GoldenCheck

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

No annotations are provided, so the description carries the full burden. It discloses the outputs (domain detection, counts, strategies) but fails to mention behavioral aspects like whether the tool modifies files, requires specific permissions, or is read-only. The description gives some transparency but lacks critical behavioral context.

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

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

The description is two concise sentences, front-loaded with the core purpose. Every word adds value, and there is no redundancy. It appropriately prioritizes key information.

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

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

Given the tool's complexity (1 parameter, no output schema, no annotations), the description provides a reasonable overview of inputs and outputs. However, it omits details on file access methods, error handling, and whether the tool is read-only. It is adequate but not fully comprehensive.

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

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

Schema description coverage is 100% with a single parameter 'file_path' documented as 'Path to the data file (CSV, Parquet, Excel)'. The description adds no extra meaning beyond this, merely listing output types. Baseline is 3, and the description does not enhance parameter understanding.

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

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

The description clearly states the tool's purpose: analyzing a data file to detect domain, profile columns, and recommend scanning strategies. It lists specific outputs (domain detection, column/row count, strategy decisions, alternatives), making it distinct from sibling tools like 'profile' or 'scan' which focus on different aspects.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, contexts where it is appropriate, or when to avoid it. The agent is left to infer usage solely from the purpose.

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

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

No annotations are provided, so the description must carry the full burden. It states the action (approve/reject) but fails to disclose behavioral traits: whether the action is reversible, what happens to the item after (e.g., removed from queue), required permissions, or any side effects. This is insufficient for confident invocation.

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

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

The description is extremely concise at two sentences, with no wasted words. It front-loads the action and immediately clarifies the required decision values. Every sentence is essential and earns its place.

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

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

Given the tool modifies state (approve/reject) and has no output schema, the description should explain return values or success indicators. It mentions nothing about what the agent can expect after calling—no confirmation, no error conditions, no side effects. The description is incomplete for safe usage.

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

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

The input schema has 100% description coverage, with clear descriptions for all three parameters (item_id, decision, reason). The description repeats the enum values from the schema, adding no new semantic meaning. Baseline 3 is appropriate as the schema already does the heavy lifting.

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

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

The description clearly states the verb ('approve'/'reject') and specific resource ('review queue item'), and explicitly defines the allowed decisions ('pin' or 'dismiss'). It is concise and leaves no ambiguity about the tool's function, distinguishing it from siblings like review_queue (which likely lists items) and review_stats (statistics).

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

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

The description provides no guidance on when to use this tool versus alternatives or when not to use it. Sibling tools like analyze_data, explain_finding, or suggest_fix are present, but no context is given for choosing approve_reject over them. An agent would need to infer usage from the tool's name and description alone.

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

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

No annotations are provided, so the description must carry full behavioral transparency. It describes the steps (scan, triage, generate) but omits side effects (e.g., whether it writes to disk, modifies files, or requires permissions). It does not disclose if the tool is read-only or destructive. The description is partially transparent but leaves open questions about behavior.

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

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

The description is two sentences, front-loaded with the primary action and a secondary optional feature. Every word contributes meaning; no filler or repetition. It is highly concise and well-structured.

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

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

Given no output schema, the description should clarify what the tool returns. It mentions 'generate goldencheck.yml content' but not whether that content is printed, saved, or returned. However, the description adequately covers the input parameters and the overall purpose. The missing output format is a minor gap, but overall it is fairly complete for a tool with simple parameters.

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

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

Schema coverage is 100%, with both parameters already described. The description adds context that 'constraints' can filter or adjust output, but the schema already lists the same fields. Thus, the description adds minimal value beyond the schema, consistent with the baseline score of 3 for high schema coverage.

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

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

The description clearly states the tool scans a data file, triages findings by confidence, and generates goldencheck.yml content. This is a specific verb+resource that distinguishes it from sibling tools like 'scan' (just scans) or 'analyze_data' (analysis). The action of generating a config file is unique.

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

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

The description does not explicitly state when to use this tool versus alternatives. It mentions optional constraints but no prerequisites (e.g., needing prior scan results) or exclusion criteria. The existence of siblings like 'scan' and 'validate' implies a workflow, but guidance is only implicit.

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

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

No annotations provided; the description only states the scanning and comparison action. It does not disclose any side effects, permissions needed, rate limits, or performance implications, which is a gap for a tool that may run many checks.

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

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

Two concise sentences, front-loaded with action and output. Every word is informative without redundancy.

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

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

For a simple tool with one parameter and no output schema, the description covers the purpose and output format adequately. However, it omits details about error handling, file format expectations, and potential performance cost of scanning all domain packs.

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

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

The single parameter 'file_path' has full schema coverage, so baseline is 3. The description adds context that the file is scanned with domain packs, but does not add new details about parameter values or constraints.

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

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

The description clearly states the tool scans with all domain packs and compares health scores, recommending the best domain. It distinguishes from sibling tools like 'health_score' (single domain) and 'list_domains' (list domains only).

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

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

The description implies using this tool to select the best domain for a file, but does not provide explicit when-to-use or when-not-to-use guidance, nor does it reference alternatives like 'get_domain_info'.

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

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

The description discloses that the tool scans the file, profiles the column, and explains findings, which signals non-trivial operations. However, without annotations, it does not clarify if these operations are read-only, what data is accessed, or potential performance impacts, leaving gaps in behavioral understanding.

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

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

The description is highly concise with two sentences. The first sentence clearly states the purpose, and the second lists the main actions. No extraneous information is present, making it easy to parse quickly.

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

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

Given the tool performs scanning, profiling, and explanation, the description provides a high-level overview but lacks details on the return format, scope of findings, or how it differs from sibling tools. An agent might miss important context for correct invocation, such as expected output structure.

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

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

Schema coverage is 100%, so the schema adequately describes the two required parameters. The description does not add any additional semantic meaning beyond what is in the schema, so it meets the baseline but does not enhance understanding.

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

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

The description clearly states the tool returns a 'natural-language health narrative' for a column, which is a specific output. However, it does not explicitly distinguish from sibling tools like 'explain_finding' or 'get_column_detail', which could cause confusion about when to use this specific tool.

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

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

No guidance is provided on when to use this tool over alternatives such as 'get_column_detail' or 'explain_finding'. There is no mention of prerequisites, context, or when not to use it, leaving the agent to infer the best choice from the description alone.

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

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

No annotations are provided, so the description bears full responsibility. It fails to disclose behavioral traits such as whether the tool is read-only, any side effects, required authentication, or the nature of the response (e.g., plain text or structured). The absence of output schema further heightens the need for such information.

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

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

The description consists of two concise sentences that front-load the primary action first ('Explain a single finding in natural language') and then state requirements. No superfluous words: every sentence serves a purpose.

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

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

Despite having moderate complexity (two parameters, one nested object) and no output schema, the description omits details about the return value (e.g., format of the explanation), error conditions, or the expected source of the 'finding.' This leaves the agent guessing about critical aspects of usage.

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

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

The input schema already describes both parameters with 100% coverage. The description adds context for 'file_path' (needed for profile context), which is meaningful beyond the schema. However, it doesn't elaborate on the structure of the 'finding' object beyond what the schema provides.

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

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

The description clearly states 'Explain a single finding in natural language,' which is a specific verb and resource. This distinguishes it from siblings like 'explain_column' and 'analyze_data,' as it focuses on explaining a single finding rather than columns or other analyses.

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

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

The description mentions prerequisites (finding as JSON dict, file_path for profile context) but does not explicitly state when to use this tool over alternatives or when not to use it. The context of siblings is present but not addressed directly.

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

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

No annotations are provided, so the description must fully disclose behavior. It only states the tool retrieves a 'detailed profile and findings', implying a read operation, but does not mention safety, authorization needs, rate limits, or what the 'findings' entail.

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

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

The description is a single, front-loaded sentence with no unnecessary words. It is concise, but the brevity leaves some gaps in comprehensiveness.

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

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

With no output schema or annotations, the description should provide more context about what 'detailed profile and findings' includes. It does not explain the return format, pagination, or how this tool differs from sibling tools, making it incomplete for effective use.

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

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

Both parameters are described in the schema (100% coverage), so the baseline is 3. The description adds that the tool returns 'detailed profile and findings', which hints at the output's richness but does not add formatting or constraints beyond the schema.

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

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

The description clearly states the action 'get' and the resource 'detailed profile and findings for a specific column'. It is specific enough to indicate what the tool does, though it does not explicitly differentiate from siblings like 'explain_column' or 'profile', which may have overlapping functionality.

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

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

There is no guidance on when to use this tool versus alternatives. The description does not mention context, prerequisites, or exclusions, leaving the agent to infer usage without assistance.

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

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

With no annotations, the description must convey behavior. The verb 'Get' and 'detailed info' correctly imply a read-only operation. However, it does not explicitly state that no data is modified, and there is no mention of authentication or rate limits. Still, for a simple info retrieval, the behavior is clear enough.

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

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

The description is a single sentence of 15 words, front-loaded with the action and target, and efficiently lists what is returned. No extraneous information.

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

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

Given only one parameter and no output schema, the description adequately explains the tool's purpose and what it returns ('semantic types, their name hints, and suppression rules'). It could be more specific about the output format (e.g., list, structure), but is sufficiently complete for an agent to understand the tool's functionality.

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

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

The schema description coverage is 100% (one parameter fully described with examples). The tool description adds no additional meaning beyond the schema; the parameter description in the schema already states 'Domain pack name (e.g., healthcare, finance, ecommerce)'. Thus, baseline score of 3 is appropriate.

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

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

The description explicitly states 'Get detailed info about a specific domain pack' and enumerates what it lists (semantic types, name hints, suppression rules), providing a specific verb+resource. It clearly distinguishes from sibling tools like list_domains (which lists packs) and install_domain (which installs).

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

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

The description implies use for retrieving details of a single domain, but provides no explicit guidance on when to use versus alternatives like list_domains or compare_domains. No when-not-to-use or prerequisite information is given.

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

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

No annotations provided; description fails to disclose behavioral traits such as read-only, side effects, or authentication needs. Minimal info.

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

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

Two sentences, front-loaded with key info. Efficient but could add more substance without losing conciseness.

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

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

Lacks behavioral details and usage context. For a simple tool, description is nearly complete but missing when to use vs siblings.

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

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

Single parameter fully described in schema (100% coverage). Description adds no extra meaning beyond schema.

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

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

Description clearly states the verb (Get), resource (health score for a data file), and output (A-F, 0-100). Differentiates from siblings like analyze_data or profile.

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

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

No guidance on when to use this tool versus alternatives like analyze_data or profile. Simple statement without context.

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

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

No annotations are provided, so the description carries full burden. It states 'download' and 'save' but does not mention whether it overwrites existing files, requires network, or has any side effects. This is insufficient for a mutation-like tool.

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

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

Single sentence of 20 words, direct and without filler. Every word adds value.

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

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

With 2 parameters, no output schema, and no annotations, the description is too brief. It lacks information on return values, prerequisites, or potential side effects. A download tool should clarify behavior like overwriting or network usage.

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

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

Schema coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the schema: it mentions 'domain pack name' which is already in schema, and does not elaborate on 'output_path' parameter.

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

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

The description clearly states the verb 'download' and the resource 'community domain pack', specifying it is from the goldencheck-types repository and saved for future scans. This distinguishes it from sibling tools like list_domains (list) and get_domain_info (info).

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

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

The description implies usage when needing to add a domain pack for scanning but provides no explicit when-to-use or when-not-to-use guidance. Sibling names offer hints, but the description itself lacks explicit context for alternatives.

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

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

No annotations, but description accurately describes a read-only list operation. Missing details on output format but acceptable for a simple zero-arg tool.

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

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

Single sentence, compact, and no superfluous text. Perfectly concise.

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

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

No output schema, but description adequately covers purpose. Missing return format guidance, but sufficient for a discovery tool.

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

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

No parameters; description adds no param info but baseline is 4 as per rules for zero-param tools with 100% schema coverage.

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

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

Description clearly states verb 'List' and resource 'profiler checks' with added detail on what they detect. Distinct from sibling tools like profile or scan.

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

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

Implicitly suggests usage by stating 'No arguments needed' but lacks explicit guidance on when to use vs alternatives like analyze_data or profile.

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

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

No annotations are provided, so the description carries full burden. It implies a read-only list operation but does not explicitly confirm it is safe, non-destructive, or has no side effects. The description lacks behavioral traits beyond the basic action.

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

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

The description is two sentences, front-loaded with the key action, and contains no unnecessary words. It is efficiently structured for quick comprehension.

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

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

The description adequately explains the tool's output scope ('all available domain packs') but lacks details on the output format or structure. Since no output schema exists, the description should ideally clarify what the response contains (e.g., names, descriptions). This gap reduces completeness.

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

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

The input schema has no parameters (100% coverage), so baseline is 3. The description adds no parameter information; it only restates the tool's purpose. No additional meaning is provided beyond the schema.

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

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

The description clearly states the tool's action ('List all available domain packs') and explains what domain packs are. It effectively distinguishes from sibling tools like install_domain or get_domain_info by focusing on listing all available packs.

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

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

The description provides no guidance on when to use this tool versus alternatives (e.g., before installing or comparing domains). It does not mention any context or exclusions.

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

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

Without annotations, the description carries the burden of disclosing behavioral traits. It mentions the output format but omits whether the tool performs writes, requires permissions, or has side effects.

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

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

Two sentences, directly stating purpose and output fields, with no redundant information.

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

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

For a simple tool with 2 parameters and no output schema, the description adequately covers the output structure. Missing behavioral context is a minor gap.

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

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

Schema coverage is 100%, so the description adds minimal value. It explains the overall output but does not elaborate on parameter semantics beyond what the schema provides.

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

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

The description clearly states it generates a structured quality attestation JSON for a data file, listing specific fields like health score, findings summary, pinned rules, and attestation status. This verb+resource combination distinguishes it from sibling tools like 'analyze_data' or 'health_score'.

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

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

The description implies usage for generating handoff attestations but does not explicitly state when to use it versus alternatives or provide context like prerequisites or when not to use it.

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

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

With no annotations, the description carries full burden. It discloses output (type, null%, etc., health score) but does not mention potential performance impacts of sampling large files, whether the tool modifies data, or error conditions. The default sample_size parameter is documented in schema but not in description, missing a chance to set expectations.

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

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

The description is extremely concise: two sentences that front-load key information (output columns and health score). Every word adds value; there is no repetition or filler. Ideal for quick comprehension.

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

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

The description covers return values adequately given no output schema, but lacks context on file format expectations, error handling, and how the health score is computed. It is complete enough for basic profiling but misses details an agent might need to use it reliably.

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

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

Schema description coverage is 100% for both parameters. The description adds no additional meaning beyond the schema—it only lists output, not parameter details. Baseline is 3 as required for high coverage, and no extra value is provided.

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

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

The description clearly states the tool's purpose: profiling a data file and returning column-level statistics plus a health score. It uses a specific verb and resource, distinguishing it from siblings like 'health_score' or 'get_column_detail' by focusing on profiling entire columns. However, it does not explicitly differentiate from 'analyze_data' which might be similar.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, file format expectations, or scenarios where other tools like 'analyze_data' or 'get_column_detail' would be more appropriate. The agent receives no context for decision-making.

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

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

With no annotations, the description carries the burden. It states the tool lists items and returns those needing human decision, implying a read-only operation. However, it does not disclose any potential side effects, permissions, or pagination behavior. Adequate but minimal.

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

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

The description is a single, well-crafted sentence that states the action and purpose. It is front-loaded and contains no filler content.

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

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

Given the simplicity of the tool (one required parameter, no output schema, no nested objects), the description adequately explains what it does and what it returns. It is sufficient for an agent to understand how to use it, though it could mention output format or sorting.

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

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

The schema provides 100% description coverage for the single parameter 'job_name'. The description echoes this by saying 'for a given job' but adds no additional semantic meaning beyond the schema.

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

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

The description clearly states the action ('List...'), a specific resource ('pending review items for a given job'), and the context ('items that need human decision (medium-confidence findings)'). This distinguishes it from siblings like 'approve_reject' and 'analyze_data'.

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

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

The description implies when to use (to get pending items for a job) but does not explicitly state when not to use or contrast with siblings. No alternatives or exclusions are provided.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns counts, but does not clarify whether it is a read-only operation, any authentication needs, or rate limits. It partially fulfills but could be more explicit about its non-destructive nature.

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

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

The description is a single, well-structured sentence that is front-loaded with the verb and resource. Every word adds value, with no redundancy or irrelevant details.

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

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

For a simple tool with one parameter and no output schema, the description adequately covers what the tool does and what the output contains. It could mention if the counts are live or cached, but overall it is 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.

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

Schema description coverage is 100% for the single parameter 'job_name' with a clear description. The tool description adds minimal value beyond stating it pertains to a 'job'. Baseline 3 is appropriate as the schema already documents the parameter sufficiently.

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

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

The description clearly states the tool's function: 'Get review queue statistics for a job' and specifies the three types of counts ('pending, pinned, and dismissed items'). This is a specific verb-resource combination that distinguishes it from siblings like 'review_queue' or 'approve_reject'.

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

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

No explicit guidance on when to use this tool versus alternatives. The description implies usage for obtaining statistics, but does not mention when not to use it or compare to sibling tools like 'review_queue'.

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

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

Without annotations, the description must disclose behavioral traits. It states the tool scans files and returns findings, implying read-only operation. It does not explicitly confirm non-destructiveness, auth needs, or rate limits. The description adds some value beyond the schema but lacks comprehensive disclosure.

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

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

The description is two sentences, front-loaded with the core purpose, and contains no unnecessary words. Every sentence adds value: first defines the task, second explains output and ease-of-use.

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

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

Given the tool has 4 parameters with full schema coverage, an output schema is absent, but the description explains key return fields. It is fairly complete for a scanning tool, though it could mention output format (e.g., list of findings). The sibling context is large, but the description sufficiently covers the tool's role.

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

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

The input schema provides 100% coverage with descriptions for all four parameters (file_path, llm_boost, sample_size, llm_provider). The description does not add significant new information about parameters beyond what the schema already offers. Baseline is 3 due to high schema coverage.

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

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

The description clearly states that the tool scans data files (CSV, Parquet, Excel) for data quality issues. It specifies the output includes severity, confidence, affected rows, and sample values. However, it does not explicitly differentiate from sibling tools like analyze_data or validate, which may perform similar analysis.

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

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

The description mentions 'No configuration needed — rules are discovered from the data,' implying zero-setup scanning. However, it provides no guidance on when to use this tool versus alternatives (e.g., validate, profile) or when not to use it. There are no prerequisites or context for optimal use.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states that the tool is non-destructive ('without applying them') and describes what the preview will show (columns, fix types, rows affected, before/after samples). This is a transparent account of the tool's behavior.

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

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

The description consists of two concise sentences. The first sentence states the tool's core purpose, and the second elaborates on the output details. Every word adds value, with no wasted or redundant phrases.

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

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

Given no output schema and no annotations, the description compensates by detailing the preview contents. However, it does not specify the return format (e.g., JSON structure) or how to interpret the result. For a preview tool with few parameters, this is mostly adequate but leaves some gaps.

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

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

Schema description coverage is 100%, so the baseline is 3. The description does not add any additional meaning about the parameters 'file_path' or 'mode' beyond what the schema already provides. It focuses on the tool's output rather than parameter details.

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

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

Description uses a specific verb+resource: 'Preview fixes for a data file.' It clearly states that no changes are applied, and it lists the output contents (columns, fix types, etc.). This distinguishes it from sibling tools like 'analyze_data' or 'approve_reject' which serve different purposes.

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

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

The description implies that this tool is used to preview fixes before applying them, but it does not explicitly state when to use this tool versus alternatives. It does not provide when-not-to-use guidance or mention related sibling tools, leaving the agent to infer usage context.

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

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

Describes output (validation findings with specific checks), but lacks side-effect disclosure (e.g., whether it modifies anything), auth needs, or error handling. With no annotations, more behavioral context expected.

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

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

Two sentences, front-loaded purpose, then details. Efficient but could be more compact.

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

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

Covers return value (validation findings). Missing error conditions, file format expectations, and prerequisites for goldencheck.yml. Adequate but not complete.

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

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

Schema coverage 100% and both parameters have descriptions. Description adds no extra meaning beyond schema (config_path default already stated). Baseline 3 applies.

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

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

Description specifies verb (validate), resource (data file against goldencheck.yml rules), and lists check types (existence, required, unique, enum, range). This clearly distinguishes it from sibling tools like analyze_data or scan.

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

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

Implied usage for validating data files, but no explicit when-to-use, when-not-to-use, or alternatives. Missing guidance on prerequisites (e.g., file format, existence of goldencheck.yml).

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