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

With no annotations, the description must carry the disclosure burden. It does disclose the append behavior and source/target, but omits side effects, asynchronous job behavior, idempotency, or failure handling.

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

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

The description is a single front-loaded sentence with no filler, efficiently conveying source, target, and append mode. It is concise, though this conciseness comes at the cost of behavioral and parameter detail.

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

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

For an ETL write operation with no annotations or output schema, the description is too sparse. It does not explain the meaning of as_job, the relationship to etl_star_historical, or what happens on failures, empty change feeds, or duplicate data.

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?

There is one parameter, as_job, with 0% schema description coverage. The description does not mention or explain this parameter, leaving the agent with only the parameter name and default value as hints.

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 names a specific verb ('Apply'), the exact data source ('Delta change feed from OLTP customer_order'), target ('fact_sales'), and mode ('append'). This clearly distinguishes it from siblings like etl_star_historical and generate_historical_oltp.

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 this is for incremental CDC appends, but it does not explicitly name alternatives, state prerequisites, or describe when not to use it. Usage context is inferable but not spelled out.

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

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

With no annotations, the description carries the full behavioral burden. It does disclose the key destructive trait 'overwrite', which is essential safety information for an agent. However, it omits consequences such as whether tables are dropped/recreated, whether the operation is transactional, and what happens to existing dependent objects.

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

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

The description is a single efficient sentence with no filler and the core action front-loaded. It is concise, though the brevity leaves out details that are penalized in other dimensions.

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

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

For a destructive ETL tool with no annotations and no output schema, the description is too thin. It leaves the as_job parameter unexplained and does not state expected completion signals, side effects, or the relationship to etl_star_cdc, making confident invocation harder than necessary.

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 has one boolean parameter 'as_job' with 0% description coverage, and the description does not explain what it controls. The agent cannot know whether setting as_job to false changes execution mode, output, or behavior, so the description fails to compensate for the schema's silence.

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

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

The description uses a specific verb ('Rebuild') with a clear resource ('star-schema dims and fact_sales') and source ('from OLTP'), and flags 'overwrite' to signal a full replacement. It does not explicitly differentiate from sibling etl_star_cdc, though the word 'historical' and the rebuild/overwrite language weakly distinguish it as a full load.

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 is given about when to use this tool instead of etl_star_cdc, generate_historical_oltp, or other siblings. An agent must infer from the name and the words 'Rebuild'/'overwrite' that this is a full refresh, but there is no direct statement of intended use 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies mutation via 'Write' but discloses nothing about whether existing data is overwritten, whether execution is synchronous or asynchronous (despite the as_job parameter hinting at job-based execution), or what side effects occur. For a tool that writes multiple tables, this is a significant gap.

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

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

A single, front-loaded sentence with no wasted words - structurally clean. However, this is under-specification rather than effective conciseness: the description omits critical information about parameters and behavior, making it too brief for the tool's complexity.

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

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

This is a 4-parameter mutation tool that writes six tables, with no annotations and no output schema. The description is far too thin - it doesn't explain parameter semantics, execution model, data implications, or any context an agent needs to invoke it correctly. Completely inadequate for a tool of this complexity.

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

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

Schema description coverage is 0% and the description adds nothing about the 4 parameters. While year_count, customer_count, and orders_per_year are somewhat self-explanatory by name, as_job is ambiguous (job vs synchronous execution) and none of the parameters are explained or contextualized. The description fails to compensate for the complete lack of schema-level parameter documentation.

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

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

States a specific action ('Write') with an enumerated resource list (customer, address, order, line, shipment, entity_link tables). The purpose is clear and concrete. However, differentiation from siblings relies mostly on the tool name ('historical' vs generate_realtime_orders) rather than the description itself, and 'write' is a generic verb that doesn't clarify whether this creates, populates, or overwrites.

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

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

Provides no guidance on when to use this tool versus alternatives. Siblings include generate_realtime_orders (similar generation, realtime) and etl_star_historical (ETL for historical data), yet the description gives no selection criteria, prerequisites, or exclusions. An agent must infer usage entirely from the tool name.

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

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

With no annotations, the description carries the full burden and only discloses that it appends orders within a count range and year-window choices. It does not state side effects, persistence, idempotency, what 'as_job' does, or what the caller should expect afterward.

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 short sentences deliver the core action, count bounds, and year-window options without wasted words. It loses a point only because omitting as_job is a meaningful structural gap, not because of verbosity.

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

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

For a tool with no annotations and no output schema, the description leaves out too much: return behavior, job semantics, prerequisite state, and how this append interacts with historical generation. An agent can make a default call but cannot fully reason about consequences.

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

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

The description explains year_window values and indirectly maps '100-10000' to the count parameter, which helps. However, as_job is completely unexplained, and schema coverage is 0%, so the agent cannot derive its meaning from the schema either.

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

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

The description uses a specific verb ('Append'), identifies the resource ('new OLTP orders'), and gives a concrete count range, so an agent knows the core action. It does not explicitly contrast with generate_historical_oltp, but the 'realtime' name and year-window options make the intent reasonably distinguishable.

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 about when to use this tool versus generate_historical_oltp, etl_star_cdc, or other siblings. The 'append' semantics hint at real-time ingestion, but the description leaves the selection criteria 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?

There are no annotations, so the description needs to convey safety and side effects. It states it returns a list of 10 specialists, which implies a read-only operation, but it doesn't explicitly say it doesn't modify anything. It also doesn't clarify if the list is cached or if it takes time, but since it's a simple list with no params, the behavior is fairly predictable. The description adds some value by noting the shared data model, which hints at the underlying structure.

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

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

The description is a single, concise sentence that front-loads the action ('List the 10 Databricks Genie fraud specialists') and adds a relevant detail about their data model. There is no fluff; every word contributes to the meaning. This is an exemplary model of brevity.

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 that there are no parameters and no annotations, the description is nearly complete: it explains what the tool does and provides a key detail about the output (the 10 specialists share OLTP + dims/facts). However, it doesn't describe the output schema (which exists), but since the description doesn't need to repeat what the output schema already holds, this is not a major gap. The description is adequate for a simple listing tool.

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

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

The input schema has zero parameters, and the description confirms that by mentioning 'the 10'—the list size is fixed. Since there are no parameters, there's no additional semantic burden; the description fully covers the empty schema. This is a case where the description doesn't need to explain anything, so a high score is appropriate. However, it could be clearer that the list is fixed and no parameters are needed.

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

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

The description tells the agent exactly what this tool does: it lists the 10 Databricks Genie fraud specialists, which is a specific, concrete action. It includes the key detail that these specialists share a specific data model (OLTP + dims/facts), which adds useful context. However, it doesn't explicitly differentiate from sibling list_fraud_cases, though the nouns differ (agents vs cases), so the differentiation is implicit.

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 the use case—when you need an overview of the fraud agents available—but it doesn't explicitly say when to use this instead of list_fraud_cases or run_fraud_agent_cases. The tool has no parameters, so it's a simple listing operation, but no guidance is given on what actions to take with the results.

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 transparency burden. 'List' communicates a non-mutating read operation and 'the 15' sets exact scope, but there is no stated behavior about output sorting, freshness, or side effects; this is adequate but minimal for a zero-parameter read 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?

Two compact sentences with the main action front-loaded; every word earns its place and the second sentence adds useful conceptual context.

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 zero-parameter listing tool with an output schema present, the description gives the critical extra information: there are exactly 15 cases and each is a named SQL evidence pack. It does not explicitly connect to run_fraud_case for the next action, but that belongs to usage guidance and is not required for invoking this tool.

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

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

The input schema is empty and fully covers all parameters (none), so there is no parameter gap for the description to fill. The 0-parameter baseline of 4 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?

Uses a specific verb ('List') and resource ('fraud detection cases'), and adds the exact count and a definition ('named SQL evidence pack'). This clearly distinguishes it from siblings such as run_fraud_case (executes) and list_fraud_agents (different entity).

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 the discovery entry point for the fixed set of fraud cases, and the definition of a case hints that returned names can be used with run_fraud_case. However, it never explicitly states when to choose this tool over alternatives or when not to use it, so 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?

With no annotations, the description carries the full behavioral burden. It discloses that only SELECT operations are run, that results are capped at 50 rows, and that full-table dumps are forbidden. This is meaningful behavioral context for a read-only query tool, though it does not mention auth, errors, or return shape.

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 short sentences with no filler. The key constraints are front-loaded and every word adds value. This is an efficient, well-structured description.

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 one-parameter query tool, the description is mostly complete: it states the operation, allowed data sources, row limit, and guarding behavior. It lacks an explicit statement about return format or output structure, but no output schema exists and the tool's purpose strongly implies a row result set.

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 describes the single 'sql' parameter only by name and type, with 0% description coverage. The tool description compensates somewhat by clarifying that the SQL must be a SELECT and that the result set is limited to 50 rows. However, it does not explain SQL formatting, table qualification, or statement restrictions beyond SELECT.

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 states a specific verb and resource: 'Run a SELECT against dims, facts, or OLTP.' It also adds a concrete constraint, LIMIT 50, making the tool's purpose unmistakable. It clearly differentiates from the ETL/generation/fraud-case siblings by being the only direct query 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?

The description gives clear context: this is for SELECT queries against dims, facts, or OLTP, with a 50-row limit. It does not explicitly name alternatives or state when not to use it, but the scope is clear enough for an agent to select it appropriately among the listed siblings.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It says 'Run' without explaining whether this starts long-running jobs, writes data, returns results, or has side effects on the evidence packs. For an action-oriented tool, this is a significant transparency gap.

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

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

The description is a single, front-loaded sentence. It uses no filler and every word contributes to describing what the tool does.

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 side-effecting 'run' tool with no annotations, no output schema, and minimal parameter explanation, the definition is incomplete. It communicates the high-level intent but lacks context about effects, output/return behavior, and how to discover the supported fraud specialist IDs.

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 no description for agent_id, and schema coverage is 0%, so the description must compensate. The phrase 'one of the 10 fraud specialists' gives some semantic meaning to agent_id, but it does not explain how to obtain valid IDs or connect this to list_fraud_agents.

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

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

The description uses a specific verb ('Run') and resource ('evidence pack'), and the plural 'every' distinguishes it from the sibling run_fraud_case, which implies a single case. The scope is clear: all evidence packs owned by one of the 10 fraud specialists. It lacks an explicit contrast with sibling tools, but the distinction is recoverable.

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 run_fraud_case, list_fraud_cases, or list_fraud_agents. The description implies a batch operation for a specialist, but it does not state prerequisites, exclusions, or how to choose among 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 are provided, so the description carries the burden of disclosing behavior. It does reveal a key behavioral trait: the tool returns at most 50 evidence rows, which is useful for setting agent expectations. However, it does not mention side effects (e.g., whether running a case performs writes or triggers external actions), which is a gap 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?

The description is extremely concise—two short sentences, no filler. It front-loads the core action and then adds the crucial output limit. Every word earns its place.

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

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

Given the tool's simplicity (single parameter, no output schema, no nested objects), the description is nearly sufficient but falls short in parameter guidance and side-effect disclosure. The 50-row limit is good context, but the lack of case_id explanation and any indication of write behavior leaves minor gaps.

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

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

The input schema only has a case_id string with no description, and the schema description coverage is 0%. The description does not explain what case_id should be (e.g., the format, the allowed range 01-15, or how to obtain valid IDs). Since the schema provides no help, the description fails to compensate, leaving the agent guessing.

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 states a specific verb ('run') and resource ('fraud case'), and clarifies that it handles one case at a time (ids 01-15). This is clear enough to distinguish from sibling tools like list_fraud_cases, though it doesn't explicitly name them.

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 it: when you need to execute a specific fraud case. However, it does not explicitly say when not to use it or mention alternatives like list_fraud_cases for listing, or run_fraud_agent_cases for running multiple cases. The single-case scope is a mild hint but not a full routing guide.

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