MCP Server Gemini

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: image analysis, token counting, text embedding, text generation, help retrieval, and model listing. The descriptions reinforce these distinct functions, making tool selection unambiguous for an agent.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (analyze_image, count_tokens, embed_text, generate_text, list_models), but get_help deviates slightly with a 'get_' prefix. The naming is still highly readable and predictable overall.
Tool Count5/5
With 6 tools, this server is well-scoped for Gemini AI interactions. Each tool earns its place by covering distinct aspects: vision, token management, embeddings, text generation, help, and model discovery, without being overly sparse or bloated.
Completeness4/5
The toolset covers core Gemini AI workflows comprehensively, including text and image processing, embeddings, and model management. A minor gap exists in lacking explicit tools for chat/multi-turn conversations or file uploads, but agents can work around this using generate_text and analyze_image.
Average 2.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
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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?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Generate text' implies a read-only operation, the description doesn't address important behavioral aspects like rate limits, authentication requirements, cost implications, error handling, or what constitutes 'advanced features.' The mention of 'Google Gemini' hints at external API usage but lacks specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point without unnecessary words. While it could be more informative, it's appropriately concise and front-loaded with the core functionality. Every word earns its place, though more content would be justified given the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 12 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'advanced features' means, doesn't describe the return format or structure, and provides no context about error conditions, rate limits, or typical use cases. The description fails to compensate for the lack of structured metadata.
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 detailed parameter documentation, so the baseline is 3. The description adds minimal value beyond the schema by mentioning 'advanced features' which vaguely references some parameters like grounding and jsonMode, but doesn't provide meaningful semantic context about how parameters interact or when to use specific combinations.
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 'Generate text using Google Gemini with advanced features' which provides a clear verb ('Generate text') and resource ('Google Gemini'). However, it's somewhat vague about what 'advanced features' entails and doesn't distinguish this text generation tool from its sibling tools like 'analyze_image' or 'embed_text' beyond the basic domain difference.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of when to choose this over other text generation methods, when to use specific parameter combinations, or how it relates to sibling tools like 'count_tokens' or 'embed_text' which might be complementary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'analyze images' but doesn't describe what the analysis entails (e.g., object detection, captioning, classification), potential limitations (e.g., image size, format constraints), or response format. This is a significant gap 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and uses clear language, making it easy to parse quickly without unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of image analysis (a non-trivial operation), no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage context, which are crucial for an AI agent to invoke this tool effectively in practice.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 4 parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain parameter interactions like choosing between 'imageUrl' and 'imageBase64'). 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 action ('analyze images') and the capability used ('Gemini vision capabilities'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate this tool from its siblings like 'generate_text' or 'embed_text', which might also handle text generation or embeddings potentially involving images, so it misses full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context for choosing this over other sibling tools (e.g., 'generate_text' for text generation or 'list_models' for model information), nor does it specify prerequisites or exclusions, leaving usage ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool counts tokens but doesn't describe what a 'token' means in this context, whether there are rate limits, error handling, or output format. For a tool with no 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, efficient sentence with zero waste. It's front-loaded with the core purpose and appropriately sized for the tool's complexity, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavioral traits, usage context, and output expectations. While the schema covers parameters well, the description doesn't compensate for missing annotations or output schema, leaving gaps in overall understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents both parameters ('text' and 'model'). The description adds no additional meaning beyond what's in the schema, such as explaining tokenization nuances or model-specific behaviors. Baseline 3 is appropriate when the schema handles all parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Count tokens for a given text with a specific model'. It specifies the verb ('count'), resource ('tokens'), and scope ('text' and 'model'). However, it doesn't explicitly differentiate from sibling tools like 'embed_text' or 'generate_text', which might also involve token processing, so it doesn't reach the highest score.
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 scenarios where token counting is needed over other operations like text generation or embedding, nor does it reference sibling tools. This leaves the agent without context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the basic function but lacks details on rate limits, authentication needs, output format (e.g., vector dimensions), error handling, or performance characteristics. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to grasp quickly. Every part of the sentence earns its place by specifying the action, resource, and technology.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (embedding generation with model selection), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what embeddings are, their format, or practical applications, leaving the agent with insufficient context to use the tool effectively beyond basic invocation.
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 both parameters ('text' and 'model') with descriptions and enum values. The description adds no additional meaning beyond what the schema provides, such as explaining embedding use cases or model differences. Baseline 3 is appropriate when the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Generate embeddings') and resource ('for text using Gemini embedding models'), making the purpose immediately understandable. It distinguishes from siblings like 'analyze_image' or 'generate_text' by focusing on embeddings. However, it doesn't explicitly differentiate from 'count_tokens' which might be related, keeping it at 4 rather than 5.
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 use cases for embeddings (e.g., semantic search, clustering) or when to choose it over other tools like 'generate_text' for similar text processing tasks. There's no explicit when/when-not or alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves help information, implying a read-only operation, but doesn't specify if it requires authentication, has rate limits, returns structured or unstructured data, or handles errors. 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently conveys the core purpose without unnecessary words. It's front-loaded with the main action, making it easy to parse. However, it could be slightly more structured by hinting at the parameter usage, but overall it's concise and well-formed.
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 low complexity (one optional parameter with full schema coverage) and lack of output schema, the description is minimally adequate. It states what the tool does but doesn't cover behavioral aspects like response format or error handling, which are important for a help tool. With no annotations, it should provide more context to be fully complete.
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 'topic' parameter fully documented including its enum values and default. The description doesn't add any semantic details beyond what the schema provides, such as explaining what each topic covers or how the help is formatted. Given the high schema coverage, the 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: 'Get help and usage information for the Gemini MCP server.' It uses a specific verb ('Get') and identifies the resource ('help and usage information'), though it doesn't explicitly differentiate from sibling tools like 'list_models' which might provide model information. The purpose is unambiguous but lacks sibling 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, context for usage, or compare it to sibling tools like 'list_models' for model info or 'generate_text' for examples. The agent must infer usage from the purpose alone, which is insufficient for optimal tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe key behaviors such as whether it's a read-only operation, if it requires authentication, rate limits, or what the output format looks like (e.g., list structure, pagination). This leaves significant gaps for an agent to understand how to interact with it effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded and appropriately sized for a simple tool, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter) and rich schema coverage, the description is minimally adequate. However, with no output schema and no annotations, it doesn't fully compensate by explaining return values or behavioral traits, leaving the agent with incomplete context for proper 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 a clear enum for the 'filter' parameter. The description mentions 'capabilities', which aligns with the schema's 'filter by capability', but doesn't add meaningful semantics beyond what the schema already provides (e.g., explaining what each enum value means in context). 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all available Gemini models and their capabilities'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_help' or 'analyze_image', which might also involve model information, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For example, it doesn't specify if this should be used for discovery before calling 'generate_text' or how it relates to 'get_help'. The description lacks context about usage scenarios or exclusions.
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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