Gemini MCP Server

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 Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., analyze_image, count_tokens, embed_text), using snake_case throughout. This predictable naming scheme enhances readability and usability for agents.
Tool Count5/5
With 6 tools, the server is well-scoped for its purpose of providing Gemini AI capabilities. Each tool serves a specific, essential function (e.g., core generation, analysis, and utility tasks), with no redundant or trivial additions.
Completeness4/5
The tool set covers key Gemini functionalities like text generation, image analysis, embeddings, and model listing, with a helpful utility tool. A minor gap is the lack of tools for managing conversations or multi-turn interactions, but core workflows are well-supported.
Average 3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 2 community issues answered or closed in the last 6 months
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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 mentions 'Gemini vision capabilities' but doesn't detail what this entails—such as rate limits, authentication needs, output format, or potential costs. For a tool with no annotations, this leaves significant gaps in understanding how it behaves beyond basic functionality.
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 extremely concise and front-loaded with a single, clear sentence: 'Analyze images using Gemini vision capabilities.' There is no wasted verbiage, and it efficiently communicates the core purpose 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 a vision analysis tool with no annotations and no output schema, the description is incomplete. It lacks information on behavioral traits, output format, error handling, or integration context. While the schema covers inputs well, the overall context for an AI agent to use this tool effectively is insufficient.
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 all parameters. The description adds no additional meaning beyond what's in the schema, such as explaining the interplay between prompt and image inputs or model selection nuances. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Analyze images using Gemini vision capabilities.' It specifies the action (analyze) and resource (images) with the technology context (Gemini vision). However, it doesn't explicitly differentiate from sibling tools like generate_text or embed_text, which might also process text or have different vision-related functions.
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 sibling tools like generate_text (which might handle text generation) or list_models (which could list available models), nor does it specify contexts or exclusions for image analysis. Usage is implied but not explicitly defined.
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 what the tool does ('count tokens') but doesn't describe behavioral traits such as whether it's a read-only operation, if it has rate limits, what the output format looks like (e.g., integer count or structured response), or any error conditions (e.g., handling of empty text). For a tool with no annotations, 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 appropriately sized and front-loaded in a single, efficient sentence. It conveys the core functionality without unnecessary words, making it easy for an agent to parse quickly. Every part of the sentence ('count tokens', 'for a given text', 'with a specific model') contributes essential information, earning its place.
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 (a computational tool with model dependencies) and lack of annotations and output schema, the description is incomplete. It doesn't explain the output (e.g., token count as an integer), error handling, or practical use cases. While the schema covers parameters well, the description fails to provide sufficient context for an agent to understand the tool's behavior and results fully, especially without structured 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?
The description adds minimal meaning beyond the input schema. It mentions 'text' and 'model' as parameters, but the schema already provides 100% coverage with clear descriptions and an enum for 'model'. The description doesn't explain why token counting might vary by model or provide additional context (e.g., token definitions, implications for different models). With high schema coverage, the baseline is 3, and 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.
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 ('count') and resource ('tokens'), and specifies the input ('text') and method ('with a specific model'). It distinguishes from siblings like 'embed_text' or 'generate_text' by focusing on token counting rather than text generation or embedding. However, it doesn't explicitly differentiate from all siblings (e.g., 'analyze_image' is clearly different, but the distinction could be more explicit).
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 (e.g., for cost estimation, input validation), nor does it reference sibling tools like 'embed_text' or 'generate_text' as alternatives for different tasks. The lack of context leaves the agent to infer usage based on the tool name alone.
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 mention important behavioral aspects like rate limits, authentication requirements, response format, or potential costs. The description is minimal and lacks operational 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 extremely concise - a single sentence that directly states the tool's function without any fluff. It's front-loaded with the core purpose and wastes no words, making it efficient for an agent to parse.
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 tool with no annotations and no output schema, the description is insufficient. It doesn't explain what embeddings are, what format they're returned in, typical use cases, or any operational constraints. Given the complexity of embedding generation and lack of structured metadata, more descriptive context would be helpful.
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?
With 100% schema description coverage, the schema already documents both parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions 'text' and 'Gemini embedding models' but provides no additional context about parameter usage or constraints.
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 ('text using Gemini embedding models'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'generate_text' or 'analyze_image' beyond mentioning embeddings specifically, which is why 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 like 'generate_text' or 'count_tokens'. There's no mention of typical use cases for embeddings (e.g., semantic search, clustering) or when other tools might be more appropriate.
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 full burden. It mentions 'advanced features' but doesn't specify what these are (e.g., grounding, JSON mode, safety settings). It doesn't disclose rate limits, authentication needs, costs, or what happens on failure. The description is too vague to help an agent understand behavioral traits beyond basic generation.
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. It's appropriately sized for a tool with this complexity. However, it could be more front-loaded by specifying key capabilities (e.g., 'Generate text with options for JSON output, grounding, and safety controls') to immediately convey value.
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' entail, doesn't guide usage relative to siblings, and leaves behavioral aspects unclear. The agent would struggle to use this effectively without relying heavily on the schema alone.
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 12 parameters thoroughly. The description adds no parameter-specific information beyond implying 'advanced features' might relate to some parameters. Baseline is 3 since the schema does heavy lifting, but the description doesn't compensate with additional context about parameter interactions or best practices.
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 ('generate') and resource ('text') with the specific technology ('using Google Gemini'). It distinguishes from siblings like analyze_image or embed_text by focusing on text generation. However, it doesn't explicitly differentiate from count_tokens or list_models in terms of when to use each.
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 mentions 'advanced features' but doesn't specify what makes it advanced compared to basic text generation or when to choose it over other siblings like analyze_image for multimodal tasks. There's no mention of prerequisites, limitations, or typical use cases.
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 full burden for behavioral disclosure. While 'Get help and usage information' implies a read-only, non-destructive operation, it doesn't specify what form the help takes (e.g., structured documentation, examples, error messages), whether it requires authentication, or any rate limits. The description is too vague about behavioral traits beyond the basic purpose.
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 that efficiently conveys the core purpose without unnecessary words. It is appropriately sized for a simple tool and front-loads the essential information, 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 with full schema coverage) and lack of annotations/output schema, the description is minimally adequate. It states what the tool does but lacks details on behavioral context, usage guidance, or output format. For a help tool that might return varied documentation, 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%, with the single parameter 'topic' fully documented in the schema (including description, enum values, and default). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 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.
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 ('Get help and usage information') and resource ('for the Gemini MCP server'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools, which are all distinct operations (image analysis, token counting, text embedding, text generation, model listing) rather than help/documentation functions.
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, timing considerations, or how it relates to the sibling tools (e.g., whether to use this before invoking other tools for guidance). The agent must infer usage context from the tool name and description alone.
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 but offers minimal information. It doesn't specify whether this is a read-only operation, if it requires authentication, what the output format looks like, or if there are rate limits. While 'List' implies a safe read operation, the lack of explicit behavioral details is a significant gap.
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 fluff or redundancy. It's appropriately sized for a simple listing tool and front-loads the essential information, 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?
For a simple listing tool with one well-documented parameter and no output schema, the description is minimally adequate. However, the lack of annotations means behavioral aspects like safety or output format are undocumented, and no usage guidelines are provided. This leaves gaps that could hinder an agent's ability to use the tool effectively in complex scenarios.
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 single parameter 'filter' well-documented via its enum values. The description adds no additional parameter semantics beyond what's in the schema, such as explaining the meaning of capabilities like 'grounding' or 'json_mode'. However, with high schema coverage, 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('all available Gemini models and their capabilities'), making the tool's purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'get_help' or 'analyze_image' beyond the obvious domain difference, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage 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 prerequisites, typical use cases, or comparison with sibling tools like 'get_help' for model information or 'generate_text' for model interaction, leaving the agent with minimal context for decision-making.
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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