to use it, and what it returns. However, it could improve by mentioning any behavioral traits like response format details or potential errors, but for a basic list tool, this is sufficient to guide an AI agent effectively.

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

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

The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the inputs. The description does not need to add parameter information, and it appropriately focuses on the tool's purpose and usage. A baseline of 4 is applied as it compensates adequately for the lack of parameters by providing clear context.

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

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

The description clearly states the tool's purpose with specific verbs ('List all available apps/integrations') and resources ('in Agentled'), and distinguishes it from siblings like 'get_app_actions' by focusing on discovery rather than detailed action information. It explicitly mentions what is returned ('app names, descriptions, and action summaries'), making the purpose highly specific and well-defined.

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 guidance on when to use this tool ('Use this to discover what integrations are available before building a workflow'), which clearly differentiates it from other tools like 'create_workflow' or 'list_workflows'. It also lists common apps as examples, helping users understand the context and scope of its application 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return fields (execution id, status, timestamps) but lacks critical details: it doesn't specify if this is a read-only operation, whether it requires authentication, any rate limits, pagination behavior beyond the 'nextToken' parameter, or error conditions. For a list tool with 5 parameters, this leaves significant gaps in understanding its 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, efficient sentence with zero waste. It's front-loaded with the core purpose and includes essential return information. Every word earns its place, making it easy to parse quickly.

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 moderate complexity (5 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and return fields but lacks behavioral context (e.g., pagination, auth needs) and usage guidelines. With no output schema, it should ideally describe the response format more thoroughly, but it does state what's returned in a high-level way.

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

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

Schema description coverage is 100%, so the schema fully documents all 5 parameters with descriptions, defaults, and enums. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain relationships between parameters like 'status' filtering with 'limit'). Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate with extra context.

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

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

The description clearly states the tool's purpose with a specific verb ('List') and resource ('recent executions for a workflow'), and specifies what information is returned. It distinguishes this tool from siblings like 'get_execution' (which retrieves a single execution) and 'retry_execution' (which performs an action). However, it doesn't explicitly differentiate from other list tools like 'list_workflows' or 'list_snapshots' beyond the resource 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a workflow ID), exclusions, or comparisons to sibling tools like 'get_execution' for detailed views or 'list_workflows' for broader listings. Usage is implied by the name and parameters but not explicitly stated.

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

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

No annotations are provided, so the description carries the full burden. It describes the tool's behavior by stating it returns detailed information (fields, source type, etc.) and its purpose for discovery, but lacks details on potential limitations like pagination, rate limits, or authentication requirements. The description doesn't contradict annotations (none exist), but could be more comprehensive for a tool with no annotation support.

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 in the first sentence, followed by elaboration and usage guidance. All three sentences add value: the first states what it does, the second details the returned information, and the third explains when to use it. There is no wasted text, making it efficient and well-structured.

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

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

Given the tool has 0 parameters, no annotations, and no output schema, the description does a good job of explaining the tool's purpose, behavior, and usage. It covers what the tool returns and why to use it, but could be more complete by mentioning potential output format or limitations. For a simple list tool with no structured metadata, it's largely adequate.

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

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

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter semantics, but it implicitly confirms no parameters are needed by not mentioning any. This aligns with the schema, earning a baseline score above 3 due to the absence of parameters.

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

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

The description clearly states the tool's purpose with specific verbs ('List all knowledge list schemas') and resources ('knowledge list schemas'), distinguishing it from siblings like 'get_knowledge_rows' or 'get_knowledge_text' by focusing on metadata rather than content. It explicitly mentions what information is returned (field definitions, row counts, metadata) and why to use it (to discover lists and understand structure).

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 guidance on when to use this tool ('to discover what lists exist and understand their structure before querying rows'), implying it should be used as a precursor to tools like 'get_knowledge_rows'. It distinguishes from siblings by focusing on schema discovery rather than data retrieval or other operations, though it doesn't 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?

With no annotations provided, the description carries full burden. It discloses that snapshots are automatically captured before external API updates and can be used for restoration, which adds useful behavioral context. However, it doesn't mention permissions needed, rate limits, pagination, or error conditions, leaving gaps for a read operation 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 efficiently structured in two sentences: the first explains what the tool does and the snapshot capture mechanism, the second specifies the return values. Every sentence adds value with zero waste, making it appropriately sized 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?

For a single-parameter read tool with no output schema, the description provides good context about snapshot purpose and return data. It covers the 'why' (restoration) and 'what' (returned fields), but lacks details on output format structure or behavioral constraints like ordering or limits, which would be helpful for full 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 schema already documents the single 'workflowId' parameter. The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when schema does the heavy lifting.

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

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

The description clearly states the action ('List available config snapshots'), specifies the resource ('for a workflow'), and distinguishes from siblings by focusing on automatic snapshots captured before external API updates. It explicitly mentions what information is returned (snapshot ID, timestamp, changed fields), making the purpose specific and well-defined.

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 when needing to view snapshots for workflow configuration restoration, but doesn't explicitly state when to use this tool versus alternatives like 'restore_snapshot' or 'get_workflow'. It mentions the automatic capture context, which provides some guidance, but lacks explicit when/when-not instructions or named alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what timelines contain (status, output, metadata) and their purpose, but lacks details about permissions, rate limits, error conditions, or pagination behavior beyond what's implied by 'list'. The description doesn't contradict any annotations since none exist.

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 perfectly concise with two sentences that each earn their place. The first sentence defines the tool's core function, while the second provides usage context. There's zero wasted language or redundancy.

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

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

For a read-only listing tool with 5 parameters and no output schema, the description is adequate but has gaps. It explains what timelines are and their purpose, but doesn't describe the return format, pagination details (beyond the nextToken parameter in schema), or error handling. With no annotations and no output schema, more behavioral context would be helpful.

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

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

Schema description coverage is 100%, so the schema already fully documents all 5 parameters. The description doesn't add any parameter-specific information beyond what the schema provides (e.g., it doesn't explain relationships between workflowId and executionId or provide examples). The baseline of 3 is appropriate when the schema does all the work.

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

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

The description clearly states the tool's purpose with specific verbs ('list timelines', 'inspect individual step results', 'debug failures', 'see execution flow') and distinguishes it from siblings like 'get_timeline' (singular) by emphasizing it lists multiple step execution records for a specific execution. It explicitly identifies the resource as 'timelines (step execution records)'.

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

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

The description provides clear context for when to use this tool ('to inspect individual step results, debug failures, or see the execution flow'), but does not explicitly state when not to use it or name alternatives. It implies usage for post-execution analysis rather than real-time monitoring, though this could be more explicit.

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

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

With no annotations, the description carries full burden but provides minimal behavioral context. It mentions the return fields but doesn't cover pagination (implied by 'limit' parameter), ordering, error conditions, authentication needs, or rate limits. 'List all workflows' suggests a read-only operation, but this isn't explicitly stated.

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 concise sentences with zero waste. The first sentence states the action and scope, and the second specifies the return fields, making it 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 annotations and no output schema, the description is incomplete. It covers basic purpose and return fields but lacks details on behavior, error handling, or usage context. For a simple list tool with full schema coverage, it's minimally adequate but leaves gaps in transparency.

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

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

Schema description coverage is 100%, so the schema fully documents both parameters. The description adds no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate coverage without 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 verb ('List') and resource ('all workflows in the workspace'), and specifies the returned fields (id, name, status, goal). It distinguishes from siblings like 'get_workflow' (singular) but doesn't explicitly differentiate from other list tools like 'list_executions' or 'list_snapshots'.

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 is provided. It doesn't mention when to prefer 'list_workflows' over 'get_workflow' for single workflows, or how it relates to other list tools like 'list_executions' for workflow runs.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by explicitly stating this is 'read-only' and 'does not create any workflow,' which clarifies safety and side effects. However, it lacks details on error handling, performance characteristics, rate limits, or authentication requirements that would be valuable for a tool processing JSON imports.

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 efficiently structured: it starts with the core purpose, lists the return components in a clear bullet format, and ends with a crucial behavioral note. Every sentence earns its place, with no wasted words, making it easy to scan and understand.

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 (processing JSON imports with multiple return components) and lack of output schema, the description does a good job outlining what the tool returns. It lists five specific output components, which helps set expectations. However, without annotations or output schema, it could benefit from more detail on error cases or example outputs to be fully complete.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain JSON format requirements or option details). This meets the baseline of 3 when the schema does the heavy lifting.

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

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

The description clearly states the specific action ('Preview a deterministic n8n import from JSON') and the resource involved (n8n workflow). It distinguishes from siblings like 'import_n8n_workflow' by emphasizing this is a read-only preview that doesn't create workflows, making the purpose highly specific and differentiated.

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

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

The description provides clear context for when to use this tool: to preview an n8n import without creating anything. It explicitly states 'This is a read-only preview and does not create any workflow,' which helps differentiate it from actual import tools. However, it doesn't explicitly mention when NOT to use it or name specific alternatives like 'import_n8n_workflow' for comparison.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly describes the destructive action ('overwrites the live pipeline config', 'deletes the draft snapshot') and safety mechanism ('A pre-promote snapshot is saved automatically'). This covers key behavioral traits like mutation effects and rollback capability, though it doesn't address permissions 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 efficiently structured in two sentences: the first states the core action and immediate effects, the second explains the safety mechanism. Every sentence adds critical information with zero wasted words, making it easy to parse and understand quickly.

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

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

For a mutation tool with no annotations and no output schema, the description does well by explaining the destructive behavior and automatic backup. However, it doesn't mention potential error conditions, response format, or prerequisites (e.g., requiring a draft to exist). Given the complexity, it's mostly complete but has minor gaps.

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

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

Schema description coverage is 100%, so the schema already documents the 'workflowId' parameter. The description does not add any parameter-specific information beyond what the schema provides, such as format examples or constraints. The baseline score of 3 is appropriate when the schema handles parameter 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 specific action ('Promote the draft snapshot to live') and resource ('draft snapshot'), distinguishing it from siblings like 'discard_draft' (which deletes without promoting) and 'restore_snapshot' (which restores previous versions). It precisely defines the operation's scope and outcome.

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 when a draft is ready to become live, but does not explicitly state when to use this tool versus alternatives like 'discard_draft' or 'restore_snapshot'. It mentions the automatic pre-promote snapshot for restoration, which provides some contextual guidance but lacks explicit when/when-not directives.

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 behavioral traits like valid status transitions and the effect of setting 'live' to publish a draft, but it lacks details on permissions, error handling, or rate limits. It does not contradict annotations, as there are none.

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 appropriately sized and front-loaded, with three sentences that each earn their place: stating the purpose, listing transitions, and explaining the 'live' status. There is no wasted text, making it 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 the tool's complexity (status transitions), no annotations, and no output schema, the description is fairly complete. It covers key behavioral aspects but could improve by addressing permissions or error scenarios. It effectively uses the high schema coverage to focus on added context.

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

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

The input schema has 100% description coverage, so the baseline is 3. The description adds value by explaining the semantics of the 'status' parameter, clarifying that 'live' publishes a draft workflow, which goes beyond the schema's enum listing. This compensates for the lack of output schema.

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

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

The description clearly states the tool's purpose with a specific verb ('Change the status') and resource ('workflow'), and it distinguishes this from sibling tools like 'start_workflow' or 'update_workflow' by focusing on status transitions rather than execution or content updates.

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

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

The description provides clear context on when to use the tool by listing valid status transitions (e.g., 'created/draft -> live'), which implicitly guides usage. However, it does not explicitly mention when not to use it or name alternatives among siblings, such as 'promote_draft' for draft workflows.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context by specifying what is returned (edges with IDs, relations, scores, metadata) and the graceful handling of an unconfigured Knowledge Graph. However, it lacks details on permissions, rate limits, or potential side effects, which are important for a query 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 appropriately sized and front-loaded, with four concise sentences that each add value: stating the action, specifying returns, providing usage examples, and noting error handling. There is no wasted text, making it efficient and 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 complexity of a query tool with no annotations and no output schema, the description does well by explaining returns and error handling. However, it could improve by detailing output structure or pagination behavior, which would enhance completeness for an agent invoking this 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 schema description coverage is 100%, so the schema already documents all parameters well. The description adds minimal value by implying filtering capabilities ('by entity name and/or relationship type') and example relationship types, but does not provide additional syntax or format details beyond what the schema offers, meeting the baseline for high coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('traverse', 'explore') and resources ('Knowledge Graph edges'), and distinguishes it from siblings by focusing on edge traversal rather than operations like creating workflows or getting knowledge rows. It explicitly mentions what it returns and its graceful handling of unconfigured states.

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

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

The description provides clear context for when to use this tool ('to explore deal relationships, investor-startup connections, and scoring edges'), which helps differentiate it from siblings like 'get_knowledge_rows' or 'get_scoring_history'. However, it does not explicitly state when not to use it or name specific alternatives, keeping it from a score of 5.

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

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

No annotations are provided, so the description carries the full burden. It states the tool will 'revert' workflow attributes, implying a destructive mutation, but lacks details on permissions needed, whether the action is reversible, rate limits, or error conditions. This is a significant gap for a mutation tool with zero annotation coverage.

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

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

The description is two sentences, front-loaded with the main purpose and followed by a prerequisite and detailed scope. Every sentence earns its place with no wasted words, making it efficient and 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 complexity of a destructive restore operation, no annotations, and no output schema, the description is incomplete. It covers the purpose and parameters but lacks behavioral details like side effects, permissions, or response format, which are critical for safe 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 description coverage is 100%, with both parameters clearly documented in the schema. The description adds minimal value by referencing list_snapshots for snapshotId but does not provide additional syntax or format details beyond what the schema already states.

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

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

The description clearly states the specific action ('Restore a workflow to a previous config snapshot') and resource ('workflow'), distinguishing it from siblings like create_snapshot, delete_snapshot, and update_workflow. It specifies what gets restored: 'steps, context, name, description, goal, and style'.

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 guidance to 'Use list_snapshots first to find the snapshot ID,' establishing a prerequisite. However, it does not mention when not to use this tool or alternatives, such as update_workflow for partial changes or discard_draft for reverting drafts.

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 key behaviors: automatic detection of the most recent failed timeline if timelineId is omitted, re-running the failed step, and continuing the workflow from that point. However, it lacks details on permissions, side effects, or error handling.

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, followed by essential behavioral details. Both sentences earn their place by providing critical information without redundancy, making it appropriately sized and well-structured.

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

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

Given no annotations and no output schema, the description covers the tool's purpose, key behavior, and parameter implications adequately for a mutation tool. However, it could be more complete by mentioning potential side effects or response format, though it's sufficient for basic understanding.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by explaining the auto-detection behavior for timelineId ('If no timelineId is provided, the most recent failed timeline is automatically detected'), which clarifies parameter semantics beyond the schema's technical 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 specific action ('retry a failed step') and resource ('workflow execution'), distinguishing it from siblings like 'start_workflow' or 'stop_execution'. It precisely defines the tool's function without being tautological.

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 by mentioning 'failed step' and 'workflow execution', but does not explicitly state when to use this tool versus alternatives like 'restart_workflow' or 'get_execution'. It provides some guidance but lacks explicit comparisons 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 of behavioral disclosure. It mentions the action ('Start a workflow execution') but lacks critical details: it doesn't specify whether this is a read-only or destructive operation, what permissions are required, how errors are handled, or what the execution entails (e.g., asynchronous vs. synchronous). This leaves significant gaps for an agent to understand the tool's 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 highly concise and well-structured: the first sentence states the core purpose, and the second provides a practical example without unnecessary details. Every sentence earns its place by adding clear value, making it easy to parse and understand quickly.

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

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

Given the complexity of starting a workflow execution (a potentially destructive operation with 3 parameters and no output schema), the description is minimally adequate. It covers the basic purpose and parameter usage but lacks details on behavioral aspects like side effects, error handling, or return values, which are crucial for safe and effective use by an AI agent.

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

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

The description adds some value by explaining the 'input' parameter with an example ('if the workflow expects "company_url", pass: { input: { company_url: "https://..." } }'), which clarifies its purpose beyond the schema's generic description. However, with 100% schema description coverage, the schema already documents all parameters adequately, so the description's contribution is marginal, meeting the baseline score.

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 ('Start a workflow execution') and the resource ('workflow'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'retry_execution' or 'stop_execution' which also involve workflow executions, leaving some ambiguity about when this specific tool should be used versus those alternatives.

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

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

The description provides some implied context about when to use the tool ('Optionally provide input data that maps to the workflow's input page fields'), suggesting it's for initial execution. However, it doesn't explicitly contrast with alternatives like 'retry_execution' or mention prerequisites such as needing an existing workflow ID, leaving gaps in comprehensive guidance.

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

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

No annotations are provided, so the description carries the full burden. It discloses the tool's behavioral constraint (only works on specific statuses) but doesn't mention permissions needed, whether the action is reversible, rate limits, or what happens after stopping. For a mutation tool with zero annotation coverage, this leaves significant 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 two sentences with zero waste: the first states the purpose, and the second adds crucial usage constraints. It's front-loaded and every sentence earns its place by providing essential 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 this is a mutation tool with no annotations and no output schema, the description is incomplete. It covers the purpose and basic constraints but lacks details on permissions, side effects, error conditions, or what the tool returns. For a tool that modifies system state, more behavioral context is needed.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('workflowId' and 'executionId'). The description doesn't add any parameter-specific details beyond what the schema provides, such as format examples or relationships between parameters. Baseline 3 is appropriate when the schema handles parameter 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 specific action ('Stop') and target resource ('a running or pending workflow execution'), distinguishing it from sibling tools like 'retry_execution' or 'get_execution'. It precisely defines what the tool does without being tautological.

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

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

It explicitly states when to use this tool ('Only works on executions with status "running" or "pending"'), providing clear context and exclusions. This helps differentiate it from alternatives like 'retry_execution' or tools for other execution states.

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

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

With no annotations provided, the description carries full burden and does well: it explains this is a testing operation that doesn't create workflows or executions (clarifying it's non-destructive), mentions it's for prompt tuning, and provides an example showing how it works. It doesn't mention rate limits or authentication needs, but covers core behavioral aspects adequately.

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?

Perfectly structured: first sentence states purpose, second explains parameter usage, third provides usage context, and fourth gives a concrete example. Every sentence earns its place with zero waste, and key information is 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?

For a tool with 5 parameters, 100% schema coverage, but no output schema, the description does well: it explains the tool's purpose, usage context, and provides an example. It doesn't describe return values (no output schema), but given the example shows expected response structure, it's reasonably complete. Could benefit from mentioning error cases or limitations.

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 some value through the example showing how parameters work together, but doesn't provide additional semantic context beyond what's already in the schema descriptions. The example illustrates parameter usage but doesn't explain semantics 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 specific action: 'Test an AI prompt in isolation without creating a workflow or execution.' It distinguishes from siblings like 'test_app_action' by focusing on AI prompt testing rather than app action testing, and from workflow-related tools by emphasizing isolation from workflow creation.

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: 'Useful for tuning prompts and response structures before adding an AI step to a workflow.' It also implies when not to use (when you want to create workflows or executions) by contrasting with sibling tools like 'create_workflow' and 'start_workflow'.

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 runs actions 'directly' and provides 'results immediately,' which adds behavioral context. However, it lacks details on permissions, rate limits, error handling, or whether it's read-only/destructive. The description doesn't contradict annotations (none exist), but could be more comprehensive for a testing 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 front-loaded with the core purpose, followed by usage guidance and a concrete example. Every sentence adds value—no fluff or repetition. It efficiently communicates key information in three sentences, making it easy to scan and understand.

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 does well on purpose and usage but lacks details on behavioral aspects like error handling or output format. For a tool with 4 parameters and nested objects, it could benefit from more context on what 'results immediately' entails or potential side effects. It's adequate but has gaps in transparency.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description adds minimal semantics: it mentions appId and actionId come from list_apps/get_app_actions and gives an example input format. This provides some context beyond the schema but doesn't deeply explain parameter interactions or constraints.

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: 'Test an app action in isolation without creating a workflow or execution.' It specifies the verb ('test'), resource ('app action'), and distinguishes it from siblings like create_workflow or start_workflow by emphasizing isolation and immediate testing. The example reinforces this specificity.

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

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

The description explicitly states when to use this tool: 'Useful for verifying inputs before wiring a step into a workflow.' It differentiates from alternatives like create_workflow or start_workflow by focusing on pre-workflow testing. The mention of 'without creating a workflow or execution' further clarifies its distinct use case.

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

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

With no annotations provided, the description carries full burden and adds valuable behavioral context: it explains that fields can be partially updated ('any combination'), that null/empty string clears fields, and that 'hideBadge' requires specific plans. However, it doesn't mention mutation effects, permissions needed, 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?

Well-structured with purpose statement, behavioral notes, and organized field list. Slightly verbose in repeating schema descriptions, but every sentence serves a purpose. Could be more concise by referencing schema instead of duplicating field details.

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

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

For a mutation tool with no annotations and no output schema, the description provides good coverage of what the tool does and how to use parameters. Missing details about response format, error handling, and complete permission requirements prevent a perfect score, but it's substantially complete.

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

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description repeats the field definitions almost verbatim from the schema, adding minimal additional semantic value beyond what's already in structured data.

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

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

The description clearly states the specific action ('Update'), target resource ('workspace's whitelabel branding configuration'), and scope ('Set any combination of branding fields'). It distinguishes from the sibling 'get_branding' tool by being the write counterpart to that read operation.

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

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

The description implies usage when needing to modify branding settings, but provides no explicit guidance on when to use this versus alternatives like 'update_workflow' or prerequisites. It mentions a plan requirement for 'hideBadge' but doesn't clarify overall access requirements.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does an excellent job explaining the merge behavior ('Only the specified fields are merged — all other steps and fields remain unchanged'), safety characteristics ('SAFER than update_workflow'), deep-merge behavior for nested objects, and draft snapshot routing for live workflows. The only minor gap is not mentioning error conditions or response format.

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 perfectly structured and efficient. It starts with the core purpose, immediately explains the safety advantage, provides usage guidance with examples, details behavioral characteristics, and ends with draft behavior context. Every sentence adds essential information with zero waste.

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

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

For a mutation tool with no annotations and no output schema, the description does an excellent job covering purpose, usage, safety, merge behavior, and draft routing. It provides sufficient context for an agent to use the tool correctly. The only minor gap is not describing the return value or error conditions, which would be helpful given the absence of an output schema.

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

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

With 100% schema description coverage, the baseline is 3. The description adds meaningful context about how the 'updates' parameter works ('Partial step updates to merge'), provides concrete examples of what can be updated, and explains the deep-merge behavior for nested objects. This significantly enhances understanding beyond what the schema provides.

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

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

The description clearly states the specific action ('Update a single step in a workflow by step ID'), identifies the resource ('step in a workflow'), and explicitly distinguishes it from its sibling 'update_workflow' by explaining it's safer for single-step modifications. This provides perfect clarity about what the tool does and how it differs from alternatives.

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 guidance on when to use this tool ('Use this instead of update_workflow when you only need to change one step') with concrete examples (e.g., 'update a prompt, change inputs, modify entry conditions'). It also specifies the safety advantage over the sibling tool, making the usage context perfectly 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 and excels. It discloses critical behavioral traits: the draft mechanism for live workflows (including the editingDraft response), automatic snapshot creation for rollback, and the distinction between live and non-live workflow updates. This goes beyond basic schema information to explain system behavior.

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

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

The description is front-loaded with the core purpose, followed by important behavioral details in a logical flow. Every sentence earns its place by explaining key usage scenarios and system behavior without redundancy. It's appropriately sized for a complex tool with multiple behavioral nuances.

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 (mutation tool with draft mechanisms) and no annotations/output schema, the description does an excellent job covering behavioral aspects. It explains the update process, draft handling, and related tools. A minor gap is the lack of explicit mention of error conditions or response format details, but it's largely complete for agent use.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by clarifying that updates should be partial ('Provide only the fields you want to change') and listing example fields (name, steps, context, etc.), which enhances understanding beyond the schema's 'Partial pipeline updates' description. However, it doesn't detail the locale 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 specific action ('Update an existing workflow') and resource ('workflow'), distinguishing it from siblings like create_workflow, delete_workflow, and update_step. It provides precise scope by specifying 'Provide only the fields you want to change' and mentions config elements like steps, context, and name.

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

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

The description explicitly states when to use this tool vs alternatives: it explains that for live workflows, edits go to a draft, and directs users to get_draft, promote_draft, or discard_draft for further actions. It also distinguishes from non-live workflows, which are updated directly, and mentions related tools like get_draft.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by detailing the types of errors checked, the return structure, and the ability to validate drafts. However, it does not mention permissions, rate limits, or side effects, leaving some behavioral aspects unclear.

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 and front-loaded with the main purpose, followed by detailed validation checks and usage notes. It is appropriately sized, but the bulleted list of checks, while informative, could be slightly condensed for better conciseness without losing clarity.

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

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

Given the complexity of validation and lack of annotations or output schema, the description does a good job of explaining what the tool does, what it checks, and the return format. However, it could be more complete by including error examples, handling of edge cases, or prerequisites, which would enhance contextual understanding.

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

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

Schema description coverage is 100%, so the schema already documents both parameters (workflowId and pipeline). The description adds context by explaining that the pipeline parameter is for 'validating a draft before saving,' but it does not provide additional semantic details beyond what the schema offers. This meets the baseline for high schema coverage.

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

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

The description clearly states the specific action ('validate a workflow's pipeline definition') and resource ('workflow'), distinguishing it from siblings like create_workflow, update_workflow, or get_workflow. It explicitly mentions what the validation checks for, making the purpose unambiguous and distinct.

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

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

The description provides clear context for when to use the tool ('after creating or updating a workflow' and 'to validate a draft before saving'), but it does not explicitly mention when not to use it or name specific alternatives among siblings. This gives good guidance but lacks exclusion criteria.

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