Gemini MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
Multiple tools have significant overlap and unclear boundaries. For example, 'analyze_codebase', 'analyze_intelligence', 'code_analyze', and 'codereview_expert' all involve code analysis with overlapping purposes. Similarly, 'debug_analysis' and 'debug_master' both handle debugging, and 'refactor_genius' and 'refactor_suggestions' both focus on refactoring. This creates confusion about which tool to select for specific tasks, as descriptions don't clearly differentiate their scopes.
Naming Consistency5/5
All tool names follow a consistent pattern: 'mcp__gemini__' prefix followed by descriptive snake_case phrases. The naming convention is uniform throughout, with no mixing of styles or deviations. This predictability makes it easy to identify tools as part of the same server and understand their general purpose from the naming structure.
Tool Count2/5
With 23 tools, the count feels excessive for the server's purpose of AI-assisted development and analysis. Many tools appear to be specialized variants of core functions (e.g., multiple code analysis and debugging tools), suggesting fragmentation rather than a well-scoped set. This could overwhelm agents and lead to decision paralysis when selecting among similar options.
Completeness4/5
The tool set covers a broad range of AI-assisted development tasks, including code analysis, debugging, refactoring, project planning, security auditing, and collaboration. While there are some gaps (e.g., no explicit tools for code generation beyond APIs/components or version control operations), the surface is largely comprehensive for its domain, allowing agents to handle most workflows without dead ends.
Average 2.8/5 across 23 of 23 tools scored. Lowest: 2.2/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under GPL 3.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
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 mentions 'AI insights' but doesn't explain what this entails—such as whether it's a read-only scan, requires authentication, has rate limits, or produces destructive changes. The term 'analysis' is vague, failing to clarify the tool's operational traits beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that is front-loaded with the core purpose. It avoids unnecessary words, though it could be more structured (e.g., by separating purpose from context). It earns its place by conveying the essential idea without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity implied by 'comprehensive codebase analysis' and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the analysis includes, the format of results, or behavioral aspects like performance or limitations. For a tool with three parameters and no structured output, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with clear descriptions for all three parameters (path, reportType, includeAnalysis). The description adds no additional meaning beyond the schema, such as explaining parameter interactions or usage examples. According to the rules, with high schema coverage, the baseline is 3, which is appropriate here as the schema adequately documents the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Comprehensive codebase analysis with AI insights' states a general purpose but lacks specificity. It mentions 'codebase analysis' as the resource and 'comprehensive' as a vague scope, but doesn't specify what actions are performed (e.g., scanning, reviewing, summarizing) or how it differs from siblings like 'code_analyze' or 'codereview_expert'. This is a tautology that restates the name's concept without concrete differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 multiple sibling tools related to code analysis (e.g., 'code_analyze', 'codereview_expert', 'debug_analysis'), there is no indication of context, prerequisites, or exclusions. This leaves the agent without direction for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions analyzing for 'quality and issues', but doesn't specify what types of issues (e.g., bugs, security, performance), the depth of analysis, output format, or any constraints like rate limits or authentication needs. This leaves significant gaps for a tool with no structured 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's appropriately sized for a basic tool definition, though it could be more front-loaded with critical details given the lack of annotations and sibling tool complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity implied by sibling tools and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the analysis returns, how results are structured, or any behavioral traits, making it inadequate for an agent to understand the tool's full context and use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 three parameters ('analysis_type', 'code', 'language') with descriptions. The description adds no additional meaning beyond what's in the schema, such as examples of analysis types or language support. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Analyze code for quality and issues' states a clear verb ('analyze') and resource ('code'), but it's vague about what 'quality and issues' specifically entails. It doesn't distinguish this tool from sibling tools like 'mcp__gemini__analyze_codebase', 'mcp__gemini__codereview_expert', or 'mcp__gemini__quality_guardian', which likely have overlapping purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With multiple sibling tools that appear related to code analysis (e.g., 'analyze_codebase', 'codereview_expert', 'quality_guardian'), the description lacks any context about specific use cases, prerequisites, or comparisons to help an agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states 'create' which implies a write/mutation operation, but doesn't disclose behavioral traits like permissions needed, whether tasks are saved permanently, rate limits, or what the output looks like. For a creation tool with zero annotation coverage, this is a significant gap 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's appropriately sized for a tool with three parameters and gets straight to the point. However, it could be more front-loaded with additional context, but as-is it's concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 creation tool with behavioral implications, the description is incomplete. It doesn't explain what 'create' entails operationally, what format the tasks are in, or any error conditions. For a tool that likely generates structured output from requirements, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 three parameters with descriptions. The description adds no additional meaning about parameters beyond implying that 'requirements' input leads to task creation. With high schema coverage, the baseline is 3 even without param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Create project tasks from requirements' states a clear verb ('create') and resource ('project tasks'), but it's vague about what 'project tasks' are and how they're generated. It doesn't distinguish from sibling tools like 'planner_pro' or 'team_orchestrator' which might have overlapping functionality. The purpose is understandable but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools focused on planning, analysis, and generation, there's no indication of this tool's specific context or prerequisites. The description implies usage for task creation from requirements, but 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'continuous monitoring' and 'predictive metrics' but doesn't disclose behavioral traits like whether this is a read-only analysis tool, if it modifies data, what permissions are required, how results are delivered, or any rate limits. The description is too vague about actual behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient phrase that communicates the core function without unnecessary words. However, it's somewhat front-loaded with jargon ('predictive quality metrics') that might not be immediately clear without more context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 no output schema, the description is inadequate. It doesn't explain what the tool returns, how monitoring works in practice, or what 'predictive quality metrics' means operationally. The gap between the vague description and the detailed input schema creates uncertainty about tool behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 parameters well. The description adds no specific meaning about parameters beyond the general monitoring context. It doesn't explain how 'alert_thresholds' relate to 'predictive quality metrics' or what 'quality_aspects' like 'code_quality' entail in practice.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool performs 'continuous quality monitoring and trend analysis with predictive quality metrics', which gives a general purpose but lacks specificity about what exactly is being monitored or analyzed. It doesn't clearly distinguish this from sibling tools like 'analyze_codebase' or 'precommit_guardian' that might also relate to quality assessment.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. With many sibling tools related to analysis and quality (e.g., 'analyze_codebase', 'precommit_guardian', 'secaudit_quantum'), the description offers no context about appropriate use cases, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'AI conversation' and 'model selection', but lacks behavioral details such as whether this initiates a new chat or continues an existing one, authentication needs, rate limits, or response format. This is inadequate for a tool with potential conversational state or model-specific behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient phrase with zero wasted words. It's appropriately sized for a basic tool and front-loaded with the core concept, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of AI conversation tools (which may involve state, model nuances, or output formats), no annotations, no output schema, and a vague description, this is incomplete. The agent lacks sufficient information to understand how to effectively invoke or interpret results from this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 three parameters (message, model, context) with their types and requirements. The description adds no additional meaning beyond the schema, such as explaining the 'model' options or how 'context' influences the conversation, resulting in the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'AI conversation with model selection' states the general purpose (AI conversation) and mentions model selection, but it's vague about what distinguishes this tool from its many siblings. It doesn't specify the verb (e.g., 'initiate', 'continue') or the exact resource (e.g., 'chat session', 'AI response'), making it less specific than ideal.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 20+ sibling tools including 'mcp__gemini__chat_plus', there's no indication of differences in context, capabilities, or prerequisites, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. 'AI-powered debugging assistance' implies analysis and suggestions but doesn't disclose behavioral traits like whether it modifies code, requires specific permissions, has rate limits, or what kind of output to expect. This leaves significant gaps for a debugging tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient phrase with zero wasted words. It's appropriately sized and front-loaded, though the brevity contributes to the vagueness in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is inadequate for a debugging tool with 3 parameters. It doesn't explain what the tool returns, how it behaves, or when to use it, leaving the agent with insufficient context to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing clear documentation for all parameters. The description adds no additional meaning beyond the schema, which already defines 'code', 'error', and 'language' with descriptions. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'AI-powered debugging assistance' states the general purpose (debugging) but is vague about the specific action and resource. It doesn't distinguish from sibling tools like 'debug_master' or 'analyze_codebase', leaving ambiguity about what makes this tool unique.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With multiple sibling tools including 'debug_master' and various analysis tools, the description offers no context about appropriate use cases, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'collaboration' and 'coordination' but doesn't explain what the tool actually does—whether it creates a session, manages tasks, or facilitates communication. Critical details like permissions, side effects, or output format are missing, leaving significant gaps for a tool with team coordination implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core idea without unnecessary words. It's appropriately sized for the tool's complexity, with no wasted phrases or redundancy, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of team coordination and the lack of annotations and output schema, the description is incomplete. It doesn't clarify the tool's behavior, expected outcomes, or how it integrates with sibling tools. For a multi-parameter tool with no structured output, more context on what the tool returns or how it operates is needed to be adequately helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 parameters (coordination_level, project, team_members, workflow_type). The description adds no additional meaning beyond the schema, such as explaining how parameters interact or their impact on collaboration. With high schema coverage, the baseline is 3, as the description doesn't compensate but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Multi-developer collaboration with shared AI contexts and workflow coordination' states a general purpose but lacks specificity. It mentions collaboration and coordination but doesn't specify what action the tool performs (e.g., 'orchestrate,' 'initiate,' 'manage'). It distinguishes from siblings like 'ai_chat' or 'analyze_codebase' by focusing on team coordination rather than individual tasks, but the verb is vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives is provided. The description implies usage for team-based projects, but it doesn't specify scenarios, prerequisites, or exclusions. Without context on when to choose this over siblings like 'create_project_tasks' or 'planner_pro', the agent must infer based on general terms.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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. While it lists three analysis capabilities, it doesn't describe what 'deep code analysis' entails, what format the output takes, whether this is a read-only operation, what permissions might be required, or any limitations. The description is insufficient for understanding the tool's behavior beyond its stated capabilities.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single phrase that efficiently communicates the core capabilities. Every word earns its place by specifying the analysis type and three key features. No wasted words or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'deep code analysis' means in practice, what the output format is, or how the three mentioned capabilities relate to the parameters. The agent would need to guess about the tool's behavior and output structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 five parameters with descriptions. The description mentions 'performance prediction' and 'business impact assessment' which map to two of the boolean parameters, but doesn't add meaningful semantic context beyond what's already in the schema. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs 'deep code analysis' with three specific capabilities: performance prediction, business impact assessment, and trend analysis. It provides a specific verb ('analyze') and resource ('code'), though it doesn't explicitly differentiate from sibling tools like 'analyze_codebase' or 'code_analyze' which appear to have overlapping purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 multiple sibling tools that appear related to code analysis (analyze_codebase, code_analyze, codereview_expert, etc.), there's no indication of what makes this tool distinct or when it should be preferred over those alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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. While it mentions 'automatic model switching' and 'context optimization', it doesn't explain what these mean operationally, what permissions are required, whether conversations are stored, rate limits, or what 'collaborative' entails. For a chat tool with potentially complex behavior, this leaves significant gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient phrase that communicates the core functionality without unnecessary words. It's appropriately sized and front-loaded with the key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a chat tool with advanced features like model switching and context optimization, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'advanced collaborative' means, how model switching works, what 'context optimization' entails, or what the tool returns. The agent would have significant uncertainty about the tool's behavior and outputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 additional meaning about parameters beyond what's in the schema descriptions. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 'Advanced collaborative AI chat with automatic model switching and context optimization', which specifies the verb (chat) and key features. However, it doesn't explicitly distinguish this from sibling tools like 'mcp__gemini__ai_chat', leaving some ambiguity about when to choose one over the other.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 multiple sibling chat-related tools (e.g., 'mcp__gemini__ai_chat'), there's no indication of what makes this 'plus' version different or when it should be preferred over basic chat tools or other specialized tools in the list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 behavioral traits like 'weighted voting, confidence scoring, and conflict resolution,' but does not disclose critical details such as how models are accessed, potential rate limits, authentication needs, or what happens in case of conflicts. This leaves significant gaps in understanding the tool's operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads key features without unnecessary words. Every phrase ('advanced multi-model consensus,' 'weighted voting,' etc.) adds value, 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity implied by 'advanced' features and no output schema, the description is incomplete. It lacks details on return values (e.g., consensus result format, confidence scores), error handling, or prerequisites. Without annotations or output schema, this leaves the agent with insufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 parameters (question, models, min_confidence, weight_strategy). The description adds no additional meaning beyond the schema, such as explaining the impact of different weight strategies or confidence thresholds. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 performing 'advanced multi-model consensus' with specific features like weighted voting, confidence scoring, and conflict resolution. It uses a specific verb ('consensus') and resource ('multi-model'), but does not explicitly differentiate from sibling tools like 'mcp__gemini__thinkdeep_enhanced' or 'mcp__gemini__chat_plus', which might have overlapping AI-related functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 lacks context such as specific scenarios (e.g., complex decision-making, high-stakes queries) or comparisons to sibling tools, leaving the agent to infer usage based on the name and features alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'execution simulation, fix validation, and step-by-step analysis' but fails to detail critical aspects like whether this is a read-only or mutating operation, permission requirements, rate limits, or what the output entails. For a debugging tool with potential side effects, this is a significant gap 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads key features without unnecessary elaboration. It avoids redundancy and waste, making it appropriately concise for a tool with a clear name and schema support.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a debugging tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, error handling, and differentiation from siblings. While the schema covers parameters, the description fails to provide sufficient context for safe and effective use in a multi-tool environment.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, meaning all parameters are documented in the input schema. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or usage examples. Given the high coverage, a baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract from the schema's documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 'Advanced debugging with execution simulation, fix validation, and step-by-step analysis,' which specifies the verb (debugging) and key features. However, it doesn't explicitly distinguish this tool from its sibling 'mcp__gemini__debug_analysis,' which appears to be a similar debugging tool, leaving some ambiguity about their differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 the sibling 'mcp__gemini__debug_analysis' or other debugging-related tools. It lacks explicit context, prerequisites, or exclusions, offering only a high-level feature list without practical usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'ROI analysis and cost-benefit calculations' but doesn't specify what the tool actually returns (e.g., numerical results, formatted reports, recommendations), whether it performs simulations or uses historical data, or any limitations like computational constraints. This leaves significant gaps in understanding how the tool behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded with the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how results are formatted, any assumptions in the calculations, or error conditions. The agent lacks crucial information about the tool's behavior and outputs despite the complex financial analysis it performs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'ROI analysis and cost-benefit calculations for technical decisions with business impact quantification.' It specifies the action (analysis/calculations) and resource (technical decisions with business impact). However, it doesn't explicitly differentiate from sibling tools like 'performance_predictor' or 'planner_pro' which might have overlapping financial analysis capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 doesn't mention any prerequisites, constraints, or compare it to sibling tools that might handle similar analyses. The agent must infer usage from the purpose alone without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'validation' but doesn't specify what kind of validation, whether it generates code or documentation, what the output format is, or any constraints like rate limits or permissions required. This leaves significant gaps in understanding the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's appropriately sized and front-loaded, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of generating API endpoints (a code generation task), no annotations, and no output schema, the description is incomplete. It lacks details on behavioral aspects like output format, error handling, or dependencies, which are crucial for effective tool use in this context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already documents all parameters with descriptions. The description adds no additional meaning beyond what's in the schema, such as explaining how parameters interact or providing examples. The baseline score of 3 reflects adequate coverage by the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 'Generate REST API endpoints with validation', which specifies the action (generate) and the resource (REST API endpoints). It distinguishes itself from sibling tools like 'generate_component' or 'analyze_codebase' by focusing specifically on API generation, though it doesn't explicitly differentiate from all siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. There's no mention of prerequisites, scenarios where it's appropriate, or comparisons to other tools in the list like 'generate_component' or 'analyze_codebase' that might overlap in functionality.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 generates UI components but doesn't describe what that entails—e.g., whether it creates full files, snippets, or templates; if it requires specific inputs beyond parameters; or any limitations like token counts or quality. This leaves significant gaps for a generative tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Generate UI components for React, Vue, Angular, Svelte'. It's front-loaded with the core action and resource, with no wasted words, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a generative tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavior, output format, error handling, or constraints, which are crucial for an AI agent to use it effectively. The high schema coverage helps but doesn't compensate for missing behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 parameters (name, framework, features, styling) with descriptions and defaults. The description adds no additional meaning beyond implying these parameters relate to UI component generation, matching the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate UI components for React, Vue, Angular, Svelte'. It specifies the verb ('Generate') and resource ('UI components'), and lists the supported frameworks. However, it doesn't explicitly differentiate from sibling tools like 'generate_api' or 'refactor_genius', which might also involve code generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 doesn't mention prerequisites, context for choosing among the frameworks, or how it differs from other code-generation siblings like 'generate_api'. Usage is implied by the name and purpose but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral disclosure. It mentions 'AI-powered' and 'optimization recommendations', hinting at analysis and suggestions, but doesn't specify output format, whether it's read-only or has side effects, accuracy limitations, or computational requirements. This is inadequate for a prediction tool with potential impact.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient phrase that front-loads key information ('AI-powered performance prediction and optimization recommendations with capacity planning'). It wastes no words and directly communicates the core function without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of performance prediction and no annotations or output schema, the description is insufficient. It doesn't explain what the tool returns (e.g., predictions, recommendations, reports), how results are formatted, or any behavioral constraints. For a tool with 4 parameters and potential decision-making impact, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing baseline documentation for all 4 parameters. The description adds no additional parameter semantics beyond the schema's descriptions of 'load_scenarios', 'metrics', 'prediction_horizon', and 'system'. It doesn't explain relationships between parameters or provide examples, so it meets but doesn't exceed the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs 'AI-powered performance prediction and optimization recommendations with capacity planning', which specifies the action (prediction/optimization) and resource (performance/capacity). It distinguishes from most siblings focused on code analysis, chat, or project management, though it doesn't explicitly differentiate from similar analysis tools like 'analyze_codebase' or 'system_status'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, scenarios where it's appropriate, or when to choose other tools like 'analyze_codebase' for code performance or 'system_status' for current metrics. Usage is implied through the tool's name and description but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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. While 'interactive project planning' suggests a generative/mutation operation, the description doesn't clarify what 'interactive' means operationally, whether it creates persistent artifacts, requires specific permissions, has rate limits, or what format the output takes. The mention of 'templates, dependency detection, and progress tracking' hints at functionality but lacks behavioral specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('interactive project planning') followed by three key features. Every word earns its place with zero redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool actually produces (a plan document? task list? Gantt chart?), how the 'interactive' aspect works, or provide enough behavioral context for safe invocation. The feature list hints at capabilities but lacks operational specifics needed for proper tool selection and use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 with basic descriptions. The tool description adds no parameter-specific information beyond what's in the schema - it doesn't explain how parameters like 'complexity' or 'project_type' affect the planning process, or provide examples of valid 'timeline' formats. The baseline 3 is appropriate when schema does the documentation work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 'interactive project planning' with specific features (templates, dependency detection, progress tracking), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'create_project_tasks' or 'team_orchestrator' that might have overlapping functionality in the project management domain.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 multiple sibling tools in the same server (like 'create_project_tasks', 'team_orchestrator', 'consensus_advanced'), there's no indication of this tool's specific context, prerequisites, or when it's preferable to other planning or project management tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'auto-fix suggestions' and 'Git integration' but doesn't specify whether fixes are applied automatically or suggested, what Git operations are performed (e.g., staging changes), error handling, or output format. For a validation tool with potential side effects, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads key capabilities. Every word earns its place by highlighting advanced validation, auto-fix suggestions, and Git integration. However, it could be slightly more structured by separating core functionality from integration aspects.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter validation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes 'validation', what types of issues are detected, how results are returned, or what 'Git integration' entails. The agent lacks critical context about the tool's behavior and outputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 adds no parameter-specific information beyond what's in the schema - it doesn't explain relationships between parameters (e.g., how 'auto_fix' interacts with 'block_on_issues') or provide usage examples. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 'Advanced pre-commit validation with auto-fix suggestions and Git integration', which includes specific verbs (validation, auto-fix) and resources (pre-commit, Git). It distinguishes from most siblings by focusing on pre-commit validation rather than analysis, chat, or generation. However, it doesn't explicitly differentiate from 'quality_guardian' which might have overlapping quality checking functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 doesn't mention prerequisites (e.g., Git repository context), when to choose it over 'quality_guardian' or 'codereview_expert', or specific scenarios where pre-commit validation is appropriate versus post-commit analysis tools. The agent must infer usage from the name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only lists capabilities without detailing behavioral traits. It mentions 'automated testing, rollback capabilities, and safety validation' but doesn't explain how these work, what 'safety validation' entails, or any limitations like computational cost or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise single sentence with no wasted words, front-loading the core purpose ('Intelligent code refactoring') followed by key features. Every phrase adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex refactoring tool with 6 parameters, no annotations, and no output schema, the description is insufficient. It lacks details on behavioral traits, output format, error conditions, and how the listed capabilities interact, leaving significant gaps for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 all 6 parameters. The description adds no parameter-specific information beyond the tool's general purpose, maintaining the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 'Intelligent code refactoring' with specific capabilities (automated testing, rollback, safety validation). It distinguishes from sibling tools like 'refactor_suggestions' by implying more comprehensive functionality, though not explicitly contrasting them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like 'refactor_suggestions' or 'code_analyze'. The description implies it's for refactoring with safety features but doesn't specify scenarios, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'AI-powered' but does not detail traits such as response format, potential limitations, rate limits, or authentication needs. For a tool that likely involves complex AI processing, this lack of context 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an AI-powered refactoring tool, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and differentiation from siblings, leaving the agent with insufficient context to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already documents all parameters (code, goals, language) with descriptions. The tool description adds no additional meaning beyond what the schema provides, such as examples or usage nuances, resulting in a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 AI-powered refactoring suggestions,' which specifies the action (get suggestions) and the domain (refactoring). It distinguishes from siblings like 'analyze_codebase' or 'debug_analysis' by focusing on refactoring, but does not explicitly differentiate from 'refactor_genius,' which appears to be a similar tool, making it slightly less specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'refactor_genius' and 'code_analyze' that might overlap, there is no indication of context, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions the tool performs 'audit' functions but doesn't describe what the audit entails operationally - whether it's read-only or makes changes, what permissions are required, how long it takes, what format results come in, or any rate limits. For a complex security audit tool with 5 parameters, this is inadequate 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that efficiently lists the three main functions. Every word earns its place, and it's front-loaded with the core purpose. There's zero waste or redundancy in this compact description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex security audit tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the audit produces, how results are returned, what the scope limitations are, or any prerequisites. The description covers the 'what' but not the 'how' or 'what results to expect', leaving significant gaps for an agent trying to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 with basic descriptions. The tool description doesn't add any parameter-specific information beyond what's in the schema. It mentions the three audit functions which map to some parameters (compliance_standards, include_quantum, threat_modeling) but doesn't provide additional context about how these parameters interact or affect the audit process.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 an 'Advanced security audit' with three specific functions: vulnerability prediction, compliance checking, and quantum-readiness assessment. It uses specific verbs and resources, but doesn't explicitly differentiate from sibling tools like 'analyze_codebase' or 'codereview_expert' which might also analyze code/security aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 many sibling tools that might overlap in security/code analysis (e.g., 'analyze_codebase', 'codereview_expert', 'precommit_guardian'), there's no indication of when this specialized audit tool is preferred or what distinguishes it from those alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'comprehensive system status' and 'performance metrics', which implies a read-only operation, but doesn't explicitly state whether this requires special permissions, what format the output takes, whether it's real-time or cached data, or any rate limits. For a system status tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core purpose without unnecessary words. It's appropriately sized for a tool with three optional parameters and no complex behavioral requirements. Every word earns its place in conveying the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a system status tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'system status' encompasses beyond mentioning 'capabilities and performance metrics', doesn't describe the return format or structure, and provides no context about what systems are being monitored. Given the complexity of system monitoring and the lack of structured output documentation, the description should provide more complete context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with all three parameters clearly documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. According to the rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Comprehensive system status showing all capabilities and performance metrics'. It specifies the verb ('showing') and resource ('system status'), and distinguishes from siblings by focusing on system monitoring rather than AI chat, code analysis, or other specialized tasks. However, it doesn't explicitly differentiate from potential similar monitoring tools that might exist.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 doesn't mention prerequisites, appropriate contexts, or when other tools might be more suitable. Given the sibling tools are all specialized AI/development tools, this stands out as a system monitoring tool, but no explicit usage guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'step validation, logical consistency checking, and progress tracking' which gives some behavioral context, but doesn't address critical aspects like whether this is a read-only operation, what the output format might be, computational requirements, or potential limitations of the extended reasoning process.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single phrase listing key capabilities. It's front-loaded with the core purpose and wastes no words. Every element ('extended AI reasoning', 'step validation', etc.) earns its place by specifying distinct functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex reasoning tool with 4 parameters and no output schema, the description is insufficient. It doesn't explain what 'extended AI reasoning' means in practice, what format the reasoning output takes, how progress tracking manifests, or what constitutes successful validation. Without annotations or output schema, users lack critical information about what to expect from this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 4 parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain how parameters like 'domain' or 'thinking_depth' affect the extended reasoning process, nor does it provide examples of valid values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function as 'extended AI reasoning' with specific capabilities like step validation, logical consistency checking, and progress tracking. It uses a specific verb ('reasoning') and resource ('AI'), but doesn't explicitly distinguish it from sibling tools like 'mcp__gemini__ai_chat' or 'mcp__gemini__consensus_advanced' which might also involve reasoning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 doesn't mention what makes it different from other reasoning/analysis tools in the sibling list, nor does it specify appropriate contexts or prerequisites for its use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'actionable fixes, risk scoring, and automated suggestions,' which gives some behavioral context, but lacks details on permissions, rate limits, output format, or whether it modifies code. For a tool with no annotations, this is insufficient to fully understand its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads key information: 'Multi-perspective code review with actionable fixes, risk scoring, and automated suggestions.' It's appropriately sized with zero wasted words, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a code review tool with 5 parameters and no output schema, the description is somewhat complete but has gaps. It outlines the tool's purpose and features, but without annotations or output schema, it doesn't fully cover behavioral aspects or return values. It's minimally adequate but could benefit from more context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 parameters. The description doesn't add any meaning beyond what's in the schema—it doesn't explain how parameters like 'review_focus' or 'generate_fixes' affect the review process. Baseline 3 is appropriate when the schema handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 'Multi-perspective code review with actionable fixes, risk scoring, and automated suggestions.' It specifies the verb ('code review') and key features, though it doesn't explicitly distinguish it from sibling tools like 'mcp__gemini__code_analyze' or 'mcp__gemini__quality_guardian' that might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 many sibling tools like 'mcp__gemini__code_analyze' and 'mcp__gemini__quality_guardian' that might handle code analysis, there's no indication of when this specific code review tool is preferred or what distinguishes it from other options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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