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 describes the action ('Stops the current Ralph loop and removes all state'), which implies a destructive operation, but doesn't detail potential side effects, error conditions, or confirmation requirements. It adds some context but lacks comprehensive 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 two concise sentences with zero waste. The first sentence states the purpose, and the second provides usage guidelines. It's front-loaded and efficiently structured, with every sentence earning its place by adding clear value.

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

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

Given the tool's complexity (simple cancellation with no parameters) and lack of annotations/output schema, the description is reasonably complete. It explains what the tool does and when to use it, though it could benefit from more behavioral details like error handling or confirmation prompts. For a zero-param tool, it covers the essentials well.

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 with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter information, and it appropriately doesn't mention any. A baseline of 4 is applied since no parameters exist to document.

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 specific action ('Cancel') and target resource ('the active Ralph loop'), distinguishing it from siblings like ralph_loop, ralph_iterate, or ralph_status. It precisely defines what the tool does without being vague or tautological.

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 explicitly states when to use this tool ('when you want to manually stop the loop before completion'), providing clear context and distinguishing it from alternatives like letting the loop complete naturally or using other Ralph tools. It gives direct guidance on the appropriate scenario for invocation.

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 describes the tool's behavior: analyzing project structure and suggesting presets, which is useful. However, it lacks details on permissions needed, whether it modifies anything (likely read-only but not stated), error handling, or output format. It adds some context but misses key behavioral traits for a tool with 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 appropriately sized and front-loaded: the first sentence states the core purpose, and the second adds clarifying details with examples. Every sentence earns its place by providing essential information without redundancy or fluff, making it efficient and well-structured.

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

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

Given the tool's complexity (analyzing project structure) and lack of annotations and output schema, the description is moderately complete. It explains what the tool does and gives examples, but for a detection/suggestion tool with no structured output, it should ideally describe the return format or behavior more explicitly. It's adequate but has gaps in contextual detail.

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 description coverage is 100% (empty schema). With no parameters, the baseline is 4, as there's nothing for the description to compensate for. The description doesn't need to add param info, so it meets expectations without extra effort.

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

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

The description clearly states the tool's purpose: 'Detect which tool presets are available for the current project' and 'Analyzes the project structure and suggests relevant tool presets.' It specifies the verb (detect/analyze) and resource (tool presets) with concrete examples (javascript-test, javascript-lint). However, it doesn't explicitly distinguish from siblings like 'ralph_list_tools' which might list tools rather than detect presets based on project structure.

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 context: when you need to discover tool presets based on project analysis, such as checking for package.json. However, it doesn't explicitly state when to use this tool versus alternatives like 'ralph_list_tools' or 'ralph_get_template', nor does it provide exclusions or prerequisites. The guidance is implied but not comprehensive.

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 specifies that it 'Returns the full template configuration' and lists included details, which helps clarify the output. However, it does not address other behavioral aspects like error handling, permissions needed, or whether it's a read-only operation, leaving gaps in transparency.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by a bulleted list of return details that is efficient and easy to scan. Every sentence and bullet point adds value without redundancy, making it appropriately sized and well-structured.

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

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

Given the tool's complexity (a read operation with one parameter) and lack of annotations and output schema, the description is moderately complete. It explains what is returned but not the format or structure of the output, and it misses behavioral context like error cases. For a tool with no output schema, more detail on return values would improve completeness.

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

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

The input schema has 100% description coverage, with the single parameter 'template_id' well-documented as 'The ID of the template to retrieve'. The description does not add any additional semantic details beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high schema coverage without extra value.

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

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

The description clearly states the tool's purpose as 'Get details of a specific template' with a verb ('Get') and resource ('template'), making it immediately understandable. However, it does not explicitly differentiate from its sibling 'ralph_list_templates', which likely lists templates rather than retrieving details for a specific one, though this distinction is implied 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 provides no guidance on when to use this tool versus alternatives, such as 'ralph_list_templates' for listing templates or other tools for template operations. It lacks context on prerequisites, exclusions, or specific scenarios, leaving usage unclear beyond the basic purpose.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool creates a commit, implying a write operation, but doesn't disclose critical traits like permissions required, whether it's destructive (e.g., overwrites data), error handling, or rate limits. The mention of 'auto_commit' adds some context but is insufficient for a mutation tool with 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 appropriately sized and front-loaded: the first sentence states the core purpose, and the second provides usage guidance. Every sentence earns its place by adding relevant information without redundancy, making it efficient and easy to parse.

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

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

Given the tool's complexity (a mutation operation with no annotations and no output schema), the description is moderately complete. It covers the purpose and basic usage but lacks details on behavioral traits, error cases, or return values. For a git commit tool, more context on what 'current iteration' means or how it interacts with other git operations would improve completeness.

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

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

The input schema has 100% description coverage, with one parameter 'message' documented as 'Commit message.' The description adds minimal value beyond this, mentioning 'custom message' but not elaborating on format, length constraints, or examples. Since schema coverage is high, the baseline score of 3 is appropriate as the description doesn't significantly enhance parameter understanding.

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

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

The description clearly states the tool's purpose: 'Create a git commit for the current iteration' and 'manually create a commit with a custom message.' This specifies the verb (create), resource (git commit), and context (current iteration). However, it doesn't explicitly distinguish this from sibling tools like ralph_git_context or ralph_git_status, which might handle git operations differently.

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 clear context on when to use this tool: 'Use this to manually create a commit with a custom message' and notes that 'If auto_commit is enabled, commits are created automatically.' This implies usage when manual control is needed, but it doesn't explicitly name alternatives or specify when-not-to-use scenarios beyond the auto_commit note.

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

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It describes what the tool does (shows recent commits) but lacks details on traits like whether it's read-only, any rate limits, error handling, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior beyond basic functionality.

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

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

The description is appropriately sized and front-loaded: the first sentence states the core purpose, and the second elaborates without redundancy. Every sentence adds value by clarifying scope ('Ralph iteration commits') and utility ('providing context'). There is zero waste, making it efficient and well-structured.

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

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

Given no annotations, no output schema, and a simple input schema, the description is incomplete. It explains the tool's function but lacks context on behavioral traits, output details, or usage scenarios. For a tool that retrieves historical data, more information on what 'context' entails (e.g., commit messages, timestamps) would improve completeness.

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

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

The input schema has 1 parameter with 100% description coverage, providing default and type info. The description adds no parameter-specific semantics beyond implying 'recent' commits, which aligns with the 'count' parameter. Since schema coverage is high, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

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

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

The description clearly states the tool's purpose: 'Get context from recent Ralph commits' and 'Shows recent Ralph iteration commits from git history'. It specifies the verb ('get', 'shows') and resource ('recent Ralph commits', 'Ralph iteration commits'), making the purpose understandable. However, it doesn't explicitly differentiate from siblings like 'ralph_git_commit' or 'ralph_git_status', which would require 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?

The description provides no guidance on when to use this tool versus alternatives. It mentions 'providing context about what was done in previous iterations', which implies usage for historical context, but doesn't specify scenarios, prerequisites, or exclusions. Without explicit when/when-not instructions or named alternatives, it falls short of higher scores.

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 describes what information is returned but does not cover critical aspects such as whether this is a read-only operation (implied but not stated), potential errors (e.g., if not in a git repo), performance characteristics, or output format. This leaves gaps in understanding how the tool behaves beyond basic functionality.

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

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

The description is front-loaded with a clear purpose statement ('Get git status and diff summary'), followed by a bulleted list that efficiently details the output. Every sentence and bullet point adds specific value without waste, making it easy to scan and understand quickly.

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

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

Given the tool's moderate complexity (providing git status information) and lack of annotations and output schema, the description is partially complete. It covers what information is retrieved but does not address error handling, output structure, or dependencies (e.g., requires git to be installed). For a tool with no structured metadata, more behavioral context would improve completeness.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on the tool's output. This aligns with the baseline expectation for tools with no parameters, providing clear value without redundancy.

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

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

The description clearly states the tool's purpose with specific verbs ('Get git status and diff summary') and resources ('git status'), and distinguishes it from siblings like ralph_git_commit (which commits changes) and ralph_git_context (which likely provides broader context). The bullet points further detail what information is retrieved, making the purpose explicit and differentiated.

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 by listing what information is shown (e.g., current branch, modified files), suggesting it's for checking git repository state. However, it lacks explicit guidance on when to use this tool versus alternatives like ralph_git_context or ralph_status, and does not specify prerequisites or exclusions (e.g., only works in a git repository).

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 full burden for behavioral disclosure. It lists what data is included in the report but doesn't address critical behavioral aspects: whether this is a read-only operation (implied but not stated), if it requires specific permissions, how data is formatted/returned, if there are rate limits, or if it's resource-intensive. The description adds some context about report content but leaves major behavioral traits unspecified.

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 perfectly structured and concise. The first sentence clearly states the core purpose, followed by a bulleted list that efficiently details the report contents without unnecessary elaboration. Every sentence earns its place, and the information is front-loaded with the most important statement first.

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

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

Given the tool's complexity (historical reporting with multiple data types), lack of annotations, and absence of an output schema, the description does an adequate but incomplete job. It specifies what data fields are included, which helps compensate for missing output schema, but doesn't address behavioral aspects like permissions, performance, or format. For a tool with no structured metadata, more comprehensive disclosure would be beneficial.

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 with 100% schema description coverage, so the schema already fully documents the lack of inputs. The description appropriately doesn't waste space discussing parameters that don't exist. It focuses instead on what the tool returns, which is valuable context given the absence of an output schema.

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

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('detailed iteration history report'), making it immediately understandable. It distinguishes from siblings like ralph_status or ralph_git_context by focusing on comprehensive historical data rather than current state or specific git operations. However, it doesn't explicitly contrast with all siblings, keeping it from a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like ralph_status (likely showing current status), ralph_git_context (likely git-specific info), and ralph_iterate (likely performing iterations), there's clear potential for overlap, but the description offers no explicit when/when-not instructions or named 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?

With no annotations provided, the description carries full burden and adds substantial behavioral context beyond basic function. It discloses multiple behavioral traits: tracking iteration history, running external tools, analyzing progress, detecting stagnation, creating git commits, and deciding loop continuation. However, it doesn't mention error handling, rate limits, or authentication needs, leaving some gaps for a complex iteration 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by a bulleted list of functions and enhanced features. Every sentence adds value, though the bulleted list could be slightly more concise. There's minimal waste, but the structure isn't perfectly optimized (e.g., the enhanced features list overlaps somewhat with the main functions).

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

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

Given the tool's complexity (iteration management with multiple functions), no annotations, and no output schema, the description provides good contextual coverage. It explains key behaviors like progress analysis, stagnation detection, and git integration. However, it doesn't describe the return value or error responses, which is a gap since there's no output schema to compensate.

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 already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain format details for 'last_output' or clarify relationships between parameters). The baseline of 3 is appropriate since the 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?

The description clearly states the tool's purpose: 'Process the next iteration of a Ralph loop with enhanced tracking.' It specifies the verb ('process') and resource ('Ralph loop iteration'), but doesn't explicitly differentiate from siblings like 'ralph_loop' (which likely starts the loop) or 'ralph_cancel' (which stops it). The enhanced tracking features are mentioned but not contrasted with sibling functionality.

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

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

The description implies usage context: 'After completing work on the current iteration, call this tool...' This suggests it should be used iteratively within a loop workflow. However, it doesn't explicitly state when NOT to use it or name alternatives among siblings (e.g., when to use 'ralph_loop' vs. 'ralph_iterate'). The guidance is present but incomplete for sibling differentiation.

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

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

No annotations are provided, so the description carries the full burden. It mentions that templates are 'pre-built prompts and configurations' with 'suggested settings and external tools,' but doesn't disclose behavioral traits like whether this is a read-only operation, if it requires authentication, rate limits, pagination, or the format of the returned list. For a list tool with zero annotation coverage, 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?

The description is appropriately sized and front-loaded: the first sentence states the core purpose. The bulleted list of template categories adds useful context without being verbose, and the final sentence about template contents is relevant. However, the bulleted list could be slightly condensed, and some sentences (e.g., 'And more...') are 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?

Given the tool's low complexity (one optional parameter) but lack of annotations and output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., list format, template identifiers), behavioral aspects like error handling, or how it integrates with sibling tools. For a list tool in a server with many related tools, more contextual guidance is needed.

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 adds no parameter-specific information beyond what the input schema provides. The schema has 100% coverage with one optional parameter 'category' described as 'Filter by category (optional).' The description lists example categories (e.g., 'REST API development'), which implicitly relates to the 'category' parameter but doesn't explain its usage or semantics. Baseline 3 is appropriate since the 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?

The description clearly states the tool's purpose: 'List all available Ralph loop templates' with a specific verb ('List') and resource ('Ralph loop templates'). It distinguishes from siblings like 'ralph_get_template' (which retrieves a specific template) by emphasizing 'all available' templates. However, it doesn't explicitly contrast with 'ralph_list_tools', which might list tools rather than templates.

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

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

The description provides no guidance on when to use this tool versus alternatives. It mentions templates include 'suggested settings and external tools,' but doesn't specify scenarios for using this list tool over others like 'ralph_get_template' for detailed info or 'ralph_loop' to apply templates. No exclusions or prerequisites are stated.

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 discloses that the tool lists presets and includes non-applicable ones, which is useful behavioral context. However, it lacks details on output format, pagination, or error handling, leaving gaps for a tool with no output schema.

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. The first sentence states the purpose, and the second adds critical scope information, making it front-loaded and efficiently structured.

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

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

Given the tool's simplicity (0 parameters, no annotations) but lack of output schema, the description is adequate but incomplete. It explains what the tool does and its scope, but without annotations or output schema, it should ideally mention what the return value looks like (e.g., a list of preset names/descriptions) to be fully 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 with 100% schema description coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, earning a baseline score of 4 for not adding unnecessary information beyond the schema.

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

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

The description clearly states the specific action ('List all available external tool presets') and resource ('tool presets'), distinguishing it from siblings like ralph_list_templates (which lists templates) and ralph_detect_tools (which likely detects applicable tools). It explicitly mentions the scope includes presets 'regardless of whether they're applicable to the current project,' which adds precision.

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 clear context for when to use this tool: to see all tool presets, including those not applicable to the current project. However, it does not explicitly state when not to use it or name alternatives (e.g., ralph_detect_tools might filter for applicable ones), which prevents a perfect score.

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 lists features like iteration history tracking, Git integration, and stagnation detection, which add useful context beyond basic functionality. However, it doesn't clarify critical behaviors such as whether this is a long-running process, what permissions or resources it requires, or how errors are handled, leaving significant gaps for a tool with complex operations.

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 well-structured and appropriately sized, starting with the core purpose, explaining the methodology, listing features in bullet points, and ending with a usage tip. Most sentences add value, though the bulleted list could be slightly condensed without losing clarity.

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

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

Given the tool's complexity (starting iterative loops with multiple features) and the absence of both annotations and an output schema, the description is moderately complete. It covers the purpose and features but lacks details on execution flow, error handling, or output expectations, which are crucial for an agent to use it effectively in this 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?

The input schema has 100% description coverage, so the schema fully documents all 6 parameters. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain 'prompt' or 'template_id' further). According to the rules, with high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.

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

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

The description clearly states the tool's purpose: 'Start a Ralph Wiggum iterative development loop' and explains it's 'a development methodology based on continuous AI agent loops.' It specifies the core function (starting iterative loops) but doesn't explicitly differentiate from siblings like 'ralph_iterate' or 'ralph_status' beyond mentioning templates.

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 some usage context by mentioning 'Use ralph_list_templates to see available templates,' which implies a prerequisite step. However, it lacks explicit guidance on when to use this tool versus alternatives like 'ralph_iterate' or 'ralph_run_tools,' leaving the agent to infer based on the 'start' action.

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

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

No annotations are provided, so the description carries the full burden. It describes what the tool does (run presets) and lists examples, but lacks details on execution behavior (e.g., sequential vs. parallel runs, error handling, output format, or side effects). It adds some context but not comprehensive behavioral disclosure.

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

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

The description is well-structured and front-loaded, starting with the core purpose, followed by a bulleted list of presets, and ending with a usage tip. Every sentence adds value without redundancy, making it efficient and easy to scan.

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 moderate complexity (executing external tools), no annotations, and no output schema, the description does well by listing presets and referencing detection. However, it could improve by mentioning execution details (e.g., output handling or errors). It's mostly complete but has minor gaps in behavioral 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 100%, so the schema already documents the 'presets' parameter. The description adds value by listing specific preset names (e.g., javascript-test, python-lint) and clarifying their purposes, which goes beyond the schema's generic description. With 0 parameters beyond the schema, baseline is 4, and the description enhances understanding.

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

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

The description clearly states the verb ('run') and resource ('external tool presets') with specific examples like test runners and linters. It distinguishes from siblings by focusing on execution rather than detection (ralph_detect_tools) or listing (ralph_list_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?

It explicitly states when to use this tool (for running presets) and when to use an alternative (ralph_detect_tools to see available presets). It provides clear context by listing available presets and referencing sibling tools for complementary actions.

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 effectively describes the tool's behavior as a read-only status retrieval with detailed progress insights (e.g., stagnation detection, estimated iterations), covering output content comprehensively. It doesn't mention performance aspects like rate limits or authentication needs, but for a status tool, the disclosed behavior is sufficient.

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 front-loaded with the core purpose in the first sentence, followed by a bulleted list that efficiently details the specific insights provided. Every bullet point adds value by clarifying the output content, with no redundant or verbose language, making it highly scannable 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?

Given the tool's complexity (monitoring loops with progress analysis) and lack of output schema, the description provides strong completeness by detailing all returned insights in the bullet list. It covers functional aspects well but doesn't address non-functional details like error handling or performance, which is a minor gap for a status tool without annotations.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter information, focusing instead on the output insights. This aligns with the baseline expectation for zero-parameter tools, where the description should explain what the tool returns rather than inputs.

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 specific verb 'Get' and resource 'current status of the Ralph loop with progress insights', distinguishing it from siblings like ralph_cancel (termination), ralph_iterate (execution), and ralph_history (past data). It goes beyond a simple status check by specifying the comprehensive insights provided.

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 context by listing what it shows (e.g., active loop status, progress analysis), suggesting it's for monitoring ongoing loops. However, it lacks explicit guidance on when to use this versus alternatives like ralph_history (which might show past loops) or ralph_git_status (which focuses on git state), and doesn't mention prerequisites or exclusions.

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