Integrates Google's Gemini models to provide image generation and editing tools, allowing for 2K image creation from text prompts, natural language image manipulation, and session-based character or style consistency.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@NanoBanana MCP create a 16:9 image of a cozy cabin in a snowy forest"
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.
MCP server that brings Gemini's image generation and editing capabilities to Claude Desktop, Claude Code, and Cursor. Supports Nano Banana 2 (Flash) and Nano Banana Pro models.
Image Generation - Create 2K images from text prompts
Image Editing - Transform images with natural language instructions
Session Consistency - Maintain style/character across generations
Runtime Model Switching - Switch between Flash and Pro models without restart
Multi-turn Chat - Conversational context with image support
Related MCP server: NanoBanana MCP
Node.js 18+
Google AI API Key (Get one here)
claude mcp add nanobanana-mcp -- npx -y @ycse/nanobanana-mcp \
-e "GOOGLE_AI_API_KEY=your_api_key"Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"nanobanana-mcp": {
"command": "npx",
"args": ["-y", "@ycse/nanobanana-mcp"],
"env": {
"GOOGLE_AI_API_KEY": "your_api_key"
}
}
}
}Create or edit .cursor/mcp.json (project-level) or ~/.cursor/mcp.json (global):
{
"mcpServers": {
"nanobanana-mcp": {
"command": "npx",
"args": ["-y", "@ycse/nanobanana-mcp"],
"env": {
"GOOGLE_AI_API_KEY": "your_api_key"
}
}
}
}See Cursor MCP Documentation for more details.
Tool | Purpose |
| Required. Set aspect ratio before image generation |
| Switch between flash/pro models at runtime |
| Generate images from text prompts |
| Edit images with natural language |
| Multi-turn conversation with images |
| View session image history |
| Reset session context |
Must be called before generating or editing images.
Valid ratios: 1:1, 9:16, 16:9, 3:4, 4:3, 3:2, 2:3, 5:4, 4:5, 21:9Switch models per-session without restarting:
Value | Model | Description |
| gemini-3.1-flash-image-preview | Nano Banana 2 - Faster (default) |
| gemini-3-pro-image-preview | Nano Banana Pro - Higher quality |
{
prompt: string; // Image description
aspect_ratio?: string; // Override session ratio
output_path?: string; // Save path (default: ~/Documents/nanobanana_generated/)
conversation_id?: string; // Session ID
use_image_history?: boolean; // Use previous images for consistency
reference_images?: string[]; // Reference images for style
}{
image_path: string; // File path, "last", or "history:N"
edit_prompt: string; // Edit instructions
aspect_ratio?: string; // Override session ratio
output_path?: string; // Save path
conversation_id?: string; // Session ID
reference_images?: string[]; // Style references
}npx @ycse/nanobanana-mcp --install-commands claude-code
# Or manually:
# mkdir -p ~/.claude/commands
# cp commands/claude-code/*.md ~/.claude/commands/npx @ycse/nanobanana-mcp --install-commands cursor
# Or manually:
# mkdir -p .cursor/commands
# cp commands/cursor/*.md .cursor/commands/See Cursor Slash Commands for more details.
/nb-flash - Switch to Flash model (faster)
/nb-pro - Switch to Pro model (higher quality)1. Set aspect ratio: set_aspect_ratio("16:9")
2. Generate: "A cyberpunk cityscape at sunset"// First image
{ prompt: "A red-hat cat", conversation_id: "cat" }
// Second image - same character
{ prompt: "The cat taking a nap", conversation_id: "cat", use_image_history: true }// Edit the last generated image
{ image_path: "last", edit_prompt: "Change hat to blue" }
// Edit specific image from history
{ image_path: "history:0", edit_prompt: "Add sunglasses" }// Start with Flash for quick iterations
set_model({ model: "flash" })
{ prompt: "Draft concept art" }
// Switch to Pro for final render
set_model({ model: "pro" })
{ prompt: "Final polished version", use_image_history: true }Variable | Required | Description |
| Yes | Google AI API key |
| No | Default model ( |
Generated images save to ~/Documents/nanobanana_generated/:
Generated: generated_[timestamp].png
Edited: [original]_edited_[timestamp].png
git clone https://github.com/YCSE/nanobanana-mcp.git
cd nanobanana-mcp
npm install
npm run dev # Development mode with hot reload
npm run build # Production build
npm run start # Run compiled serverImage generation fails:
Verify API key is valid
Check quota at Google AI Studio
Ensure set_aspect_ratio was called first
Tools not showing:
Restart Claude Desktop/Code
Check config file syntax
Verify npx -y @ycse/nanobanana-mcp runs without errors
MIT
Clear conversation history for a specific conversation ID
| Name | Required | Description | Default |
|---|---|---|---|
| conversation_id | Yes | The conversation ID to clear |
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 states the tool 'clears' conversation history, implying a destructive mutation, but doesn't specify whether this is permanent/reversible, requires specific permissions, affects other data, or has side effects (e.g., notifications). For a destructive tool with zero annotation coverage, this leaves critical behavioral traits undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given this is a destructive mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'clear' entails (e.g., deletion, archiving), what happens on success/failure, or return values. For a tool that permanently modifies data, more context is needed to use it safely and effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'conversation_id' documented as 'The conversation ID to clear'. The description adds no additional meaning beyond this, such as format examples or constraints. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('clear') and resource ('conversation history for a specific conversation ID'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its siblings (like 'gemini_chat' or 'get_image_history'), which appear to be related to chat/image functionality but aren't directly comparable deletion alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., whether the conversation must exist), exclusions (e.g., cannot clear active conversations), or related tools (like 'gemini_chat' for creating conversations). The agent must infer usage from context alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Chat with Gemini 3.1 Flash model. Supports multi-turn conversations with up to 10 reference images.
| Name | Required | Description | Default |
|---|---|---|---|
| message | Yes | The message to send to Gemini | |
| images | No | Array of image paths to include in the chat (max 10). Supports file paths, 'last', or 'history:N' references. | |
| conversation_id | No | Optional conversation ID for maintaining context and accessing image history | |
| system_prompt | No | Optional system prompt to guide the model's behavior |
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 multi-turn conversations and image limits (max 10), which are useful, but lacks critical details like rate limits, authentication needs, response format, or error handling. This is inadequate for a chat tool with potential complexity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose and key features (multi-turn, image support). Every word earns its place with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a chat tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks information on response format, error cases, rate limits, and how it integrates with siblings like clear_conversation or get_image_history. More context is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning about parameters beyond implying image support aligns with the 'images' parameter. 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Chat with') and target ('Gemini 3.1 Flash model'), and mentions multi-turn conversations and image support. However, it doesn't explicitly differentiate from siblings like gemini_edit_image or gemini_generate_image, which also interact with Gemini but for different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions multi-turn conversations and image support, which implies usage for interactive chat with visual inputs. However, it provides no explicit guidance on when to use this tool versus alternatives like gemini_edit_image or set_model, nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Edit or modify existing images based on prompts. Supports session history references ('last' or 'history:N') and image consistency features.
| Name | Required | Description | Default |
|---|---|---|---|
| image_path | Yes | Path to the original image. Use 'last' for most recent generated image, or 'history:N' (e.g., 'history:0') to reference by index | |
| edit_prompt | Yes | Instructions for how to edit the image | |
| aspect_ratio | No | Aspect ratio for the edited image. Overrides session setting if provided. | |
| output_path | No | Optional output path. If not provided, saves to ~/Documents/nanobanana_generated/ | |
| conversation_id | No | Session ID for accessing image history and maintaining consistency | |
| reference_images | No | Additional reference images for style consistency (max 10). Supports file paths, 'last', or 'history:N' references. | |
| enable_google_search | No | Enable Google Search for real-world reference grounding |
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 that the tool 'supports session history references' and 'image consistency features,' which adds useful context about how it interacts with session state. However, it doesn't disclose critical behavioral traits like whether edits are destructive to the original image, authentication requirements, rate limits, error conditions, or what happens when output_path isn't provided (beyond the schema's description). The description adds some value but leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Edit or modify existing images based on prompts') and adds two key features. There's no wasted verbiage, and every clause adds value. It could be slightly more structured by separating core purpose from features, but it's appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, mutation operation, no annotations, no output schema), the description is moderately complete. It covers the core purpose and hints at session integration, but it lacks details on behavioral outcomes, error handling, or what the tool returns. Without annotations or output schema, the description should do more to explain the mutation's effects and results, but it provides a basic foundation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description doesn't add any parameter-specific semantics beyond what's already in the schema (e.g., it doesn't explain how 'edit_prompt' interacts with 'reference_images' or clarify the 'conversation_id' usage). With high schema coverage, the baseline is 3, and the description doesn't compensate with additional insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as editing or modifying existing images based on prompts, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'gemini_generate_image' (which likely creates new images) or 'get_image_history' (which retrieves but doesn't edit). The description mentions session history references and image consistency features, which adds specificity but not explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through mentions of session history references ('last' or 'history:N') and image consistency features, suggesting this tool is for iterative editing within a session. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'gemini_generate_image' for new images or 'clear_conversation' for session management. No when-not-to-use scenarios or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate images using Gemini's image generation capabilities. Supports session-based image consistency for maintaining style/character across multiple generations.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Description of the image to generate | |
| aspect_ratio | No | Aspect ratio for the generated image. Overrides session setting if provided. | |
| output_path | No | Optional path where to save the generated image. If not provided, saves to ~/Documents/nanobanana_generated/ | |
| conversation_id | No | Session ID for maintaining image history and consistency across generations | |
| use_image_history | No | If true, includes previous generated images from this session for style/character consistency | |
| reference_images | No | Array of file paths to reference images for style/character consistency | |
| enable_google_search | No | Enable Google Search for real-world reference grounding |
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 discloses key behavioral traits: it's a generation tool (implying creation/mutation), supports session-based consistency, and mentions style/character maintenance. However, it lacks details on permissions, rate limits, error handling, or output format (e.g., image type, size). The description doesn't contradict annotations, but it's incomplete for a tool with multiple parameters and no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded: the first sentence states the core purpose, and the second adds key functionality (session-based consistency). Both sentences earn their place by providing essential information without redundancy or fluff, making it easy to scan and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, no annotations, no output schema), the description is moderately complete. It covers the main purpose and a key feature (session consistency) but lacks details on behavioral aspects like mutation effects, error cases, or output handling. Without annotations or output schema, the description should do more to compensate, but it provides a basic foundation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 7 parameters. The description adds minimal value beyond the schema: it mentions 'session-based image consistency,' which relates to 'conversation_id' and 'use_image_history,' but doesn't provide additional syntax or usage details. With high schema coverage, the baseline is 3, as the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate images using Gemini's image generation capabilities.' It specifies the action (generate) and resource (images) with the technology (Gemini). However, it doesn't explicitly differentiate from sibling tools like 'gemini_edit_image' or 'get_image_history' beyond mentioning session-based consistency.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through 'session-based image consistency for maintaining style/character across multiple generations,' suggesting this tool is for generating new images with optional consistency features. It doesn't provide explicit guidance on when to use this versus alternatives like 'gemini_edit_image' or 'set_aspect_ratio,' nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the list of generated/edited images in a session for reference
| Name | Required | Description | Default |
|---|---|---|---|
| conversation_id | Yes | The session ID to get image history for |
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 implies a read-only operation ('Get the list') but does not specify permissions, rate limits, pagination, or what 'session' entails. This is inadequate for a tool that likely interacts with session data, leaving key behavioral traits undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded and appropriately sized, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It does not explain what the returned list contains (e.g., image metadata, URLs), how it's structured, or any error conditions. For a tool that retrieves historical data, this leaves significant gaps in understanding its full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the parameter 'conversation_id' documented as 'The session ID to get image history for'. The description adds no additional meaning beyond this, such as format examples or constraints, so it meets the baseline for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Get the list') and resource ('generated/edited images in a session'), making the purpose understandable. However, it does not explicitly differentiate from sibling tools like 'gemini_generate_image' or 'gemini_edit_image', which might handle similar image-related operations but with different functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as how it relates to sibling tools like 'gemini_generate_image' or 'clear_conversation'. It mentions 'for reference' but lacks explicit context, prerequisites, or exclusions, leaving usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Set the aspect ratio for subsequent image generation and editing in this session. Must be called before generating/editing images if a specific ratio is desired.
| Name | Required | Description | Default |
|---|---|---|---|
| aspect_ratio | Yes | The aspect ratio to use for image generation/editing | |
| conversation_id | No | Session ID to apply this setting to (default: 'default') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that this is a session-level configuration tool that affects subsequent operations, which is valuable behavioral context. However, it doesn't mention whether this setting persists across sessions, what happens if called multiple times, or any error conditions beyond the temporal requirement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description uses only two sentences that are completely focused and front-loaded. The first sentence states the purpose, the second provides critical usage guidance. There is zero wasted text or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a configuration tool with 2 parameters and 100% schema coverage but no annotations or output schema, the description provides good context about when and why to use it. It could be more complete by addressing persistence across sessions or error conditions, but it covers the essential operational context adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific information 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Set the aspect ratio') and the target ('for subsequent image generation and editing in this session'). It distinguishes this tool from siblings like gemini_generate_image or gemini_edit_image by focusing on configuration rather than creation/modification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('Must be called before generating/editing images if a specific ratio is desired') and implies alternatives (using default ratios if not called). This provides clear temporal and conditional guidance for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Set the Gemini model for this session. 'flash' for faster generation (default), 'pro' for higher quality.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model to use: 'flash' (gemini-3.1-flash-image-preview) or 'pro' (gemini-3-pro-image-preview) | |
| conversation_id | No | Session ID to apply this setting to (default: 'default') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that this affects session configuration and mentions default behavior, but doesn't cover important behavioral aspects like whether this persists across sessions, requires specific permissions, or has rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste - the first states the purpose, the second provides usage guidance. Every word earns its place, and the information is front-loaded appropriately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a configuration tool with 2 parameters and 100% schema coverage but no annotations or output schema, the description is reasonably complete. It explains what the tool does and provides practical guidance, though it could benefit from more behavioral context given the absence of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds minimal value beyond the schema by mentioning the performance characteristics of each model option, but doesn't provide additional parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Set') and resource ('Gemini model for this session'), distinguishing it from siblings like gemini_chat or set_aspect_ratio. It specifies the exact configuration being modified.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use each option ('flash' for faster generation, 'pro' for higher quality) and indicates the default. However, it doesn't explicitly state when NOT to use this tool or mention alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.1.1clear_conversationgemini_chatgemini_edit_imagegemini_generate_imageget_image_historyset_aspect_ratioset_modelEach tool has a clearly distinct purpose with no overlap: conversation management (clear_conversation), chat (gemini_chat), image generation (gemini_generate_image), image editing (gemini_edit_image), history retrieval (get_image_history), aspect ratio configuration (set_aspect_ratio), and model selection (set_model). The boundaries between tools are well-defined and unambiguous.
The naming follows a mostly consistent pattern with six tools using snake_case and clear action-object naming (clear_conversation, get_image_history, set_aspect_ratio, set_model). The three gemini_* tools deviate slightly by prefixing with 'gemini_' but maintain internal consistency. The main inconsistency is that gemini_chat doesn't follow the verb_noun pattern as strictly as others.
Seven tools is an ideal number for this Gemini interaction server, covering core functionality without bloat. Each tool serves a distinct purpose in the workflow: conversation management, chat interaction, image generation/editing, history access, and session configuration. No tool feels redundant or unnecessary for the server's scope.
The tool surface provides excellent coverage for Gemini-based chat and image workflows, including conversation management, multi-modal chat, image generation/editing, and session configuration. The only minor gap is the lack of a tool to manage multiple conversations or sessions beyond the current one, but agents can work effectively with the provided tools for most use cases.
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