preset-mcp

Server Quality Checklist

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Latest release: v0.7.2
Disambiguation4/5
Most tools target distinct resources (dashboards, charts, datasets, annotations, etc.), but there is overlap among validation and verification tools (validate_chart vs validate_chart_render vs verify_chart_workflow) that could confuse an agent. The core CRUD tools are well-separated.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., list_dashboards, create_chart, delete_annotation). Even compound tools like validate_chart_render maintain this pattern without mixing styles.
Tool Count2/5
With 59 tools, the server is over-scoped. While Preset is complex, many tools could be consolidated (e.g., multiple validate/verify tools, separate repair tools). Typically a well-scoped MCP server has 3-15 tools; this far exceeds that range.
Completeness3/5
The tool set covers most lifecycle operations for dashboards, charts, datasets, annotations, etc., but lacks delete operations for charts and datasets, which are important gaps. Import/export and repair tools are present, so overall it's decent but incomplete.
Average 3.8/5 across 59 of 59 tools scored. Lowest: 2.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on March 22, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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{
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  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description fails to disclose important behavioral traits such as error handling, authentication requirements, or what happens on overwrite. It only states the high-level action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short but at the cost of omitting essential details. It does not earn its place as a single sentence for a tool with many parameters and nuanced behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema to cover return values, the description is too sparse for a complex tool with 7 parameters and no schema descriptions. It lacks completeness in explaining the tool's operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the tool description does not explain any of the 7 parameters. It adds no value beyond the parameter names and types in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Capture' and the resource 'templates from one or more dashboards' with destination 'into a local folder'. However, it does not differentiate from the closely named sibling 'capture_dashboard_template', missing an opportunity to clarify plural vs singular usage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'capture_dashboard_template'. There are no context hints or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits but only states the action. It does not mention whether the original dashboard is modified, any destructive or read-only implications, authentication requirements, or rate limits. The effect on the existing dashboard and the template creation process remains unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words, but it is too brief given the tool's complexity (six parameters, output schema). It could elaborate on key points without becoming verbose, so it is minimally adequate but not optimally structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (reducing the need to explain return values), the description omits parameter semantics, behavioral details, and usage guidelines. With six parameters and a sibling tool for similar functionality, the description is incomplete and fails to provide sufficient context for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not explain any of the six parameters (dashboard_id, portable, include_query_context, include_dataset_schema, output_path, response_mode). Since schema description coverage is 0%, the description must compensate but adds no parameter guidance, leaving the agent to infer from names and defaults alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool captures a reusable dashboard+chart template from an existing dashboard, using the specific verb 'capture' and resource 'dashboard+chart template'. However, it does not differentiate from the sibling tool 'capture_golden_templates', which likely serves a similar purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'capture_golden_templates', 'export_dashboard', or other creation tools. It lacks any context for appropriate usage scenarios or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It only states that it imports and reports IDs, but does not disclose whether the import is destructive, how conflicts are handled, or any side effects like overwriting existing dashboards.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence. However, it sacrifices essential detail for brevity; more information is needed to be truly useful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of 4 parameters and an output schema, the description is insufficient. It does not explain the purpose of boolean parameters or the format of the output, leaving the agent without key context for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 4 parameters (import_path, overwrite, repair_duplicate_layout, verify_after_import) with 0% schema description coverage. The description provides no information about any parameter's meaning, format, or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Import') and the resource ('dashboard ZIP bundle'), and mentions the output ('report the affected dashboard IDs'). It is specific enough to understand the tool's primary function, though it does not differentiate from sibling tools like create_dashboard.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., create_dashboard vs import). No prerequisites, context, or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states snapshots are 'locally saved' and 'captured before mutations,' hinting at a safe, read-only operation, but it does not clarify authorization needs, side effects, or data freshness. The minimal information leaves significant behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, but it sacrifices essential detail for brevity. While there is no fluff, the structure fails to front-load key information such as parameter usage or result behavior. The conciseness is acceptable but comes at the cost of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 2 parameters, no annotations, and an output schema, the description should provide more context. It lacks explanations of parameter roles, usage scenarios, and behavioral traits. The presence of an output schema mitigates return-value documentation, but the description remains incomplete for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, requiring the description to explain parameter meanings. However, the description does not mention dashboard_id or limit. Without any parameter semantics, the agent cannot infer how to filter or paginate results, making the tool nearly unusable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists 'locally saved dashboard snapshots captured before mutations.' It identifies the resource (dashboard snapshots) and the action (list). While it distinguishes from sibling tools like list_dashboards, the phrase 'captured before mutations' may be ambiguous without additional context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives. Sibling tools like restore_dashboard_snapshot exist for restoring snapshots, but the description does not clarify when listing is appropriate. This omission limits the agent's ability to choose correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description must carry behavioral disclosure. It does not state whether the tool is read-only or has side effects, how it interacts with the dashboard, or what constitutes a validation failure. The term 'validate frontend render status' is vague.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (10 words) and under-informative. While concise, it lacks substance and does not earn its place by adding value beyond the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema and multiple parameters, the description is grossly incomplete. It does not explain what the tool returns, the effect of timeout/settle/response_mode, or how to interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% with no parameter descriptions. The description fails to mention any of the four parameters (dashboard_id, timeout_ms, settle_ms, response_mode), leaving the agent without guidance on required inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool validates frontend render status for all charts on a dashboard. It uses a specific verb and resource, and distinguishes from sibling tools like validate_chart_render (single chart) and validate_dashboard (structural validation).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. With many sibling tools, such as validate_chart_render or validate_dashboard, the description should specify scenarios (e.g., after dashboard creation/update) or differentiate criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must fully disclose side effects, permissions, and behavior. It only mentions 'frontend-level probe', implying a read-only validation, but does not explicitly state that it has no destructive effects, performance impact, or failure modes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely brief (one sentence), which ensures conciseness but sacrifices necessary detail. It is not structured to front-load key information like side effects or output format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters (1 required), an output schema, and no annotations, the description is insufficient for an agent to understand full behavior, parameter interactions, and expected return values. It lacks completeness for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no additional meaning to the parameters. While parameter names are somewhat self-explanatory, the tool would benefit from explaining the role of timeout_ms, settle_ms, and response_mode beyond defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states a specific action: validate chart rendering using a headless browser. However, it does not differentiate from sibling tools like validate_chart or validate_dashboard_render, leaving ambiguity about the exact scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this tool versus alternatives such as validate_chart or validate_dashboard_render. The description lacks context for appropriate invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions 'run checks' but does not disclose if the tool is read-only, if it modifies state, or what side effects occur. This is insufficient for an agent to understand behavioral implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise but too minimal for a tool with 8 parameters and an output schema. It sacrifices necessary detail for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the high parameter count and zero schema description coverage, the description is incomplete. The presence of an output schema may mitigate some return value concerns, but the description lacks essential guidance for parameter usage and tool behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only hints at chart_id, dashboard_id, and include_render, ignoring row_limit, force, timeout_ms, settle_ms, and response_mode. Most parameters are unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Run end-to-end checks' and the resource 'chart query/render and optional dashboard context', which distinguishes it from sibling tools like validate_chart. However, it does not specify what 'end-to-end' entails, leaving some ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus sibling tools such as validate_chart, validate_chart_render, or verify_dashboard_workflow. The description lacks context for appropriate usage scenarios or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It states it runs verification but does not clarify if it is read-only, whether it modifies state, or any side effects. It also lacks details on rate limits, authentication needs, or error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 10 words, which is concise but too brief for a tool with 7 parameters. It front-loads the main action but sacrifices necessary detail. It could be expanded to include key parameter context without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 7 parameters, no parameter descriptions, no annotations, and an output schema (though not shown), the one-sentence description is insufficient. It does not explain verification levels, response modes, or how parameters affect behavior. The description is incomplete for an AI agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must add meaning to parameters. It mentions 'structure + query + optional render', which loosely maps to 'include_render', but does not explain other parameters like 'row_limit', 'force', 'timeout_ms', 'settle_ms', or 'response_mode'. The 0% coverage requires more comprehensive parameter guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool runs structure, query, and optional render verification on a dashboard. It specifies the verb 'run' and the resource 'verification for a dashboard'. This distinguishes it from siblings like 'verify_dashboard_structure' (structure-only) and 'validate_dashboard' (validation, not verification). However, it could be more explicit about the scope and output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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, context, or exclusion criteria. For example, it doesn't specify whether to use this instead of 'validate_dashboard' or 'verify_dashboard_structure'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavioral traits. It identifies the action as destructive but does not elaborate on cascading effects, reversibility, or permission requirements, which are critical for a delete operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, making it concise, but it sacrifices completeness. It is front-loaded with the action but omits important details, striking a balance between brevity and clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite an output schema existing, the description does not explain return values or error scenarios. For a destructive tool, this lack of completeness hinders an agent's ability to handle responses correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides no information about the two parameters (dashboard_id, dry_run). Given 0% schema coverage, the agent must infer meaning solely from parameter names, which is insufficient for confident use.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Delete') and resource ('a dashboard'), and adds a prerequisite ('after exporting a full backup'). This distinguishes it from other delete tools and informs the agent of a required precondition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying the backup prerequisite, but it does not explicitly state when to use this tool vs alternatives (e.g., archival or disable actions) nor 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only says 'validate', which implies read-only, but doesn't confirm lack of side effects, permissions needed, or whether it returns a pass/fail result.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence is efficient and front-loaded with key verbs and nouns. Could potentially add more context without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description omits what the tool returns (list of issues, status, etc.) and doesn't clarify the meaning of 'layout graph integrity' or 'chart reference health'. Insufficient for a validation tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage and description adds no parameter explanation. response_mode enum values are not described; dashboard_id is obvious but not explained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool validates dashboard layout graph integrity and chart reference health, distinguishing it from broader validation tools like validate_dashboard and repair tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs siblings like validate_dashboard or repair_dashboard_chart_refs. Agent has to infer from tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries full burden for behavioral disclosure. It states the removal action but does not reveal whether the operation is destructive, requires permissions, or what happens to other dashboard elements. The dry_run parameter is not explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no redundant words. However, it is overly brief and could be restructured to include critical details like parameter usage and output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, output schema exists), the description is incomplete. It does not mention output schema contents (e.g., success message, count of duplicates removed) or the effect of dry_run, leaving gaps for safe and correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds no extra meaning to the parameters. It does not explain the purpose of 'dry_run' (e.g., preview vs. actual removal) or provide any context beyond parameter names and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Remove duplicate chart placements') and the target resource ('dashboard layout'). It is specific and distinguishes from sibling tools like 'repair_dashboard_chart_refs' which handles chart references rather than layout duplicates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, how to interpret the dry_run parameter, or what to do after removal. The description lacks context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must bear full burden. It does not disclose that restore is destructive (overwrites current state), whether it merges or replaces, permissions needed, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence with 8 words is concise but overly minimal for a 5-parameter restore tool. It could be expanded without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a restore operation (5 parameters, output schema), the description is insufficient. It omits dry_run behavior, restore_json_metadata purpose, and id mismatch handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate but only hints at snapshot_path. Five parameters exist, including boolean flags with defaults, but no explanation of their meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Restore', the resource 'dashboard layout/settings', and the source 'local snapshot JSON file'. It distinguishes from import/export tools and snapshot listing tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool vs alternatives (e.g., import_dashboard), prerequisites, or exclusions. The description is minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 parameters but does not disclose behavioral traits like whether updates are destructive, permission requirements, or what happens on execution. The 'dry_run' parameter hints at a preview, but the behavior of the actual update is not explained.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, using only four lines for parameters and a clear one-line purpose. Every sentence serves a purpose without any fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, the description does not need to explain return values. However, the tool is a mutation with no annotations, so more behavioral context (e.g., 'This modifies the layer in place') would improve completeness. For a simple update, it is minimally adequate but leaves gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning for each parameter beyond the schema's property names, explaining 'New name for the layer' and 'New description'. However, it is essentially a paraphrase of the schema. With 0% schema description coverage, the description is necessary but minimal; it could add constraints (e.g., length limits) or formatting details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Update an existing annotation layer' which is a specific verb and resource. However, it does not differentiate from sibling tools like 'create_annotation_layer' or 'delete_annotation_layer', but the verb 'update' is distinct enough among the list of siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites or conditions, such as that the layer must exist or that 'create_annotation_layer' should be used for new layers.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as whether the tool is read-only, modifies data, requires authentication, or how it handles errors. The term 'aggregate statuses' is vague without explaining what statuses mean or how they are computed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is front-loaded with the key verb and resource, containing no unnecessary words. It efficiently conveys the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description is too brief. It does not explain validation behavior, interpretation of results, or edge cases. For a tool with 4 parameters and no annotations, the description should provide more context on when to use different parameter configurations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 4 parameters (dashboard_id, row_limit, force, response_mode) with 0% schema description coverage. The description does not explain any parameter's purpose or behavior, leaving the agent to guess. For example, 'force' and 'response_mode' are not mentioned, nor is the impact of row_limit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool validates all charts on a dashboard and returns aggregate statuses, specifying the verb (validate), resource (all charts on a dashboard), and outcome. This distinguishes it from sibling tools like validate_chart (single chart) and validate_dashboard_render (render-specific).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for validating all charts on a dashboard, but it does not explicitly mention when not to use it or suggest alternatives. While the sibling list provides context (e.g., validate_chart for single charts), the description itself lacks guidance on prerequisites or decomposition strategies.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It fails to mention whether the operation is read-only or destructive, what side effects occur (e.g., file creation), or the nature of the output path parameter (local vs remote). The optional 'output_path' with default null implies return in response but is not clarified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence of 10 words, front-loaded with verb and resource. No superfluous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While an output schema likely explains return values, the description omits key context such as expected response format, file size limits, or permission requirements. For a 2-parameter tool with an output schema, the description is minimally adequate but lacks completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to the parameters. 'dashboard_id' and 'output_path' are self-explanatory but lack details on format, behavior when null, or valid values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Export'), resource ('dashboard'), output format ('ZIP bundle'), and purpose ('backup or migration'). It effectively distinguishes from siblings like 'import_dashboard' (inverse) and 'capture_dashboard_template' (template-specific).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives such as 'capture_dashboard_template' or 'import_dashboard'. No prerequisites, limitations, or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It only says 'get detail' and lists parameters, but does not disclose whether the operation is read-only, requires permissions, or has any side effects. This is insufficient for a mutation-free tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (3 lines) and front-loaded with the primary purpose. It wastes no words, but the parameter list could be integrated more seamlessly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool, the description covers the main function (get detail + annotations). An output schema exists to document return values. However, it omits context like response_mode semantics or typical use patterns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds brief meaning for layer_id ('the annotation layer ID') and enumerates response_mode options, which the schema only provides as types/enums without descriptions. However, it does not explain what the different response modes return in terms of detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'get' and the resource 'annotation layer', including that it returns annotations. This distinguishes it from siblings like list_annotation_layers (list all) and update/delete (modify).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like list_annotation_layers (for browsing) or get_dashboard (for different resource). The usage context is only implied by the parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It does not mention whether results are paginated, if the operation is read-only, or any side effects. The description only states the basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at about 100 words, with a clear opening sentence, a brief definition of annotation layers, and an Args section. Every sentence serves a purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has an output schema (not shown), so the description need not detail return values. However, it could mention that the output varies by response_mode. Overall, it covers the essential context for usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description adds significant value by explaining each parameter: response_mode's enum options (compact, standard, full) with their meanings, and name_contains as a case-insensitive substring filter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists annotation layers in the current workspace, explaining what annotation layers are and that the tool helps discover layer IDs. This distinguishes it from the sibling get_annotation_layer, though not explicitly mentioned.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description notes 'Use this to discover layer IDs,' implying the use case, but does not contrast with alternatives like get_annotation_layer or specify when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description implies read-only behavior by mentioning 'Get' and return values. However, it does not explicitly state safety or permissions needed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is short (3 sentences) with no fluff. First sentence states purpose, second explains return value, third repeats parameter name. Efficient but could omit the Args line if schema covers it.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool with one parameter and existing output schema, the description adequately explains the return value. No mention of errors but sufficient for basic usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the tool description only repeats 'The dashboard ID' which adds no new meaning beyond the schema's type and required flag.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb 'Get' and resource 'embedded configuration', clearly distinguishing from siblings like enable_embedded_dashboard or describe_dashboard by focusing on embedding state.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. Siblings like disable_embedded_dashboard or enable_embedded_dashboard exist but no comparison provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must disclose behavioral traits. It mentions the dry_run parameter for preview, but lacks details on permissions, overwrite behavior, or error states.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a front-loaded purpose statement followed by a structured parameter list. No redundant information, though slightly longer due to parameter docs.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers parameter semantics well and the output schema exists, so return values are unneeded. However, lacks usage context and behavioral details, making it minimally adequate for a 6-parameter mutating tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description explains each parameter's purpose (e.g., 'New label (name) for the query') and the dry_run preview behavior, adding meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Update an existing saved query,' with a specific verb and resource. It distinguishes from sibling tools like create_saved_query and delete_saved_query.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. No prerequisites, exclusions, or context for usage are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behavioral traits. It does not mention whether deletion is permanent, reversible, requires permissions, or affects related entities. The dry_run parameter hints at preview capability but the core action's consequences are under-described.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short, structured with a clear definition and a list of parameters. Every sentence adds value; no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, return values need not be explained. However, for a deletion tool, additional context like cascading effects or soft-delete behavior would improve completeness. The description is adequate but not comprehensive given the lack of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description adds meaningful explanations: template_id is identified as the ID to delete, and dry_run is explained as previewing without execution. This provides value beyond the schema's type/required fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Delete a CSS template,' which is specific and distinguishes from sibling tools like create_css_template, update_css_template, get_css_template, and list_css_templates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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, prerequisites, or cautionary notes. The description only states the action, not the context for deletion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. It mentions 'recent local mutation-journal entries' implying a time window and locality, but lacks explicit disclosure about read-only nature or other behavioral traits such as authorization 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded with the key verb and resource. It is efficient but could benefit from additional detail without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the moderate complexity (4 optional parameters with an enum) and 0% schema coverage, the description is incomplete. It omits crucial filtering semantics and the limit default, though an output schema exists for return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description adds no explanation of the parameters (resource_type, resource_id, tool_name, limit). The agent cannot infer filtering capabilities or the default limit from the description alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List'), the resource ('recent local mutation-journal entries'), and the purpose ('for incident debugging'). It is specific and distinct from sibling tools, which are mostly CRUD on other resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for incident debugging but does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention any conditions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the dry_run parameter for previewing, but lacks details on side effects, authorization needs, rate limits, or success/error responses. The output schema covers return values, but the description adds minimal behavioral context beyond the creation action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: one sentence for purpose followed by a simple bullet-like parameter list. Every sentence adds value, and the purpose is front-loaded. No irrelevant information is included.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a creation tool with three parameters and an output schema, the description covers purpose and parameter semantics. However, it misses context like template name uniqueness, CSS validation rules, or permissions. It also does not differentiate from sibling creation tools like capture_dashboard_template.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the parameter descriptions in the tool description are essential. They provide clear, if brief, explanations for all three parameters: template_name, css, and dry_run. This adds meaning beyond the bare schema definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Create a new CSS template for dashboard styling,' using a specific verb and resource. This distinguishes it from sibling tools like update_css_template, delete_css_template, and other create_* tools that target different resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as capture_dashboard_template or update_css_template. The description only lists parameters without explaining prerequisites or scenarios where creation is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It explains dry_run for validation and allow_empty_layout flag, but does not explicitly state that updates are permanent or mention side effects like orphaned containers. Adequate but could be more explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with a concise opening sentence followed by a clear Args list. Length is appropriate for the parameter count, but some redundancy (e.g., 'default: False' repeated) could be trimmed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 params, no annotations, and an output schema (not shown), the description covers all necessary context: prerequisite tools, parameter details, and usage scenarios. Complete enough for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description compensates by explaining each param: dashboard_id, dashboard_title, published, position_json (with layout context), json_metadata, allow_empty_layout, dry_run. Adds significant meaning beyond raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Update an existing dashboard's properties' and lists key properties, distinguishing it from create/delete tools. However, it does not explicitly differentiate from repair or validate tools, which are siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using list_dashboards to find the ID and get_dashboard to inspect current state, providing clear prerequisite context. It does not mention when not to use or alternatives, but this is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description discloses key behaviors: does not take a screenshot, executes same query context, dashboard_id speeds lookup. Covers the main behavioral aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with purpose, efficient use of bullet points and notes. No wasted words. Could be slightly more concise but overall well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given output schema exists, description doesn't need to explain return values. Covers validation concept, parameter meanings, and special notes. Adequate for the tool's complexity, though missing some details on error handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must compensate. It provides brief explanations for all 5 parameters, including enum values for response_mode. Adds meaning beyond the schema, though could be more detailed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it validates chart query and returns render status, with specific verb and resource. Does not explicitly differentiate from sibling tools like validate_chart_render, but the purpose is clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides context on when to use (validate chart params) and notes about dashboard_id, but lacks explicit guidance on when not to use or alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the action (revoke, prevent embedding) and the dry_run behavior. However, it does not mention whether the action is reversible, required permissions, or other side effects. Since no annotations are provided, the description carries full burden and could be more detailed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is lean, with two short paragraphs and a structured Args section. Every sentence adds value, and the purpose is front-loaded. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the description covers the core action and parameters, it lacks information about reversibility, authorization, or return values. With an output schema present, return values are covered, but behavioral completeness is moderate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains both parameters: dashboard_id as the target and dry_run as a preview option. This adds clear meaning beyond the schema, which only provides types and defaults. With 0% schema coverage, the description compensates well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'disable' and the resource 'embedded dashboard', and explains the effect ('revokes the embed UUID', 'prevents embedding'). It distinguishes from siblings like enable_embedded_dashboard.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use or not use this tool, nor does it compare with alternative tools like enable_embedded_dashboard or get_embedded_dashboard. Usage is implied but not guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses dry_run behavior but omits side effects (creates resource), auth requirements, and conflict handling. Missing expected write operation transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise with front-loaded purpose. Includes structured Args section. Minor inefficiency in repeating parameter descriptions that could be integrated, but overall clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given output schema exists (return values not needed), description covers creation and parameters. Missing mention of persistence or link to retrieval tools, but acceptable with sibling context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the 'Args' section fully explains each parameter's purpose (label, sql, database_id, schema, description, dry_run), compensating completely.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb+resource: 'create' and 'saved SQL query'. Distinguishes from siblings like 'run_sql' (execute) and 'delete_saved_query' (delete).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
States that saved queries persist reusable SQL snippets, implying use for saving SQL. No explicit when-not-to-use, but context and sibling names provide sufficient differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It mentions polling for results but does not describe what happens if the query is still running, timeout behavior, error handling, or whether the tool blocks. This is insufficient for a polling tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: a one-line summary, then a clear explanation of the async query flow, and a separate Args section for the parameter. Every sentence adds value without fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter and an output schema exists, the description covers purpose and parameter semantics adequately. However, it could be more complete by detailing polling behavior (e.g., retry logic, timeout).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds meaning by explaining that query_id is the key returned by an async SQL Lab query. It provides context beyond the schema's type and required flag. However, it could be more detailed about format or source.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches results of an async SQL query by its query ID. It uses a specific verb 'Fetch results' and resource 'async SQL query', and the context distinguishes it from synchronous query tools like run_sql.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains that this tool is used after an async SQL Lab query returns a query ID, and to poll for results once the query completes. While it gives clear context for using the tool, it does not explicitly state when not to use it or name alternatives, but the sibling tool list provides that context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility. The description states a read operation ('get') but does not explicitly confirm non-destructiveness or disclose potential behaviors like error handling, authorization needs, or side effects. The output schema helps but transparency is minimal beyond the name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one sentence plus two parameter lines—no redundancy, front-loaded with purpose. Every sentence contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema covering return values, the description covers the essential purpose and parameters. It lacks mention of potential errors (e.g., invalid query_id) or dependencies on other tools, but these are minor omissions for a simple getter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning by explaining query_id as 'The saved query ID' and enumerating response_mode options ('compact', 'standard', 'full'). This compensates for the schema having no parameter descriptions (0% coverage). However, it could elaborate on differences between response modes.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 detail for a single saved query.' The verb 'get' and resource 'detail for a single saved query' are specific and distinguish from siblings like list_saved_queries (list overview) and create/update/delete operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving details of a specific saved query but offers no explicit guidance on when to use this tool versus alternatives (e.g., list_saved_queries) or when not to use it. No prerequisites or context are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the 'dry_run' parameter for previewing behavior but does not disclose any destructive implications, auth requirements, or side effects. This is insufficient for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with the primary purpose, and includes a structured Args list. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (though not shown), the description adequately covers the creation action. It mentions the dry_run option and links to the next step. Missing details like error handling or response format are minor given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no property descriptions (0% coverage), but the tool description adds detailed explanations for all three parameters in the Args block, including purpose and defaults. This greatly compensates for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Create a new annotation layer' and explains its purpose: grouping time-based annotations for time-series charts. It distinguishes from the sibling 'create_annotation' by noting that individual annotations are added later.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a sequential usage hint: 'After creating a layer, use create_annotation to add individual annotations.' This implies when to use this tool. However, it does not explicitly mention prerequisites or when not to use alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It states the destructive nature and cascading deletion of all annotations. It also mentions the dry_run preview, which adds transparency. However, it does not mention irreversibility or permission requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with one sentence for the action and a brief arg list. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the action and input parameters well. It does not detail the output, but an output schema exists to cover that. Minor gaps exist (e.g., behavior on non-existent layer) but are acceptable for a delete tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description provides clear explanations for both parameters: layer_id and dry_run. This adds essential meaning beyond the schema's type definitions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool deletes an annotation layer and all its annotations, using a specific verb and resource. It distinguishes itself from the sibling delete_annotation tool, which deletes a single annotation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for removing an entire layer but lacks explicit guidance on when to use vs. alternatives like delete_annotation. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description takes full burden. It discloses the destructive action and preview via dry_run, but omits details on permanence, required permissions, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: one sentence for purpose, then structured parameter descriptions under 'Args:'. No redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers action and parameters adequately for a simple tool with an output schema. It could mention if deletion is permanent or requires confirmation, but overall it is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates by explaining both parameters: 'query_id: The saved query ID to delete.' and 'dry_run: If True, preview the action without executing.' This adds essential meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Delete a saved query,' using a specific verb and resource. This clearly distinguishes it from sibling tools like create_saved_query, update_saved_query, and get_saved_query.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for deletion but does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. It does not disclose behavioral traits such as whether the operation is read-only, idempotent, what errors might occur, or any rate limits. It only describes the return data, not the behavior or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is succinct and well-structured: a two-sentence summary, a bullet list of return fields, a usage note, and a clear args section. Every sentence serves a purpose, with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple retrieval tool with two parameters and an output schema, the description covers the return values, usage, and parameters adequately. However, it lacks information about error handling or prerequisites beyond 'list_charts first,' which could be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description adds significant meaning: it describes chart_id as 'Numeric chart ID' and explains response_mode values ('compact', 'standard', 'full') with what each returns, including a default of 'full'. This fully compensates for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool retrieves details for a single chart, listing specific return fields (visualization type, parameters, query_context, datasource info, other metadata). It also mentions to use list_charts first, distinguishing it from sibling tools like list_charts, create_chart, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a clear usage hint: 'Use list_charts first to find valid IDs.' Explains the three response modes. However, does not explicitly state when not to use this tool or mention alternatives besides list_charts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 'Get detail' which implies a read operation, but does not explicitly declare it as read-only or safe. It also lacks any mention of permissions or side effects. The description is adequate but could be more transparent about the operation's nature.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, starting with the purpose followed by parameter explanations. It is well-structured and easy to read, but could be slightly improved by using a more formal format or listing parameters as bullet points.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that it is a simple read tool with two parameters and an existing output schema, the description covers the core functionality and parameter meanings. It lacks explicit mention of return behavior, but that is likely documented in the output schema. Overall, it is complete for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema properties have no descriptions. The tool description adds meaning by specifying 'The CSS template ID' for template_id and listing the possible response_mode values. This compensates for the missing schema descriptions, though it could be more detailed (e.g., explaining each response mode).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get detail for a single CSS template,' which is a specific verb+resource combination. It distinguishes from sibling tools like list_css_templates (which lists all) and create/update/delete (which modify).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
While no explicit when-to-use or alternatives are mentioned, the description makes it clear that this tool is for fetching details of a specific template by ID, implying it should be used when you have a template_id and need full details. However, missing explicit exclusion of list_css_templates for browsing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions that dashboards can contain large blobs and explains response_mode, but lacks details on error handling, permissions, or side effects. Adequate but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded: first sentence states purpose, then usage tip, then parameter details. No unnecessary words, though slightly repetitive in listing modes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (not shown) and few parameters, the description provides sufficient context: prerequisite, parameter details, and verbosity control. It doesn't need to detail return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema coverage, the description adds meaning beyond the schema: dashboard_id is numeric, and response_mode enum values are explained in context (compact, standard, full) with behavioral implications.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get detail for a single dashboard,' specifying the action and resource. It distinguishes from sibling tools like list_dashboards and other get_* tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises to 'Use list_dashboards first to find valid IDs,' which is explicit prerequisite guidance. It also explains the response_mode options to control verbosity, helping the agent decide when to use the tool appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the burden of behavioral disclosure. It explains the syncing of position_json and json_metadata.chartsInScope, and details the two strategies and dry_run option. However, it does not explicitly state the mutability or reversibility of the operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured. The main purpose is stated in the first sentence, followed by a brief explanation of what it syncs, and a clear list of parameters with their meanings.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description adequately covers the inputs but does not describe the output or return value. Since an output schema exists (as per context signals), the description is not required to explain it, but additional context on edge cases (e.g., invalid dashboard_id) would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides clear explanations for all three parameters: dashboard_id as required, strategy with both enum values explained ('replace_by_name' maps stale IDs to matching names, 'remove_orphans' drops stale refs), and dry_run as a preview mechanism.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 ('repair') and resource ('stale chart IDs in a dashboard's layout metadata'). It is distinguishable from the sibling tool 'repair_dashboard_layout_duplicates' which targets duplicate layout entries, not chart references.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the two strategies but does not provide guidance on when to use this tool versus alternatives, nor does it mention prerequisites or conditions for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry full burden. It describes what the tool returns but does not disclose that it is read-only or idempotent, nor mention any side effects 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise (2 sentences plus well-organized Args) with no redundant information. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description does not need to detail return values. It covers purpose, parameters, and usage adequately, though could mention that the tool is safe to call.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has no descriptions (0% coverage), but the description's Args section adds meaningful detail for each parameter, including types, defaults, and enum options. This compensates well for the schema gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states purpose: 'Return a normalized dashboard summary in one call', and lists specific contents (metadata, markdown blocks, chart inventory, dataset inventory, lineage). This distinguishes it from siblings like get_dashboard.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description advises using get_dashboard to find dashboard_id, and explains parameters including response_mode options. However, it does not explicitly contrast with siblings or state when this tool should be used over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It does not mention side effects, permissions, rate limits, pagination, or any constraints beyond listing. The description is minimal in this regard.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear summary, a brief explanatory paragraph, and an Args section. It is front-loaded and concise, though the Args section could be slightly more compact without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool with 2 optional parameters and an output schema, the description is fairly complete. It explains parameters well but does not mention pagination or default result limits, which would be helpful for a listing operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description thoroughly explains both parameters. It details the three enum values for response_mode and describes name_contains as a case-insensitive substring filter, adding significant meaning beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('list') and resource ('CSS templates in the current workspace'). It distinguishes itself from sibling tools like get_css_template and create_css_template by explaining its role in the workflow (discovering template IDs).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance: 'Use this to discover template IDs, then pass one to get_css_template for full detail.' This tells the agent when to use this tool and when to use an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description adequately explains filtering behavior (case-insensitive, exact-match, server-side). However, it lacks details on pagination, authentication, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, starting with a summary and then listing parameters. It could be slightly more compact, but it earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's core functionality and parameters, and since an output schema exists, return values need not be explained. It could mention pagination but is otherwise complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given 0% schema description coverage, the description fully compensates by explaining each parameter's purpose and filtering behavior in detail, adding significant value beyond the schema's type and default information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists datasets in the current workspace, but it does not explicitly differentiate from similar list tools like list_charts or list_dashboards.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides useful guidance by mentioning list_databases for creating datasets, but it does not specify when not to use this tool or alternative approaches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the dry_run parameter for previewing without executing, which adds transparency. However, it does not disclose other behavioral traits such as permissions needed, whether mutations are reversible, or any side effects beyond creation. Adequate but could be more thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficient: a one-sentence purpose, a brief contextual note, and a clear arg list. Every sentence adds value with no fluff. It is front-loaded with the action and provides essential details in a structured format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 6 parameters (4 required), no annotations, and an output schema, the description explains each parameter and provides usage context. It does not describe the output, but since an output schema exists, that is acceptable. It could mention constraints like overlapping annotations or confirmation behavior, but overall it is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description includes a detailed Args section covering all 6 parameters, including formats (ISO 8601 for datetimes) and the meaning of dry_run. The input schema has 0% description coverage, so the description fully compensates and adds significant value beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Add an annotation to an existing annotation layer.' This specifies the verb (add), resource (annotation), and scope (to an existing layer). It distinguishes from sibling tools like create_annotation_layer (which creates layers) and delete_annotation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context by explaining annotations are time-based markers for time-series charts and advises to use list_annotation_layers or get_annotation_layer to find layer IDs. It implicitly suggests using this tool when you have an existing layer, but does not explicitly state when not to use it or list alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 behavioral traits such as the dry_run parameter (preview without changes), the default behavior of repair_dashboard_refs (no mutation unless requested), and the validate_after_create flag. It does not mention rate limits or auth, but overall transparency is good.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is fairly long but well-structured with a purpose statement, workflow recommendation, and bullet-point parameter list. It front-loads the key information. Every sentence adds value, though it could be slightly more concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 12 parameters, 0% schema coverage, and no annotations, the description covers the tool's behavior comprehensively, including dry_run, repair_dashboard_refs, validation, and template strategy. An output schema exists, so return values need not be explained. The description is sufficient for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It lists all 12 parameters and explains most, including examples for viz_type, the template enum, and the nuanced behavior of repair_dashboard_refs. Some parameter details (e.g., metrics can be ad-hoc objects) could be clearer, but overall adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Create a chart from an existing dataset,' providing a specific verb and resource. It distinguishes from siblings like update_chart and validate_chart by outlining the recommended workflow (create_dataset → create_chart) and referencing list_datasets and list_dashboards.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit context on when to use the tool, referencing the recommended workflow and listing prerequisite tools (list_datasets, list_dashboards). It also hints at when not to use regarding repair_dashboard_refs (defaults to False to avoid mutation). However, it does not explicitly state when to avoid using this tool in favor of alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description explains dry_run behavior ('validate inputs and return a preview without making any changes') and parameter defaults, offering sufficient transparency for a creation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise 6-line description with clear Args section. Front-loaded with main purpose. Slightly longer than minimal but each sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers creation and post-creation guidance; output schema exists so return values need not be described. Missing edge-case details but adequate for primary use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description adds meaningful explanations for all three parameters (dashboard_title, published, dry_run) beyond type/default, including dry_run's validation purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Create a new, empty dashboard' and provides follow-up actions (create_chart, update_dashboard), distinguishing it from sibling tools like create_chart and update_dashboard.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises using create_chart or update_dashboard after creation, giving clear context for subsequent steps. Does not include when-not-to-use scenarios but adequately guides usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states the tool deletes an annotation and provides a dry_run parameter to preview without executing. However, it does not disclose other behavioral traits such as irreversibility, required permissions, or return value. With no annotations, more detail could be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with a single sentence for the action and three lines for parameter descriptions. Every sentence provides unique information without repetition or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and the presence of an output schema (not shown), the description covers the core functionality, parameters, and a dry_run option. It lacks details on preconditions or error handling, but is sufficiently complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Since schema description coverage is 0%, the description fully compensates by explaining each parameter: layer_id (annotation layer ID), annotation_id (annotation ID to delete), and dry_run (preview without executing). This adds essential meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Delete' and the resource 'a specific annotation from an annotation layer'. It distinguishes from sibling tools like delete_annotation_layer (which deletes the entire layer) and create_annotation (creates).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that the tool is used for deleting a single annotation, requiring layer_id and annotation_id. It does not explicitly state when not to use it or provide alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It mentions that the tool enables embedding and returns a UUID, and that dry_run previews without executing. However, it does not clarify whether re-enabling an already embedded dashboard returns the same UUID, or discuss required permissions or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with a clear header sentence followed by an Args section. Every sentence adds value without redundancy. It is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (not shown), the description covers the key return value (embed UUID) and links to SDK usage. However, it does not address edge cases like re-enabling or error conditions, which would enhance completeness for a 3-parameter action tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description fully compensates. It explains each parameter: dashboard_id (required), allowed_domains (with example and behavior for empty list), and dry_run (previews action). This adds significant meaning beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Enable embedding for a dashboard and return its embed UUID.' It specifies the action (enable) and resource (dashboard), and distinguishes itself from sibling tools like disable_embedded_dashboard and get_embedded_dashboard.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the use case: embedding dashboards via the Superset embedded SDK using the returned UUID. It does not explicitly mention when not to use this tool, but the context from sibling tools makes it clear. The inclusion of dry_run provides guidance on previewing the action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It does mention that refresh_columns triggers a live query to the source database, which is useful context. However, it does not state whether the tool is read-only, require specific permissions, or have any side effects, leaving some gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a main sentence followed by parameter details. It is concise but the parameter descriptions could be slightly more streamlined. However, the structure is clear and all sentences are relevant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists and the tool is a simple retrieval operation, the description sufficiently covers what the tool returns (columns, metrics, SQL) and the effect of refresh_columns. No additional context is needed for optimal agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema. For dataset_id, it clarifies it as numeric. For response_mode, it describes each option and the default. For refresh_columns, it explains the behavior and output. Since schema description coverage is 0%, the description fully compensates and provides rich semantic context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves details for a single dataset, including columns, metrics, and SQL. It uses a specific verb ('Get') and resource ('dataset'), and the sibling tools include 'list_datasets' (for listing) and 'query_dataset' (for querying), distinguishing this from those.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises 'Use list_datasets first to find valid IDs', providing a clear prerequisite. It also explains the use case for setting refresh_columns=True. However, it does not explicitly mention when not to use this tool or discuss alternatives beyond the prerequisite hint.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It mentions the response modes but does not disclose behaviors like pagination, ordering, permissions, or side effects. For a simple list tool, this is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: a one-sentence purpose, a one-sentence usage hint, followed by clear bullet-style parameter descriptions. It is front-loaded and every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 0% schema coverage, the description adequately covers the parameters and purpose. It does not mention output structure, but an output schema exists (not shown). Some missing details like pagination limits are minor for a list tool; overall it is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description compensates by explaining each parameter: response_mode options, name_contains as case-insensitive substring, viz_type as exact-match, and dataset_id as datasource_id filter. This adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List charts in the current workspace,' using a specific verb and resource. It distinguishes itself from siblings like 'get_chart' (singular) and 'create_chart' by focusing on listing multiple charts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear use case: 'Use this to find chart IDs and see which viz types are in use.' However, it does not explicitly exclude alternatives or provide when-not-to-use guidance, though the purpose inherently differentiates from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description bears full responsibility. It describes the listing behavior and parameter effects, but does not explicitly state that the operation is read-only, mention authentication requirements, or note any potential side effects or limitations (e.g., pagination). The description is adequate but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with an introductory sentence, context about saved queries, usage guidance, and parameter documentation. Every sentence adds value, and the information is front-loaded with the core purpose and workflow.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists, the description does not need to detail return values. It covers purpose, workflow, and parameters thoroughly. However, it could mention whether the result is paginated or if there is a limit on the number of queries returned, making it slightly incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% coverage. It explains each parameter in detail: response_mode has three enumerated values with descriptions of the data each returns, and name_contains is clearly described as a case-insensitive substring filter. This compensates fully for the schema's lack of description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it lists saved SQL queries in the current workspace, identifies them as reusable snippets from SQL Lab, and distinguishes from get_saved_query by noting that this tool is used to discover query IDs. The verb 'list' and resource 'saved queries' are specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to use this tool to discover query IDs, then pass one to get_saved_query for full detail. This provides a clear workflow. However, it does not mention when not to use it or contrast with alternative tools like run_sql.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It mentions the tool is 'heavier' and returns counts/lists, but does not explicitly state whether it is read-only, has rate limits, or other side effects. The output schema helps but the description lacks full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first describes the action and output, second gives usage context. No wasted words, front-loaded with core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no params, output schema present), the description covers key aspects: what it does, what it returns, and when to use it. Nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has zero parameters, so the description naturally does not need to elaborate on them. Baseline for 0 parameters is 4, and the description contributes nothing beyond that, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool captures a full inventory of the workspace, listing specifics (dashboards, charts, datasets, databases). It explicitly differentiates from sibling tool workspace_catalog, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides direct guidance on when to use this tool vs workspace_catalog: 'use catalog for navigation, snapshot for full audit.' Implies this is for exhaustive audits, but does not explicitly state when not to use it or mention other alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are not provided, so the description must disclose behavioral traits. It mentions the dry_run parameter which previews without executing, but does not discuss destructive potential, permissions, or validation behavior. The output schema exists but is not described.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured as a docstring with a one-line summary and bulleted args. No extraneous information, and the most important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (4 params, 1 required, no enums, no nested objects, and an output schema exists), the description is largely complete. It could mention whether the update replaces all fields or only provided ones, but overall it covers the essential aspects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description carries the full burden. It gives meaningful explanations for all parameters: template_id, template_name, css, and dry_run, including default behavior for dry_run. This adds significant value beyond the input schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Update an existing CSS template.' which clearly states the verb and resource. It distinguishes the tool from siblings like create_css_template, get_css_template, and delete_css_template by specifying 'update' and 'existing'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating 'Update an existing CSS template.' It does not explicitly mention when not to use or prerequisites, but the purpose is distinct from siblings. No alternatives are named, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description carries full burden. It describes the listing behavior and response modes but does not discuss permissions, rate limits, or side effects. Since it is a read-only list, this is acceptable but not fully comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with purpose. It uses a clear bullet-like format for arguments. Every sentence adds value, but the structure could be slightly improved with more explicit labeling.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description does not need to detail return values. It covers the purpose, usage context, and parameter details. It adequately informs an agent for correct invocation, though mentioning workspace scope is already done.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description fully explains both parameters: response_mode with its three options (compact, standard, full) and name_contains as a case-insensitive substring filter. This adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'List dashboards in the current workspace' with specific verb and resource. It further distinguishes from siblings by guiding the agent to use this tool first to discover IDs, then use get_dashboard for details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use this tool: 'Start here to discover dashboard IDs, then use get_dashboard for detail on a specific one.' This provides clear context, though it does not mention exclusion cases or alternative list tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes query path, aggregation, time-series support, caching (force parameter), and response modes. Given no annotations, it carries the burden well, but lacks explicit statements about being read-only or side effects. Still, it covers many 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with a clear one-liner, followed by a brief explanation and structured Args list. While somewhat lengthy, each sentence serves a purpose and the structure is logical. Minor redundancy in describing time-series could be trimmed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers main use cases (aggregation, time-series, filtering) and prerequisites. Mentions output schema exists but doesn't describe return values, which is acceptable. Could include error handling or usage beyond time-series, but overall complete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the detailed Args section fully explains each parameter's meaning, format (ISO-8601 for dates), examples for granularity, and response_mode values. Description adds all necessary semantics beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it queries datasets using Superset's metric/dimension abstraction, differentiating from raw SQL and aligning with chart queries. The tool's purpose is specific and distinct from siblings like run_sql.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a clear prerequisite (use get_dataset first) and contrasts with raw SQL. However, it lacks explicit when-not-to-use or alternatives to other siblings like run_sql or validate_chart. Guidance is present but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and discloses key behaviors: dry_run for validation without changes, strict semantics for params_json requiring complete payloads, and an optional validation step. It does not cover error handling or permissions, but the main behavioral traits are well described.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear high-level summary followed by detailed parameter explanations. It is slightly lengthy but front-loads the purpose and key behaviors. Every sentence adds value, though some redundancy exists between the narrative and the Args list.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, 1 required) and the presence of an output schema, the description covers the main aspects: parameter roles, strict JSON semantics, dry run, and validation. It does not discuss return values or error scenarios, but the output schema likely covers those. Overall, it is sufficiently complete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite zero schema description coverage, the description thoroughly explains each parameter in the Args list, including the critical strict semantics of params_json and the behavior of dry_run and validate_after_update. It adds meaning beyond the schema by providing usage examples and warnings.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: updating an existing chart's title, viz type, or parameters. It identifies the specific resource (chart) and actions, and differentiates from siblings like create_chart and list_charts by focusing on modification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
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 the tool (for updating existing charts), including a reference to list_charts for finding the chart_id and advice on using get_chart for inspecting params. It does not explicitly state when not to use it or name alternatives, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses that it returns dependency links and is token-efficient, implying read-only behavior. Does not discuss error conditions or authentication, but the concise scope is sufficient for a cataloging tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two paragraphs, front-loaded with main purpose and then detail. Every sentence adds value; no wasted words. Highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers return content (resources with links) and token efficiency. Has an output schema (though not shown) to define structure. Could mention limits or recursive dependencies, but for a simple catalog tool it is adequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has no parameters, so baseline is 4. Description adds no parameter semantics because none exist, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it builds a 'relationship-aware catalog' of the workspace, listing specific resources (databases, datasets, charts, dashboards) with dependency links. Distinguishes from sibling snapshot_workspace by noting it is cheaper and returns only navigation fields.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance to use before creating or updating resources to understand workspace topology. Mentions it is cheaper than snapshot_workspace, providing context for selection. Does not explicitly state when not to use or list other alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that write SQL is blocked, only SELECT queries are allowed, and dry_run validates without changes. However, it does not mention side effects, authorization needs, or the immediate usability of the created dataset.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with a clear opening sentence followed by a contextual paragraph and then parameter descriptions. Every sentence adds value without filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description is not required to explain return values. It covers purpose, usage, constraints, and all parameters. It could mention potential errors or prerequisites, but overall it is adequate for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides clear, concise descriptions for all five parameters in the Args section, adding meaning beyond the schema's type and default values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a virtual dataset for charting, specifying the verb 'Create', the resource 'dataset', and its SQL-based nature. It differentiates from siblings by positioning itself as the main entry point from analytics to visualization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises to use list_databases to find database_id, mentions that write SQL is blocked, and describes the dry_run option. It implies when to use (after having a validated SQL query) but does not explicitly compare with siblings like create_chart or run_sql.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that updating SQL causes charts to refresh on next refresh and explains the dry_run behavior. Missing authorization needs or potential column metadata loss, but the provided details are valuable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the purpose, then a usage hint, a behavioral note, and a clear parameter list. Every sentence adds value, no redundancy, and it's appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, prerequisite, side effect, and all parameters. The output schema exists, so return value documentation is not required. Minor gaps like permissions or reversibility are missing, but overall it's thorough given the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description's Args section fully explains each of the 6 parameters, including purpose and recommendations (e.g., override_columns recommended when changing SQL). This adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it updates an existing dataset's SQL, name, or description. This verb+resource combination distinguishes it from create_dataset (creates new) and get_dataset (reads), and it is specific among sibling update tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using list_datasets to find the dataset_id, providing a clear prerequisite. However, it does not explicitly mention when not to use or list alternatives, though the context of updating versus creating or querying is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description indicates a state change (switching workspace) but does not detail side effects, permissions, or other behavioral traits. Since no annotations are provided, this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three sentences and a structured Args section. Every sentence adds value, and the purpose is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, clear purpose), the description fully covers what an agent needs: purpose, prerequisite, parameter guidance, and link to sibling tool. Output schema exists but its explanation is unnecessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema only specifies type string for workspace_title, but the description adds an example ('Mysten Labs--General') and clarifies it is the exact title. This adds meaningful context beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Switch to a different Preset workspace by its exact title.' It uses a specific verb and resource, and distinguishes itself from sibling tools by referencing list_workspaces for discovering titles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: 'You must call this before any read/write tools if PRESET_WORKSPACE was not set at startup.' It also advises using list_workspaces to find titles, providing clear usage context and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the burden. It explains the behavior of the response_mode parameter with three options and default. For a read-only tool, this is good transparency, though it doesn't discuss error handling or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences: purpose, prerequisite, parameter details. Every sentence earns its place with no wasted words. Front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two parameters and an output schema present, the description covers purpose, prerequisite, and parameter semantics sufficiently. No additional information needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description fully compensates by explaining both parameters: database_id as a numeric ID and response_mode with each enum option and default. This adds significant meaning beyond the schema's bare types and enums.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Get detail for a single database connection,' using a specific verb and resource. It distinguishes from sibling tools like list_databases by advising to use that first to find valid IDs, and from other get_* tools by the resource name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises to use list_databases first, providing a clear prerequisite. Does not mention when not to use or alternatives, but for a simple get operation this is sufficient context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and description does not explicitly state if the operation is read-only or disclose any behavioral traits beyond basic listing. However, the nature of listing implies safety.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with purpose and usage, then parameter details in a clear structure. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Comprehensive for a simple list tool: covers purpose, usage context, parameter details, and references output schema. Also provides ordering guidance relative to sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Fully explains both parameters: response_mode with enum options and default, and name_contains as case-insensitive substring filter, adding meaning beyond the schema despite 0% coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'List database connections in the current workspace' and specifies it should be called before run_sql or create_dataset, distinguishing it from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to call this tool BEFORE run_sql or create_dataset to find a valid database_id, providing strong usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description carries full burden. Clearly states read-only, single statement, blocked operations, response modes. Does not mention authentication or error handling, but constraints are well-disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with purpose and usage context, then parameter list. No redundant sentences. Each sentence adds value. Well-structured and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers parameter semantics, usage guidelines, behavioral constraints, and prerequisite (list_databases). Does not explain return values because output schema exists. Missing minor details like timeouts, but overall complete for a query tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description comprehensively explains each parameter: sql, database_id (with lookup hint), schema (optional), limit (default 1000), and response_mode (enums with descriptions). Adds meaning beyond bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it executes a read-only SQL query via Preset, specifying it's for debugging/verification before creating datasets. Distinguishes from sibling tools by referencing Snowflake MCP for primary exploration.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use (confirm query works before creating dataset) and when not (prefer Snowflake MCP for primary SQL exploration). Also lists permitted commands (SELECT, SHOW, DESCRIBE, EXPLAIN) and blocks write operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 clearly states the tool lists workspaces, which implies read-only behavior. However, it does not explicitly state that it is non-destructive or safe, but given the simple nature of a list operation, this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. The first sentence states the purpose, the second provides crucial usage guidance. Perfectly front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters, an output schema exists, and the description clearly states what the tool does and how it fits into a workflow (call first, pass to use_workspace). This is fully complete for its context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters, the description need not add parameter details. The baseline for 0 params is 4. The description mentions the output contains workspace titles, which adds context, but no parameter information is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('list') and resource ('workspaces'), clearly stating the scope ('you have access to'). It differentiates from sibling tools by explicitly noting that this should be called first to discover workspace titles, which contrasts with similar listing tools for other resources.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool ('FIRST') and how the output should be used ('pass one to use_workspace'). This provides clear guidance on context and avoids alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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