Which integrations are available for this server?

Integrates with Windsurf IDE (from Codeium) to provide codebase analysis capabilities through the MCP protocol Leverages Google Gemini's large context window to perform comprehensive code analysis, security audits, and codebase exploration Runs as a Node.js-based MCP server to enable AI agents to analyze codebases across various programming languages

How do I use Gemini CLI Orchestrator MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Gemini CLI Orchestrator MCP plan analysis for understanding the authentication flow in this project" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Gemini CLI Orchestrator MCP

by dnnyngyen

Overview Schema Related Servers Score Discussions

JavaScript

Hybrid

Gemini CLI Orchestrator v2.0 - Sequential Thinking MCP

A metaprompting-first MCP server that guides AI agents through intelligent, multi-step codebase analysis using Google's Gemini AI.

🧠 Core Philosophy

Don't build intelligence into the system. Build prompts that elicit intelligence from the agent.

This tool doesn't wrap or replace your thinking - it guides you to think more systematically about complex codebase analysis through structured workflows.

Related MCP server: Gemini Bridge

🚀 Quick Start

Step 1: Install Gemini CLI

npm install -g @google/gemini-cli

Step 2: Authenticate (one-time setup)

gemini auth login

Step 3: Install this tool

npm install

Step 4: Test it works

node gemini-collaboration-guide.mjs

Step 5: Try your first analysis (using Claude Code CLI)

# Start with planning a simple analysis
gemini_plan_analysis goal="Understand this project's main architecture"

# Then craft a specific prompt for the first step
gemini_craft_prompt step_description="Analyze package.json and README for project overview" context="Starting fresh analysis"

That's it! Authentication is handled automatically by the gemini CLI.

How It Works

Instead of trying to be "smart" about your analysis, this tool provides four simple tools that guide you through systematic analysis:

🎯 `gemini_plan_analysis(goal)`

Breaks down complex analysis goals into a step-by-step plan. Be specific about what you want to understand and why.

Example:

gemini_plan_analysis goal="Audit authentication system for security vulnerabilities"

🔍 `gemini_craft_prompt(step_description, context)`

Helps you write better prompts for Gemini by suggesting effective commands and context for each analysis step.

Example:

gemini_craft_prompt step_description="Analyze JWT token handling" context="Found 3 auth endpoints, focusing on token security"

🔄 `gemini_iterate_analysis(current_understanding, iteration_goal)`

Guide iterative analysis using observe-think-act cycles for dynamic problem-solving.

Example:

gemini_iterate_analysis current_understanding="Found potential SQL injection in login" iteration_goal="Investigate if other endpoints have similar issues"

📊 `gemini_synthesize_findings(steps_summary, synthesis_goal)`

Combine insights from multiple analysis steps into comprehensive understanding.

Example:

gemini_synthesize_findings steps_summary="Analyzed auth system, found 2 vulnerabilities, tested 5 endpoints" synthesis_goal="Create security audit report with prioritized fixes"

Example Workflow

Here's how you'd conduct a security audit using the orchestrator:

# 1. Plan the overall analysis
gemini_plan_analysis goal="Analyze authentication system for security vulnerabilities"
# → Outputs: 5-stage analysis plan with specific steps

# 2. Execute each planned step
gemini_craft_prompt step_description="Identify authentication mechanisms" context="Security audit of auth system"
# → Outputs: Optimized gemini command to analyze auth files
# → You run: cat src/auth/**/* | gemini -m gemini-2.5-flash -p "..."

# 3. Dive deeper if needed
gemini_iterate_analysis current_understanding="Found JWT + session auth" iteration_goal="Check for common auth vulnerabilities"
# → Outputs: ReAct loop guidance for systematic vulnerability testing

# 4. Synthesize final report
gemini_synthesize_findings steps_summary="Analyzed 3 auth mechanisms, found 2 issues" synthesis_goal="Security audit report with fixes"
# → Outputs: Comprehensive synthesis strategy for final report

🎯 Key Benefits

✅ Simplified Authentication - Uses your existing gemini CLI setup
✅ True Metaprompting - Guides your intelligence, doesn't replace it
✅ Multi-Step Analysis - Break complex problems into manageable steps
✅ Flexible Workflows - Adapt your approach based on what you discover
✅ Direct Gemini Integration - No wrapper complexity or auth overhead

📚 Documentation

How It Works - Deep dive into the architecture and metaprompting philosophy
Contributing Guide - How to contribute to the project
Code of Conduct - Community standards and expectations

🤝 Contributing

We welcome contributions! Please see our Contributing Guide and Code of Conduct for details on how to get involved.

MCP Configuration

🚨 Important Note for Existing Users

If you've previously installed this MCP server (especially if you used gemini-orchestrator.mjs), you may have cached configurations. The filename was renamed to gemini-collaboration-guide.mjs for clarity. See "Handling Previous Installations" below if you encounter setup issues.

Claude Code CLI (Recommended)

The easiest and most reliable way to configure the MCP server:

# Add the server using Claude Code's built-in command
claude mcp add gemini-collaboration-guide node /path/to/your/gemini-cli-orchestrator/gemini-collaboration-guide.mjs

# Verify it was added
claude mcp list

# Test it's working in Claude Code
/mcp

Important:

Use the full absolute path to the gemini-collaboration-guide.mjs file
The server name must be exactly gemini-collaboration-guide
Restart Claude Code after adding the server

Handling Previous Installations / Filename Changes

If you previously configured this MCP server or encounter issues like "Status: ✘ failed", follow these steps:

# 1. Remove any old configurations
claude mcp remove gemini-cli-orchestrator    # Old name
claude mcp remove gemini-orchestrator        # Another old name  
claude mcp remove gemini-collaboration-guide # Clean slate

# 2. Verify clean state
claude mcp list

# 3. Add with correct filename (use YOUR actual path)
claude mcp add gemini-collaboration-guide node /Users/dannynguyen/gemini-cli-orchestrator/gemini-collaboration-guide.mjs

# 4. Restart Claude Code completely

# 5. Test it works
/mcp

Manual Configuration (Alternative)

If you prefer manual JSON configuration:

Claude Desktop - Config file: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "gemini-collaboration-guide": {
      "command": "node",
      "args": ["/absolute/path/to/your/gemini-cli-orchestrator/gemini-collaboration-guide.mjs"]
    }
  }
}

Cursor IDE - Config file: .cursor/mcp.json

{
  "mcpServers": {
    "gemini-collaboration-guide": {
      "command": "node",
      "args": ["/absolute/path/to/your/gemini-cli-orchestrator/gemini-collaboration-guide.mjs"]
    }
  }
}

Windsurf IDE - Config file: windsurf_config.json

{
  "mcpServers": {
    "gemini-collaboration-guide": {
      "command": "node",
      "args": ["/absolute/path/to/your/gemini-cli-orchestrator/gemini-collaboration-guide.mjs"],
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

Troubleshooting

If /mcp shows "No MCP servers configured" or tools aren't available:

Check for filename/configuration conflicts (Most Common Issue)
- Follow the "Handling Previous Installations" section above
- The file was renamed from gemini-orchestrator.mjs to gemini-collaboration-guide.mjs

Clean setup process:

# Remove all old configurations
claude mcp remove gemini-cli-orchestrator
claude mcp remove gemini-orchestrator  
claude mcp remove gemini-collaboration-guide

# Add with correct filename
claude mcp add gemini-collaboration-guide node /absolute/path/to/gemini-collaboration-guide.mjs

Verify the file exists:

ls -la /path/to/your/gemini-cli-orchestrator/gemini-collaboration-guide.mjs

Test the server can start:

node /path/to/your/gemini-cli-orchestrator/gemini-collaboration-guide.mjs --help

Restart Claude Code completely after any configuration changes

Verify success:

claude mcp list    # Should show: gemini-collaboration-guide
/mcp              # Should show the four tools

Common Errors:

Status: ✘ failed → Usually wrong filename (use gemini-collaboration-guide.mjs)
File does not exist → Check absolute path is correct
Tools not appearing → Restart Claude Code after configuration

Advanced Usage Examples

Security Audit Workflow

1. Start: "Comprehensive security audit of web application"
2. Step 1: "Identify all authentication mechanisms" → @src/auth/ @middleware/
3. Step 2: "Analyze API endpoint security" → @src/api/ @src/routes/
4. Step 3: "Review data validation and sanitization" → @src/validation/ @src/models/
5. Step 4: "Check for common vulnerabilities (OWASP)" → @src/
6. Conclude: "Generate prioritized security report with remediation steps"

Performance Analysis Workflow

1. Start: "Identify performance bottlenecks in React application"
2. Step 1: "Analyze component rendering patterns" → @src/components/
3. Step 2: "Review state management efficiency" → @src/store/ @src/hooks/
4. Step 3: "Check for unnecessary re-renders" → @src/components/
5. Step 4: "Analyze bundle size and imports" → @package.json @webpack.config.js
6. Conclude: "Provide performance optimization recommendations"

Architecture Review Workflow

1. Start: "Review microservices architecture for scalability"
2. Step 1: "Understand service boundaries" → @services/ @docker-compose.yml
3. Step 2: "Analyze inter-service communication" → @src/api/ @src/clients/
4. Step 3: "Review data flow and dependencies" → @src/models/ @src/schemas/
5. Step 4: "Evaluate error handling and resilience" → @src/middleware/ @src/utils/
6. Conclude: "Recommend architectural improvements for scalability"

File Pattern Examples

Use glob patterns to focus analysis on relevant files:

# Language-specific patterns
@**/*.js @**/*.ts          # JavaScript/TypeScript
@**/*.py                   # Python
@**/*.go                   # Go
@**/*.rs                   # Rust
@**/*.java                 # Java

# Framework-specific patterns
@src/components/ @src/hooks/     # React
@src/models/ @src/views/         # MVC frameworks
@src/services/ @src/controllers/ # Service layer

# File type patterns
@package.json @*.config.js       # Configuration
@**/*.test.js @**/*.spec.js      # Tests
@README.md @docs/                # Documentation

Requirements

Node.js 18+
Google Gemini CLI installed and authenticated
Basic understanding of glob patterns

Troubleshooting

"Command not found: gemini"

npm install -g @google/gemini-cli

"Authentication failed"

gemini auth login

"No files found"

# Check your file patterns match your project structure
# Use broader patterns like @src/ or @**/*.js

What's New in v2.0

🎉 Complete architectural redesign - From complex wrapper to simple orchestrator
🎉 Eliminated authentication complexity - Uses native gemini CLI auth
🎉 True metaprompting approach - Guides intelligence instead of replacing it
🎉 Sequential thinking workflows - Multi-step analysis with state management
🎉 Simplified setup - No complex configuration or environment variables

Philosophy

This tool embodies the metaprompting principle: trust agent intelligence over system complexity.

Instead of trying to be smart about your analysis, it provides simple tools that guide you to think systematically about complex problems. The result is more thoughtful analysis and better insights.

Perfect for leveraging Gemini's massive context window through intelligent, structured workflows.

Available Tools

4 tools

gemini_craft_promptB

Helps you write better prompts for Gemini by suggesting effective commands and context.

This tool guides you on crafting effective bash commands for gemini collaboration:

How to write focused, specific prompts that get better results
How to select the right files for each analysis step
How to structure your bash commands for optimal collaboration
How to interpret and build on gemini's responses

You execute the suggested commands yourself using your bash tools.

ParametersJSON Schema

Name	Required	Description	Default
`step_description`	Yes	What you want to analyze in this step. Be specific about your focus and intent.
`context`	No	Context from previous steps or additional context about your codebase that should inform this step.

TDQS

B3.1/5.0

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 describes the tool as a guide that suggests commands, which implies it's a read-only advisory tool, but it doesn't explicitly state this or cover other behavioral aspects like whether it requires specific permissions, has rate limits, or what the response format looks like. The description adds some context about executing commands yourself, but overall behavioral transparency is limited.

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

Conciseness3/5

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

The description is moderately concise but could be more front-loaded. It starts with a clear purpose statement, but the bulleted list and final sentence add detail that, while informative, might be verbose. Some sentences, like 'How to interpret and build on gemini's responses,' could be integrated more tightly. Overall, it's adequate but not optimally structured for quick scanning.

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 tool's complexity (advisory with 2 parameters), no annotations, and no output schema, the description provides a basic overview but lacks depth. It explains the tool's role in prompt crafting and execution context, but doesn't cover expected outputs, error handling, or integration with sibling tools. For a tool without structured behavioral data, this leaves gaps in completeness.

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 clear documentation for both parameters (step_description and context). The description doesn't add any specific parameter semantics beyond what the schema provides, such as examples or usage tips for the parameters. Given the high schema coverage, a baseline score of 3 is appropriate, 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.

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: to help write better prompts for Gemini by suggesting effective commands and context. It specifies the verb 'guides you on crafting' and resource 'effective bash commands for gemini collaboration', making it clear what the tool does. However, it doesn't explicitly distinguish this from sibling tools like gemini_iterate_analysis or gemini_synthesize_findings, which prevents 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.

Usage Guidelines3/5

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

The description implies usage context through phrases like 'guides you on crafting effective bash commands for gemini collaboration' and 'You execute the suggested commands yourself using your bash tools,' suggesting this is for prompt crafting guidance rather than direct execution. However, it lacks explicit guidance on when to use this tool versus alternatives like gemini_plan_analysis or gemini_synthesize_findings, leaving usage somewhat ambiguous.

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

gemini_iterate_analysisB

Guide iterative analysis using observe-think-act cycles for dynamic problem-solving.

This tool helps you implement iterative analysis patterns that adapt based on findings:

How to structure reasoning before taking action
How to observe and interpret results effectively
How to reflect on findings and adjust strategy dynamically
How to decide when to continue iterating vs. when to conclude
How to handle unexpected results and pivot approaches

Based on ReAct patterns used by Gemini CLI itself for complex problem-solving workflows.

ParametersJSON Schema

Name	Required	Description
`current_understanding`	Yes	What you currently understand about the problem/codebase from previous analysis steps.
`iteration_goal`	Yes	What specific aspect you want to investigate in this ReAct iteration.
`unexpected_findings`	No	Optional: Any unexpected results from previous iterations that require strategy adjustment.

TDQS

B3.4/5.0

Behavior3/5

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 adds some context by describing the iterative process (observe-think-act cycles) and mentions handling unexpected results and pivoting approaches, which hints at adaptive behavior. However, it lacks details on permissions, rate limits, or specific output formats, leaving gaps for a tool with no annotations.

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 appropriately sized and front-loaded, starting with a clear purpose statement followed by bullet points that elaborate on key aspects. Each sentence adds value, such as explaining the iterative pattern and referencing ReAct, with minimal redundancy. It could be slightly more concise but is well-structured.

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 (iterative analysis tool), no annotations, and no output schema, the description provides a good overview of the process but lacks specifics on what the tool returns or how it integrates with siblings. It covers the 'how' of iterative analysis but doesn't fully compensate for the missing structured data, leaving some contextual gaps.

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 thoroughly. The description doesn't add any additional meaning or context for the parameters beyond what's in the schema (e.g., it doesn't explain how 'current_understanding' or 'iteration_goal' relate to the iterative cycles). 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: to 'guide iterative analysis using observe-think-act cycles for dynamic problem-solving.' It specifies the verb ('guide iterative analysis') and the method ('observe-think-act cycles'), though it doesn't explicitly differentiate from sibling tools like gemini_plan_analysis or gemini_synthesize_findings. The purpose is specific but lacks sibling comparison.

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

Usage Guidelines3/5

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

The description implies usage through phrases like 'for dynamic problem-solving' and 'Based on ReAct patterns... for complex problem-solving workflows,' suggesting it's for adaptive, multi-step analysis. However, it doesn't explicitly state when to use this tool versus alternatives like gemini_plan_analysis or gemini_synthesize_findings, nor does it provide exclusions or clear prerequisites.

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

gemini_plan_analysisB

Breaks down complex analysis goals into a step-by-step plan. Gives you the roadmap, you execute the steps.

This tool helps you plan multi-step analysis by guiding your thinking about:

How to decompose complex goals into manageable steps
What order to approach different aspects of your analysis
How to build understanding incrementally
How to use each step to inform the next

You execute the suggested gemini commands yourself using your bash tools.

ParametersJSON Schema

Name	Required	Description	Default
`goal`	Yes	Your overall analysis objective. Be specific about what you want to understand and why.

TDQS

B3.4/5.0

Behavior3/5

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 adds some context by explaining that it provides a 'roadmap' for planning and guides thinking on decomposition, order, and incremental understanding. However, it doesn't disclose critical behavioral traits such as whether it's read-only, if it has side effects, rate limits, or authentication needs, which are significant gaps for a tool with no annotations.

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 appropriately sized and front-loaded, starting with a clear purpose statement. It uses bullet points efficiently to list guidance aspects without unnecessary elaboration. However, the final sentence about executing commands with bash tools could be integrated more smoothly, slightly affecting structure.

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 tool's complexity (planning multi-step analysis) and the absence of annotations and output schema, the description is moderately complete. It explains the planning function and guidance aspects but lacks details on behavioral traits, output format, or how it integrates with sibling tools. This leaves gaps in understanding the tool's full context and usage.

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 the 'goal' parameter well-documented as 'Your overall analysis objective. Be specific about what you want to understand and why.' The description adds minimal value beyond this, mentioning 'complex analysis goals' but not providing additional syntax or format details. Given the high schema coverage, a baseline score of 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: 'Breaks down complex analysis goals into a step-by-step plan' and 'helps you plan multi-step analysis.' It specifies the verb ('breaks down') and resource ('complex analysis goals'), making the function evident. However, it doesn't explicitly differentiate from siblings like gemini_iterate_analysis or gemini_synthesize_findings, which might involve similar analysis processes.

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

Usage Guidelines3/5

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

The description implies usage by stating it's for 'complex analysis goals' and 'multi-step analysis,' suggesting it's for decomposing intricate objectives. It mentions 'You execute the suggested gemini commands yourself using your bash tools,' which hints at a planning role rather than execution. However, it lacks explicit guidance on when to use this tool versus alternatives like gemini_craft_prompt or gemini_iterate_analysis, leaving the context somewhat vague.

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

gemini_synthesize_findingsC

Combine insights from multiple analysis steps into comprehensive understanding.

This tool helps you synthesize results from multiple gemini interactions:

How to identify patterns and themes across different analyses
How to structure synthesis prompts that connect insights
How to build comprehensive understanding from incremental steps
How to formulate actionable conclusions

You execute the synthesis commands yourself using your bash tools.

ParametersJSON Schema

Name	Required	Description	Default
`steps_summary`	Yes	A brief summary of the analysis steps you have taken so far and what you learned.
`synthesis_goal`	Yes	What you want to achieve with the synthesis - what kind of comprehensive understanding or conclusions you are seeking.

TDQS

C2.4/5.0

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 vaguely mentions 'execute the synthesis commands yourself using your bash tools,' implying this tool might generate or guide commands rather than perform synthesis directly, but it doesn't clarify the tool's actual behavior (e.g., does it output a prompt, a plan, or something else?). It lacks details on permissions, side effects, or response format, leaving significant gaps for a tool with no 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.

Conciseness2/5

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

The description is moderately long but poorly structured. The first sentence is clear, but the bullet points list generic 'how-to' concepts that don't directly explain the tool's function, and the final sentence about bash tools is tangential. It lacks front-loading of critical information and includes unnecessary details that don't earn their place in a tool 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?

Given no annotations and no output schema, the description is incomplete for a synthesis tool. It fails to explain what the tool returns (e.g., a synthesis prompt, a structured output, or just guidance), how to interpret results, or any behavioral nuances. The mention of bash tools adds confusion rather than clarity, 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?

Schema description coverage is 100%, providing clear documentation for both parameters (steps_summary and synthesis_goal). The description adds no specific parameter information beyond what the schema already states, such as format examples or constraints. However, with high schema coverage, a baseline score of 3 is appropriate as the schema adequately handles parameter semantics.

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 helps 'combine insights from multiple analysis steps into comprehensive understanding' and mentions synthesizing results from gemini interactions, which provides a general purpose. However, it doesn't specify what resource or output this creates (e.g., a synthesis document, report, or actionable plan), and it doesn't clearly distinguish from sibling tools like gemini_iterate_analysis or gemini_plan_analysis that might also involve analysis steps.

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 explicit guidance on when to use this tool versus alternatives. It mentions synthesizing results from 'multiple gemini interactions' but doesn't specify prerequisites (e.g., must have completed prior analysis steps) or contrast with sibling tools. The bullet points describe general synthesis concepts rather than practical usage scenarios.

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

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose in the Gemini analysis workflow: prompt crafting, iterative analysis, planning, and synthesis. There is no overlap in functionality—craft_prompt focuses on command formulation, iterate_analysis on dynamic cycles, plan_analysis on step decomposition, and synthesize_findings on combining insights. An agent can easily differentiate them based on their specific roles.

Naming Consistency5/5

All tool names follow a consistent 'gemini_verb_noun' pattern (e.g., gemini_craft_prompt, gemini_iterate_analysis). This uniform structure makes the set predictable and readable, with no deviations in style or convention. The naming clearly indicates the domain (Gemini) and the action-resource pairing.

Tool Count5/5

With 4 tools, this server is well-scoped for its purpose of orchestrating Gemini CLI workflows. Each tool addresses a distinct phase of analysis (prompting, iteration, planning, synthesis), and none feel redundant or missing. The count is appropriate for covering the core lifecycle without being overwhelming or insufficient.

Completeness4/5

The tool set covers the essential phases of a Gemini analysis workflow: from initial prompt crafting to iterative cycles, planning, and final synthesis. However, there is a minor gap in direct execution or feedback tools, as all descriptions note 'you execute the commands yourself,' which might leave agents without guidance on handling execution errors or real-time adjustments. Overall, the surface is nearly complete for the stated orchestration purpose.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

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An MCP server that gives AI agents structured code understanding and precise code intelligence via local indexing of AST, call graphs, and semantic search.
Updated 3 months ago (2026-05-26 10:13 UTC)
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Apache 2.0
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Code Analysis AI & Machine Learning Developer Tools
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An MCP server that gives your IDE or agent access to Google Gemini with autonomous codebase exploration, enabling deep code analysis, architectural reviews, and bug hunting.
Updated 3 months ago (2026-06-10 15:27 UTC)
20
10
MIT

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MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dnnyngyen/gemini-cli-orchestrator'

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Gemini CLI Orchestrator v2.0 - Sequential Thinking MCP

🧠 Core Philosophy

🚀 Quick Start

How It Works

🎯 gemini_plan_analysis(goal)

🔍 gemini_craft_prompt(step_description, context)

🔄 gemini_iterate_analysis(current_understanding, iteration_goal)

📊 gemini_synthesize_findings(steps_summary, synthesis_goal)

Example Workflow

🎯 Key Benefits

📚 Documentation

🤝 Contributing

MCP Configuration

🚨 Important Note for Existing Users

Claude Code CLI (Recommended)

Handling Previous Installations / Filename Changes

Manual Configuration (Alternative)

Troubleshooting

Advanced Usage Examples

Security Audit Workflow

Performance Analysis Workflow

Architecture Review Workflow

File Pattern Examples

Requirements

Troubleshooting

What's New in v2.0

Philosophy

Available Tools

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MCP directory API

🎯 `gemini_plan_analysis(goal)`

🔍 `gemini_craft_prompt(step_description, context)`

🔄 `gemini_iterate_analysis(current_understanding, iteration_goal)`

📊 `gemini_synthesize_findings(steps_summary, synthesis_goal)`