nanobanana-mcp-server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: generate_image creates/edits images, maintenance handles cleanup, show_output_stats displays statistics, and upload_file uploads files. No overlap in functionality.
Naming Consistency3/5Names use mixed patterns: verb_noun (generate_image, upload_file), verb_noun_noun (show_output_stats), and a noun (maintenance). While readable, the inconsistency could cause confusion.
Tool Count4/54 tools is appropriate for an image generation server, covering core operations. It could benefit from additional tools like delete_image, but the current count is reasonable.
Completeness3/5The set covers generation, editing, upload, and maintenance, but lacks explicit listing or deletion of images, and show_output_stats is somewhat superficial. Notable gaps exist.
Average 3.5/5 across 4 of 4 tools scored. Lowest: 2.7/5.
See the Tool Scores section below for per-tool breakdowns.
- 4 of 4 community issues answered or closed in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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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
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description states operations like 'cleanup_expired' and 'cleanup_local' that remove data, but annotations declare readOnlyHint=true, a contradiction. No mention of destructive nature beyond operation names. Dry-run parameter is not highlighted. Annotation contradiction flag set to true.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Description is short and well-structured with a bullet list for operations. It is front-loaded with a general statement. No superfluous content, but it could be even more concise by avoiding the external reference.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description should explain what each operation returns or reports. It does not. The contradiction with annotations is not addressed, and the 'workflows.md' reference is unexplained. The tool has multiple operations with different behaviors, but completeness is lacking.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are well-documented in the schema. The description adds minimal additional meaning—only listing operation names and referencing an external file (workflows.md). The schema already explains each operation, so description adds limited value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's purpose: performing maintenance operations with a list of specific tasks. The sibling tools are unrelated (generate_image, show_output_stats, upload_file), so differentiation is clear. However, the description references 'workflows.md patterns' which may not be accessible, slightly reducing clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives or how to choose between operations. Prerequisites and context are missing. The description merely lists operations without explaining appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description describes write operations ('generate new images or edit existing images'), but annotations set readOnlyHint to true, which indicates the tool should not modify data. This is a direct contradiction. While the description adds useful behavioral details (auto-detection, local filesystem reading, return format), the contradiction severely undermines transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively long but well-structured with bullet points and clear numbered modes. It front-loads the core purpose. Some redundancy exists (e.g., mode auto-detection mentioned twice), but overall it is efficiently organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the high complexity (16 params, multiple modes) and no output schema, the description covers input modes and general behavior but lacks details on output structure, error handling, or performance implications. It mentions returning 'MCP image content blocks and structured JSON' without specifying the JSON format. Completeness is adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds context about input modes and mode auto-detection but does not significantly enhance understanding of individual parameters beyond what the schema provides. The description's added value is moderate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Generate new images or edit existing images using natural language instructions' and lists four specific input modes. The title reinforces the purpose with 'Multi-Model: Flash & Pro'. It is distinct from sibling tools (maintenance, show_output_stats, upload_file).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists four input modes and explains automatic mode detection based on parameters. It also details the 'mode' parameter with 'generate' and 'edit' options. However, it does not explicitly state when to avoid using this tool or compare it to alternatives; but siblings are unrelated, so context is clear.
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?
Annotations already mark readOnlyHint=true, so the description need not repeat. It adds context about the scope (output directory and recent images) but doesn't elaborate on performance, frequency limits, or 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise sentence that clearly conveys the tool's purpose without extraneous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description should hint at what stats are returned. The current description is vague ('statistics', 'recently generated images') and could be improved by listing examples like file count or total size.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the description adds nothing beyond the schema. With 100% schema coverage and zero params, the baseline is met; no additional explanation is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool shows statistics about the output directory and recently generated images. It uses a specific verb 'show' and identifies the resource, distinguishing it from sibling tools like generate_image and upload_file.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for retrieving stats but does not explicitly instruct when to use this tool versus siblings, nor does it provide conditions for use or exclusions.
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?
Annotations indicate it's a write operation (readOnlyHint=false) and returns dynamic data (openWorldHint=true). The description adds return details (URI & metadata) and a usage condition, but doesn't elaborate on side effects or requirements beyond what the schema provides. With annotations present, the burden is partially met.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences. The first states the exact purpose, and the second adds a valuable usage condition. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema, so the description partially covers return values (URI and metadata). It addresses large file support, which is a key concern for uploads. Missing details like overwrite behavior or error conditions, but overall sufficient for typical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description does not add new semantic meaning beyond the schema; it confirms the path is server-accessible and display_name is optional, which is already documented. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool uploads a local file to the Gemini Files API and returns its URI and metadata. It distinguishes itself from siblings like generate_image by focusing on file upload rather than image generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides specific context for when to use the tool: when the image is larger than 20MB or reused across prompts. It doesn't explicitly list alternatives or when not to use, but the context is clear and helpful.
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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