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

With no annotations, the description carries the full burden, but it only describes the basic operation and a quality tip. It does not disclose whether the call is read-only, what happens with malformed input, whether code is returned in a certain format, or any 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?

The description is two sentences with no filler and front-loads the core purpose. The second sentence is concise but slightly vague about what 'instructions and context' refers to.

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 4-parameter tool with 0% parameter documentation and no annotations, the description is too thin. It covers prefix/suffix but leaves language and timeout semantics unexplained, and with an output schema present the return shape is covered but the invocation context is not.

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 only adds meaning for prefix/suffix ('partial code'). It does not explain the language or timeoutMs parameters, and the advice to provide instructions/context in English does not map to any explicit 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 names a specific action, 'Complete partial code from prefix/suffix,' which clearly identifies the function and distinguishes it from siblings like code_generate or code_refactor. The tool's role as a fill-in-the-middle completion operation is unambiguous.

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

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

The input scenario is implied by 'from prefix/suffix,' and the second sentence gives a quality tip about providing English instructions and context. However, it never explicitly says when to prefer this over ruvltra_code_generate or other siblings, nor does it state any 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 must carry the full behavioral disclosure burden. The description only restates the tool's purpose and hints at input quality; it does not disclose possible side effects, output format, process requirements, or limitations. For an explainer tool, this is a relatively low-risk operation, but with zero annotation coverage the description still fails to provide meaningful 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 short, front-loaded, and contains no fluff. The first sentence captures the essential purpose, and the second sentence provides actionable guidance for better results. It is concise, though it sacrifices informativeness for brevity, which is acceptable given the simple tool scope.

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 4 parameters, no annotations, and 0% schema description coverage, the description is not complete enough. It does not clarify parameter options, timeout semantics, or how this tool fits alongside similar code-related siblings. An output schema exists, so return values need not be explained, but the missing parameter and usage context leaves an agent with meaningful ambiguity.

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 overage is 0%, and the description does not explain any of the 4 parameters. While 'audience' is loosely implied by 'for a given audience' and 'language' is hinted by the English advice, there is no description of what values these accept. 'timeoutMs' remains completely unexplained, including what it controls and its unit. The description does not compensate for the missing schema documentation.

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

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

The description states a clear verb+resource action: 'Explain code clearly for a given audience.' This distinguishes it at least implicitly from sibling tools like code_generate, code_review, and code_refactor, since 'explain' is a distinct purpose. It does not explicitly contrast with siblings, but the core purpose is unambiguous enough for an agent to recognize that this tool explains rather than modifie or generates code.

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 offer some usage guidance by saying 'For best output quality, provide instructions and context in English.' This implies the user should supply relevant context and that English input is preferred. However, it does not specify when to use this tool over the sibling code tools, nor does it provide any exclusions or alternative relationships.

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 of behavioral disclosure, but it merely restates the tool's name ('Fix code') without explaining side effects, whether it returns patched code or a diff, or how it handles incomplete errors. It adds little beyond the tool name and does not inform the agent about the operation's nature beyond 'fix'.

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 only two sentences with no filler. It front-loads the primary behavior and keeps the quality tip separate. It is appropriately sized for the tool's simplicity, though it could have used the space to clarify parameters.

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 zero annotations and zero schema parameter descriptions, the description is insufficient. It does not state what the output contains (output schema exists, so that helps), what language/timeout defaults are, or any prerequisites. The agent would have to infer most invocation details from the tool name and parameter names alone.

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%, so the description must compensate. It maps roughly to code ('code'), error ('using error details'), and instruction ('optional guidance'), but does not explain language or timeoutMs. Most parameters remain semantically undocumented in both schema and description.

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

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

The description states a specific action ('Fix code') and the core input ('error details' and 'optional guidance'), which clearly distinguishes it from generation, review, refactor, explain, and other siblings. However, it does not explicitly contrast with ruvltra_code_refactor, which also modifies code, leaving some ambiguity between fix and refactor.

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 a quality tip ('provide instructions and context in English') but gives no guidance on when to choose this tool over alternatives like ruvltra_code_refactor or ruvltra_code_review. There is no mention of exclusions or scenarios where a sibling would be more appropriate.

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 carrries full burden of behavioral disclosure. It only says it generates code and recommends English; it doesn't disclose side effects, whether filePath writes to disk, execution behavior, or error conditions.

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

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

Two sentences with no filler. The action is front-loaded and the English-language hint is a single concise, useful sentence.

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?

Output schema covers return format, but the tool has 7 parameters, no annotations, and no sibling differentiation. The description is too thin to let an agent reliably select or configure the tool correctly for non-trivial generation tasks.

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%, so the description must compensate for the seven parameters. It maps 'instruction' and 'context' and hints at language via English, but leaves filePath, maxTokens, timeoutMs, and temperature unexplained.

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 ('Generate') and resource ('code') and names the inputs 'instruction and context.' However, with siblings like code_complete and parallel_generate, it doesn't distinguish why this tool is chosn over those alternatives.

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

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

It says to provide instructions and context in English for best output quality, which is a useful usage hint, but it doesn't explain when to use this tool versus ruvltra_code_complete or ruvltra_parallel_generate, nor provide 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It communicates the key guarantee of preserving behavior, but omits other important behaviors such as whether it modifies files in place, how it handles unsupported languages, rate limits, or whether results are returned asynchronously. Relying on 'refactor' alone leaves too much unstated.

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

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

The description is two sentences with no filler. The core action is front-loaded, and the English-context tip is brief and actionable. Every word contributes.

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 4 parameters, no annotations, and 0% schema coverage, the description leaves too many gaps: language format, timeout semantics, instruction expectations, and operational side effects are all unaddressed. The output schema helps with return shape, but the input contract is incomplete.

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%, so the description must compensx for undocumented parameters. It makes 'code' and 'instruction' partially understandable, but 'language' and 'timeoutMs' receive no meaningful explanation. The tip about providing English instructions adds some context, but not enough for four parameters.

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

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

The description uses a specific verb and resource: 'Refactor code while preserving behavior.' This clearly distinguishes the tool from siblings like ruvltra_code_fix, ruvltra_code_translate, and ruvltra_code_review by emphasizing behavior-preserving transformation.

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 whenever code needs restructuring without changing behavior, but it does not explicitly state when to prefer this over siblings or provide exclusion criteria. The English-language tip is useful for output quality but not for tool selection.

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

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

No annotations are present, so the description carries the behavioral disclosure burden. It does disclose the scope of the review, which is useful, and the English-context note hints at how output quality is influenced. It does not state whether the tool is read-only, how results are returned, or any side effects, though the presence of an output schema reduces the need to describe 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?

The description is just two sentences, front-loads the main purpose, and contains no filler. The second sentence about English context is concise and relevant to output quality.

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 four-parameter tool with no annotations, the description leaves important gaps: optional parameters are undocumented, no guidance distinguishes this from sibling review-like tools, and behavioral notes are minimal. The output schema covers return values, which helps, but the missing parameter and routing context makes the description incomplete.

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%, so the description must compensate by explaining the four parameters. It does not: 'code' is obvious from the schema, but focus, language, and timeoutMs are never described, and the 'provide instructions and context in English' advice does not map clearly to any parameter. An agent cannot determine valid values or meaning for the optional parameters.

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

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

The description clearly identifies the verb and resource: it reviews code, and it names five concrete review dimensions (bugs, security, performance, correctness, maintainability). However, it does not explicitly contrast itself with sibling review-like tools such as ruvltra_swarm_review, so differentiation is inferred rather than 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?

The description implies the tool is for code review and advises providing English instructions/context for best output quality. It never explicitly states when to prefer this tool over siblings like ruvltra_code_test, ruvltra_code_fix, or ruvltra_swarm_review, so selection guidance remains 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 provided, the description must carry the full burden of behavioral disclosure. It only states the outcome and a quality tip, without revealing whether tests are executed, whether code is modified, what the response format is, or any side effects. This is a significant gap for a tool with no annotation safety hints.

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

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

The description is appropriately short and front-loaded: the first sentence states the core action, and the second sentence adds a useful quality note. There is no filler, but the brevity contributes to under-specification, so it does not earn a 5.

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 four input parameters, no schema descriptions, no annotations, and no explanation of inputs, this description is far too minimal. It lacks parameter semantics, output behavior, supported languages/frameworks, and error conditions. The existence of an output schema does not compensate for the missing input guidance.

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%, so the description needed to explain the parameters. It does not mention code, language, framework, or timeoutMs at all, and the vague tip about 'instructions and context' is not mapped to any parameter. An agent cannot infer parameter meaning from this description.

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

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

The description clearly states a specific action: generate tests for provided code. The focus on 'tests' distinguishes it from siblings like ruvltra_code_generate, ruvltra_code_review, and ruvltra_code_refactor, so an agent can select it based on purpose alone.

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 tool should be used when tests for existing code are needed, but it does not explicitly say when not to use it or mention alternatives. The English-language tip is a quality hint rather than a usage condition.

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 reveals only that the tool translates code; it says nothing about whether code is transmitted to an external service, how timeoutMs behaves, whether sourceLanguage is auto-detected when omitted, or what side effects (if any) occur. For a tool that likely processes user-submitted code externally, this is a significant omission.

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?

Sixteen words across two sentences with the purpose front-loaded and no repetition of schema fields or fluff. Both sentences earn their place despite the second being a minor quality tip.

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?

Minimum viable: an agent can assemble a correct basic call (code + targetLanguage), and the presence of an output schema covers return-value expectations. However, optional-parameter semantics (timeoutMs, sourceLanguage detection) and behavioral traits are absent, and with no annotations the description is the sole source of guidance beyond the schema.

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

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

With 0% schema description coverage, the description must compensate for the schema's silence, but it only loosely implies the roles of code, sourceLanguage, and targetLanguage via the phrase 'translate code between programming languages.' timeoutMs is entirely unexplained, and there is no guidance on whether sourceLanguage is required or auto-detected.

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 (translate) and resource (code) with a clear outcome (moving between programming languages). The operation is unmistakably distinct from the sibling set (generate, review, refactor, explain, test, fix, complete), so an agent can select this tool correctly without inspecting the schema.

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 when-to-use or when-not-to-use guidance is given, and no alternatives are named; an agent must infer usage from the tool's name and purpose. The tip to provide instructions and context in English is a quality suggestion, not usage-selection guidance, and it references inputs (instructions/context) that do not appear as parameters in the schema.

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 disclose behavioral characteristcs itself. It does reveal concurrency through the worker pool and hints at quality expectations for English input, but it omits key side effects of a file-generating tool: overwrite behavior, permissions requirements, failure handling, or creation semantics for multiple files. This is a significant transparency gap for a write operation.

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

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

Two sentences, both purposeful: the first communicates the core operation and scope, the second gives a practical quality tip. No redundant phrasing or filler exists, and the main action is front-loaded.

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

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

For a tool that creates multiple files, the description is incomplete. It does not clarify whether files are overwritten, how partial failures are handled, whether parallel generation has limits, or what side effects occur outside the workspace. An output schema can cover return shape, but it cannot substitute for missing behavioral and safety context.

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

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

Schema description coverage is 0%, so the description carries the burden of explaining parameters. It adds a useful clue that instruction and context should be in English, but it does not define filePath, language, timeoutMs, or the relationship between tasks and generated files. The parameter names are suggestive but not adequately documented.

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 clear action ('Generate multiple files') and resource ('via the worker pool'), and the concurrency aspect distinguishes it from the singular generation siblings. It does not explicitly name an alternative or contrast with tools like ruvltra_code_generate, so it stops short of full sibling differentiation.

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 when-to-use guidance is provided, and no alternatives or exclusions are mentioned. The tip about using English instructions/context is style advice, but it does not tell an agent when to choose this tool over ruvltra_code_generate or other siblings. Usage context must be inferred entirely from the name and first phrase.

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 carries the full burden of explaining side effects. It only mentions that scaling respects configured min/max bounds; it does not disclose whether the operation mutates running work, whether changes are immediate or reversible, or any permission implications. This is thin for a state-changing operation.

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

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

The description is a single sentence with no filler. It front-loads the action, names the resource, and embeds the key constraint efficiently.

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 scaling tool with an output schema present, the description is minimally viable: it states the operation and the constraint. However, without annotations or any behavioral/side-effect context, the overall picture is incomplete for an agent deciding whether this call is safe or how it affects ongoing work.

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%, so the description must compensate. It succeeds in linking the lone 'target' parameter to the intended worker pool size and adds the meaningful constraint that the value must fall within configured min/max. An agent can infer that 'target' is the desired new pool size even though the parameter name is not explicitly restated.

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 ('Scale') with a clear resource ('worker pool size') and a visible constraint ('within configured min/max'). No sibling tool overlaps with this operation, so an agent can distinguish this from the code-generation and status tools without needing to inspect further.

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 purpose itself implies when to use it: an agent should call this when it needs to resize the worker pool. However, the description provides no explicit when-to-use guidance, no exclusions, and no mention of alternatives, leaving the decision mostly 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?

No annotations are provided, so the description carries the burden. 'Return' strongly implies a read-only operation with no side effects, and 'all workers or a specific worker' clarifies input behavior. However, it does not disclose auth requirements, rate limits, or other behavioral traits.

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

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

The description is a single, front-loaded sentence with no filler. Every word contributes to clarifying the tool's purpose and scope.

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 optional parameter and an output schema, the description covers the essential purpose and scope. It is slightly thin on explicit usage guidance and behavioral caveats, but the tool's simplicity and the presence of an output schema mitigate those 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 coverage is 0%, but the description compensates by indicating that results can cover all workers or be narrowed to a specific worker. This adds meaning beyond the bare 'workerId' string property, though it does not name the parameter directly or specify its format.

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 ('Return'), names the resource ('SONA learning statistics'), and specifies the scope ('all workers or a specific worker'). This clearly distinguishes it from the code-generation and worker-management sibling 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 description implies when to use the tool: whenever SONA learning statistics are needed, optionally filtered by a worker. However, it gives no explicit guidance about when not to use it or which alternative might be more appropriate.

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 of behavioral disclosure. It implies a read-only status retrieval but does not state that it is non-mutating, nor does it mention any potential side effects, response latency, or auth requirements. Minimal additional behavioral context is given.

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 one short sentence with no filler words. It front-loads the action and clearly names all three status categories, making it efficient and easily scannable.

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 read-only status tool with an output schema present, the description is largely complete. The only gap is the lack of explicit usage context relative to sibling tools such as 'ruvltra_sona_stats', which could have created a minor ambiguity, but the core behavior is fully conveyed.

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 zero parameters, so the description is not required to explain parameter meaning. The baseline of 4 for no-parameter tools applies, and the description does not introduce confusion.

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 'Return' and the resource 'server, worker, and backend runtime status', which distinguishes it from the code-focused sibling tools. However, it does not explicitly contrast itself with 'ruvltra_sona_stats', another stats-like sibling, so differentiation is mostly 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?

No guidance is given on when to use this tool versus alternatives, nor any exclusions. The description only states what it does, leaving the agent to infer when it is appropriate.

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 disclosure burden. It adds genuinely useful behavioral traits: parallel execution, an 8-perspective ceiling, and a dependency on English input quality. However, it is silent on whether execution is asynchronous or long-running — the timeoutMs parameter and the ruvltra_status sibling suggest a job model — and on return shape or failure 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?

Two short sentences, roughly 20 words, with the action front-loaded and no filler. The English-language tip is secondary but brief and earns its place as a quality note.

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 5-parameter parallel tool with zero annotations, 0% schema description coverage, and no routing to its sibling ruvltra_code_review, the description is incomplete. It covers the core purpose and perspective count, but leaves sibling differentiation, async/long-running behavior, and the meaning of most parameters unaddressed.

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%, so the description must compensate, but it only illuminates the 'perspectives' concept and the 8-agent cap that mirrors maxAgents' maximum. The other four parameters — code format, language values, maxAgents semantics, and timeoutMs behavior — receive no explanatory help whatsoever.

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 ('Run'), a resource ('multi-perspective parallel code reviews'), and a concrete cap ('up to 8 perspectives'). The qualifier 'multi-perspective parallel' implicitly separates it from the sibling ruvltra_code_review, but it never names that sibling or spells out the distinction, which keeps it from a 5.

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

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

There is no guidance on when to pick this tool over the closely related siblings ruvltra_code_review or ruvltra_parallel_generate. The only advice — 'provide instructions and context in English' — is an input-quality tip, not a selection rule, so it does not help an agent route between alternatives.

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