Nano-Banana-MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity. The descriptions explicitly differentiate between generating new images, editing specific files, continuing from the last image, and configuration tasks. The boundaries are well-defined, preventing misselection.
Naming Consistency5/5
All tools follow a consistent verb_noun naming pattern (e.g., configure_gemini_token, generate_image, edit_image). The naming is uniform throughout, using snake_case and clear action-object pairs without any deviations.
Tool Count5/5
With 6 tools, this server is well-scoped for image generation and editing. Each tool earns its place by covering essential operations like configuration, generation, editing, continuation, and status checks, without being overly sparse or bloated.
Completeness5/5
The tool set provides complete coverage for the nano-banana image generation domain. It includes configuration setup, new image generation, specific image editing, iterative editing, status checks, and session management, with no obvious gaps or dead ends for agents.
Average 3.8/5 across 6 of 6 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
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This repository is licensed under MIT License.
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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 states this is a configuration tool, implying a write/mutation operation, but doesn't describe key behaviors: whether this overwrites existing tokens, requires specific permissions, has side effects (e.g., affecting other tools), or what happens on success/failure. For a mutation 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It's front-loaded with the core purpose ('Configure your Gemini API token') and includes necessary context ('for nano-banana image generation'). Every part of the sentence earns its place by specifying the action, resource, and application scope efficiently.
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 tool's complexity (a mutation operation for API configuration) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., success status, error messages), how it interacts with sibling tools (e.g., 'generate_image'), or behavioral details like persistence or validation. For a configuration tool with no structured support, more context is needed to guide the agent 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 description doesn't add any parameter-specific information beyond what's in the input schema, which has 100% coverage for the single parameter 'apiKey'. The schema already describes it as 'Your Gemini API key from Google AI Studio'. Since schema coverage is high, the baseline score is 3, 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: 'Configure your Gemini API token for nano-banana image generation.' It specifies the verb ('configure') and resource ('Gemini API token'), and mentions the context ('for nano-banana image generation'), which helps distinguish it from generic configuration tools. However, it doesn't explicitly differentiate from sibling tools like 'get_configuration_status', which might be related.
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 minimal usage guidance. It implies this tool should be used to set up API tokens for image generation, but it doesn't specify when to use it (e.g., before generating images, when tokens expire) or when not to use it (e.g., if already configured). No alternatives or prerequisites are mentioned, leaving the agent with unclear operational context.
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 states what the tool does but doesn't describe what 'check' entails - whether it returns a simple boolean, detailed status information, error messages if unconfigured, or any side effects. For a tool with zero annotation coverage, 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.
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 any wasted words. It's appropriately sized for a simple tool and front-loads the essential 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?
Given the tool has no annotations and no output schema, the description should provide more complete context about what the check returns and how to interpret results. As a configuration status tool, users need to know what format the status information comes in and what actions to take based on the result, but this is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and it doesn't need to compensate for any schema gaps.
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 with a specific verb ('Check') and resource ('Gemini API token configuration'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'configure_gemini_token' beyond the obvious check vs. configure distinction.
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 (like needing to run this before using other tools), nor does it suggest when to use 'configure_gemini_token' instead. The usage context is implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 states this is an edit/mutation operation (implying destructive changes) and mentions optional reference images for style transfer/element addition, but doesn't disclose permission requirements, rate limits, error conditions, or what happens to the original file. For a mutation tool with zero annotation coverage, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that each earn their place. The first sentence states the core functionality, and the second provides crucial usage guidance. There's zero wasted language or redundancy.
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?
For a mutation tool with no annotations and no output schema, the description provides adequate purpose and usage guidance but lacks important behavioral context. It doesn't explain what the tool returns, error conditions, or mutation specifics. Given the complexity of image editing operations, more completeness would be beneficial.
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 fully documents all three parameters. The description adds minimal value beyond the schema - it reinforces that 'imagePath' must be exact and 'referenceImages' are optional, but doesn't provide additional syntax, format details, or constraints. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verb ('Edit') and resource ('a SPECIFIC existing image file'), and distinguishes it from sibling tools like 'generate_image' (which creates new images) by emphasizing modification of existing files. The phrase 'optionally using additional reference images' adds further specificity about functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('when you have the exact file path of an image to modify'), which clearly differentiates it from 'generate_image' (for creating new images) and 'continue_editing' (presumably for ongoing edits). It establishes clear prerequisites for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 discloses that this creates new content ('generate a NEW image') but doesn't mention behavioral traits like rate limits, quality expectations, generation time, or output format. The description adds some context about the creation scope but lacks operational details.
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?
Two sentences with zero waste. The first sentence states the core purpose, and the second provides critical usage guidance. Every word earns its place, and the structure is front-loaded with essential information.
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?
For a single-parameter tool with no annotations and no output schema, the description is adequate but has gaps. It covers purpose and usage boundaries well, but doesn't address what the tool returns (image data, URL, metadata) or any operational constraints. The context is partially complete but lacks output information.
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 the single 'prompt' parameter. The description adds minimal value by reinforcing that the prompt should describe 'the NEW image to create from scratch', which slightly expands on the schema's description. Baseline 3 is appropriate when schema does most of the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('generate') and resource ('image') with specific scope ('from text prompt'). It explicitly distinguishes from siblings by stating 'not when modifying an existing one', which differentiates it from tools like 'edit_image'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance with 'Use this ONLY when creating a completely new image, not when modifying an existing one.' This clearly defines when to use this tool versus alternatives like 'edit_image' and establishes clear boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 discloses key behavioral traits: it's a mutation tool (editing implies changes), it tracks session state ('LAST image in this session'), and it has automatic referencing. However, it lacks details on permissions, rate limits, error conditions, or what happens if no previous image exists, leaving 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by usage context and a key behavioral note. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (mutation with session state) and no annotations or output schema, the description does well by covering purpose, usage, and key behavior. However, it lacks details on return values, error handling, or session management specifics, which would be helpful for full 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?
Schema description coverage is 100%, so the schema already fully documents both parameters. The description adds no additional meaning beyond what the schema provides, such as examples or edge cases for parameters. Baseline 3 is appropriate when the schema does all the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('continue editing'), target resource ('the LAST image that was generated or edited in this session'), and scope ('iterative improvements, modifications, or changes'). It distinguishes from sibling tools like 'edit_image' by emphasizing it works on the most recent image automatically without file paths.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
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 ('for iterative improvements, modifications, or changes to the most recent image') and distinguishes it from alternatives by noting it 'automatically uses the previous image without needing a file path,' which contrasts with tools like 'edit_image' that likely require explicit file references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 discloses that the tool retrieves information (e.g., file path, size) about the last image in the session, which implies read-only behavior, but lacks details on error handling, session scope, or data freshness. This is adequate but with gaps in 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 two sentences with zero waste: the first states the purpose, and the second provides usage guidance. It is front-loaded with essential information and appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no annotations, no output schema), the description is nearly complete: it explains what the tool does and when to use it. However, it lacks details on output format (e.g., what 'etc.' includes) and potential errors, which could be improved for full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter details, maintaining focus on the tool's purpose and usage, which aligns with the baseline for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Get information about') and resources ('last generated/edited image'), distinguishing it from siblings like 'generate_image' or 'edit_image' by focusing on retrieval of metadata rather than image creation or modification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use this tool ('to check what image is currently available for continue_editing'), providing clear context and distinguishing it from alternatives by linking directly to the 'continue_editing' sibling tool.
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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