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

No annotations are provided, so the description carries the behavioral disclosure burden. It does convey that the tool uses a utilization threshold (min_utilization) to filter patterns, linking behavior to the parameter. However, it doesn't disclose return format, ordering, or any side effects — though this appears to be a read-only query tool, and the description doesn't contradict anything.

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

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

Two sentences, front-loaded with the core purpose, followed by the standardization-opportunity framing. No wasted words. Could arguably be trimmed, but it's efficient and informative.

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 a single optional parameter, 100% schema coverage, and no output schema required (a list tool), the description is reasonably complete. However, the context suggests this tool is part of a family (BRE-100 stats/patterns), and the description could benefit from noting whether this is read-only or how the returned set of patterns is structured (list vs count), especially given there's no output schema to fill that 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 description coverage is 100%, so the schema fully documents min_utilization with its default. The description adds the semantic framing of 'min_utilization' meaning 'used by 3+ clients', which slightly enriches the param meaning but the schema already covers the core. The description's 'used by 3+ clients' maps directly to the schema's Default: 3.

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

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

The description states a specific verb+resource ('Get high-value BRE-100 patterns') with a clear scope qualifier ('used by multiple clients'). It distinguishes the tool's purpose by explaining these represent standardization opportunities for FSM solutions. However, it doesn't explicitly differentiate from the sibling reai_bre100_stats tool, which is likely a close relative.

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

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

The description explains that the tool surfaces patterns used by multiple clients and positions them as standardization opportunities, which implies when to use it (when needing high-value, cross-client patterns). It does not explicitly state when NOT to use it or name alternative tools like reai_bre100_stats for different BRE-100 queries.

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. The description clearly establishes this is a read/statistics operation with no side effects implied, which is reasonably transparent. However, it doesn't disclose return format, pagination behavior, or whether the stats are live or cached, which would add value given the zero annotation coverage.

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

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

The description is a single compact sentence that conveys the purpose and the two specific breakdown dimensions. Every word earns its place with zero repetition or filler.

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 zero-parameter statistics tool with no output schema, the description adequately covers what the tool provides: total count plus two breakdown axes. Given the simplicity of the tool, this is reasonably complete. It could benefit from mentioning the return format, but for a simple stats tool with no inputs, the core requirements are met.

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

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

The tool has 0 parameters, so there is no parameter semantics to convey. Per the rubric, 0 params earns a baseline of 4. The description focuses on what data the tool returns rather than explaining parameters, which 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 states a specific verb ('Get') with a clear resource ('BRE-100 pattern registry statistics') and specifies the exact scope of what's returned: total patterns, breakdown by business process and complexity. It clearly distinguishes itself from sibling tools like reai_v10v11_stats (different registry) and reai_bre100_high_value (focused on high-value patterns rather than 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 implies when to use this tool (when statistics about the BRE-100 pattern registry are needed) by stating what data it returns. However, it doesn't explicitly state when not to use it or name alternatives, though the sibling reai_v10v11_stats is implicitly the counterpart for a different version. The breakdown detail ('by business process and complexity') provides useful context for matching specific use cases.

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. It does disclose the tool's output granularity (4 specific files) and that it's a generation/write operation. However, it doesn't mention whether it overwrites existing files, requires any environment setup, or produces side effects. For a generation tool this is acceptable but not rich.

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

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

The description is compact, front-loaded with the action and output, and ends with required inputs. Two sentences, no filler. Slightly verbose on the file enumeration but each file type with its parenthetical purpose is informative.

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 generation tool with 3 fully-documented parameters, the description covers the essentials: what it does, what it produces, and what to provide. However, it doesn't describe return/output behavior beyond file names, or any validation rules. Adequate but leaves an agent guessing about what happens after generation (where files go, how to access them).

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 each parameter well-described (PascalCase format, e.g., values, array structure). The description adds that field definitions are needed but doesn't add meaning beyond the schema. Baseline 3 applies since the schema does the heavy lifting and the description doesn't contradict or extend it.

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

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

The description clearly states the verb ('Generate') and specific resource ('Columnar LPL code package for FSM'). It enumerates the 4 files produced, which is specific and useful. It doesn't explicitly differentiate from sibling tools (which appear to be stats/analysis tools), but the core 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.

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

The description provides inputs needed ('Provide the report name, business class, and field definitions') which implies use cases. It doesn't offer explicit when-to-use vs when-not-to-use guidance or mention alternatives, but the sibling tools are sufficiently different (stats, search, modules) that context is reasonably clear. It lacks exclusions or prerequisites.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It goes beyond a simple 'search' by listing the specific attributes of each pattern (business process, FSM classification, navigation path, etc.), giving the agent a clear idea of what to expect in results. It also notes the registry scale (617+ patterns from 11 client implementations), which adds useful 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 sentences long. The first is a clear action statement, and the second is a compact list of result fields. Every word serves a purpose, with no redundant or vague phrasing.

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 search tool with no output schema, the description effectively communicates the result structure by enumerating the pattern fields. It also provides context about the registry's size and origin. Combined with the schema's parameter documentation, the description is self-sufficient for an agent to understand what the tool does and what it returns.

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 provides complete descriptions for all three parameters (query, limit, threshold), achieving 100% coverage. The tool description does not add parameter-specific guidance beyond what the schema offers, so a baseline 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 opens with a specific action and resource: 'Search the BRE-100 pattern registry for report migration patterns.' It further enumerates the exact fields contained in each pattern, fully clarifying what the tool returns. The inclusion of 'BRE-100' in the name and description clearly distinguishes it from sibling search tools like reai_search_v10v11.

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 clearly indicates the intended use case: searching the BRE-100 pattern registry. It does not explicitly mention when to avoid this tool or alternative tools, but the strong resource specification provides clear context for when this should be used.

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. It discloses the search/retrieval nature and what fields are returned (report codes, names, modules, statuses, FSM paths). However, it doesn't describe edge-case behavior like empty results, fuzzy matching semantics, or whether it returns partial matches on multi-token queries.

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 tight sentences plus useful database-scale context (967+ mappings). Zero filler, front-loaded purpose in the opening clause.

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 search tool with 100% schema coverage and no output schema, the description covers what it returns, purpose, scale, and usage context. Slightly lean on behavioral detail (fuzzy threshold semantics, result sorting) but adequate for a straightforward search tool.

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

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

Schema coverage is 100%, so the schema already documents all 4 parameters well. The description adds modest value by framing results ('report codes, names, modules, statuses'), but doesn't add syntax/format details beyond the schema. Baseline 3 is appropriate since schema does the heavy lifting.

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

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

Clear specific verb+resource: 'Search the V10-V11 mapping database for S3/Lawson to FSM report migration paths.' Distinguishes from siblings — reai_v10v11_stats and reai_v10v11_modules are clearly different (stats vs module listing vs search). The scope (S3/Lawson→FSM migration) is explicitly stated.

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?

States when to use ('Use this to find how legacy S3/Lawson reports map to FSM'), gives concrete query examples, and notes the database scale (967+ mappings across 20+ modules). Does not explicitly exclude alternatives like stats/modules, but sibling differentiation is clear enough from the sibling names and purpose statement.

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 behavioral burden. It states the tool lists modules with report counts but does not disclose sorting order, pagination behavior, whether it returns aggregate data, or any performance implications. For a list-type tool this is a moderate gap.

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

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

The description is two concise sentences with zero waste. It front-loads the core action ('List all FSM modules') and then adds useful context about report counts and use case. 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?

The tool is simple (0 params, no output schema) and the description covers the core purpose well. However, it lacks detail on the return format, ordering of modules, and what 'report counts' specifically means, which an agent might need to interpret the results. Adequate but not rich.

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

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

The tool has 0 parameters and schema coverage is 100% (empty schema), so there is nothing for the description to add regarding parameter semantics. Baseline of 4 for zero-parameter tools applies, and the description correctly mentions the report counts as the output content.

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 action ('List all FSM modules') with a clear resource (V10-V11 mapping database) and additional detail (report counts). It distinguishes from siblings like reai_search_v10v11 (search) and reai_v10v11_stats (stats) by focusing on module listing with report counts.

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 understanding module coverage ('Useful for understanding what modules are covered'), providing clear context. However, it does not explicitly state when NOT to use it versus alternatives like reai_search_v10v11 or reai_v10v11_stats, leaving differentiation to inference.

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

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

With no annotations provided, the description carries the full burden. It clearly signals this is a read-only, non-destructive statistics/overview operation by describing aggregate data output (total reports, paths, module breakdown). It does not, however, disclose response size or format, which would be minor gaps for a zero-parameter stats 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 sentences, zero waste. The first states the action and output; the second provides usage framing. Every clause earns its place with no redundancy.

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

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

For a zero-parameter statistics tool with no output schema and no annotations, the description adequately conveys what the tool does and the nature of its output (aggregate counts and module breakdown). It could mention the output shape or caveats, but for a simple overview tool this is close to 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?

The tool has 0 parameters, so there is nothing to document. Per rubric, 0 params earns a baseline of 4, and the description accurately reflects that no inputs are needed by framing the tool as returning high-level aggregate statistics.

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 ('Get') with a clear resource ('V10-V11 mapping database statistics') and enumerates what it returns (total reports, total navigation paths, module breakdown). It doesn't explicitly distinguish from sibling tools like reai_bre100_stats, but the V10-V11 scope differentiates it from the BRE100 tools.

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

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

The phrase 'Good for getting an overview of the migration knowledge base' provides some usage context, implying this is a high-level overview tool. However, it doesn't explicitly state when to use this versus reai_search_v10v11, reai_v10v11_modules, or other siblings, leaving selection partly to inference.

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