gemini-mcp-server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: query for conversational AI, search/fetch for web retrieval, generate_image for image generation, generate_video/check_video for async video, generate_omni_video for sync video, generate_speech for TTS, generate_music for music, and reference_search for grounded Q&A. No two tools overlap in functionality.
Naming Consistency5/5
All tool names follow the verb_noun pattern (e.g., generate_image, check_video, reference_search) using imperative verbs and underscores consistently. No camelCase or mixed conventions.
Tool Count5/5
10 tools cover the server's multi-modal AI capabilities (text, web, images, videos, audio) without bloat. Each tool provides a necessary function, and the count is well-balanced for a comprehensive generative AI server.
Completeness5/5
The tool set covers the full lifecycle for each modality: generation, status checks where needed (via check_video for async video), and multiple generation backends (e.g., two video models). The query tool serves as a general-purpose agent. No obvious missing operations for the declared scope.
Average 4.2/5 across 10 of 10 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 12 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}
```
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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 must disclose behavioral traits. It only states 'Fetch the full contents' but does not mention whether the tool is read-only, any authentication requirements, rate limits, or error behavior. The description is insufficient for a tool with zero 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and to the point, consisting of two sentences. However, the second sentence ('Follows OpenAI MCP specification...') is unnecessary and provides no actionable information, slightly reducing efficiency. Still, it is appropriately sized for a simple tool.
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 fetch tool with one required parameter and no output schema, the description is moderately complete. It lacks information about return format, error conditions, or when to use this tool over siblings. Given the minimal context from other fields, a more detailed description 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 coverage is 100% for the single parameter 'id', so the schema already documents it. The description adds no additional semantic value (e.g., format constraints, examples) beyond reiterating that the ID identifies the document.
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 (fetch), the resource (full contents of a search result document), and the means (by ID). It is specific and distinguishes this tool from siblings like query or search, though it does not explicitly contrast them.
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 explicit guidance on when to use this tool versus alternatives. The description only mentions following the OpenAI MCP specification, which is vague and provides no practical usage context or prerequisites.
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, the description carries the full burden of behavioral disclosure. It only mentions the use of Gemini and result listing, omitting details like read-only nature, rate limits, source restrictions, or side effects.
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 short (three sentences) and front-loaded with purpose. However, the third sentence about 'OpenAI MCP specification' adds no operational value for the agent and could be considered extraneous.
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 single-parameter tool, the description covers purpose and basic output. Yet it lacks details on result format, pagination, or usage context, leaving gaps given the absence of an output schema.
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 coverage is 100% with the 'query' parameter having a description. The tool description adds no additional semantic value beyond what the schema already provides.
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 searches for information using Gemini and returns a list of results. However, it does not differentiate from the sibling tool 'query', which likely has overlapping functionality.
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 like 'query' or 'fetch'. The description lacks any context about appropriate use cases 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?
With no annotations, description carries the burden. It discloses multi-turn execution, automatic tool use, and multimodal support, but omits safety traits like cost, rate limits, error handling, or latency.
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?
Front-loaded with main purpose, followed by key capabilities. Minor redundancy (e.g., multi-turn and multimodal mentions), but overall efficient and well-structured.
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?
Covers core functionality but lacks output format details (e.g., response structure) and limits on tool calls. Given complexity and no output schema, more detail would improve 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 coverage is 100%, so baseline 3 applies. Description adds no extra meaning beyond schema descriptions (e.g., model, parts, sessionId are already explained in schema).
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?
Description clearly states it queries Google AI (Gemini models) with a prompt, specifying it as an intelligent agent with multi-turn and multimodal capabilities. It differentiates from sibling tools like 'search' (direct search) and 'generate_image' (media generation) by emphasizing agentic reasoning and automatic tool use.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage for complex queries requiring multi-step reasoning and automatic tool use, contrasting with siblings. Context is clear but lacks explicit when-not scenarios or direct comparisons, e.g., when to use 'search' instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses behavioral traits: supported models, constraints per model (e.g., max reference images, supported resolutions/aspect ratios), file types, output location, and base64 return. Lacks info on auth, rate limits, but is sufficient.
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?
Information is well-structured and front-loaded. Slightly long but every sentence adds value. Could be trimmed slightly without loss of clarity.
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?
For a complex tool with 8 parameters and no output schema, description covers model differences, constraints, output location, and return format. Lacks error handling details but is complete enough.
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?
Schema coverage is 100%, and description adds extra context (e.g., model tiers, limitations, supported file types) that goes beyond schema descriptions, enriching parameter meaning.
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?
Clearly states 'Generate images using Gemini's native image generation' and lists specific models. Distinguishes from sibling tools like generate_speech and generate_video by focusing on 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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides detailed guidance on which model to use for specific needs (e.g., fast/low-cost tier, required model for certain resolutions/aspect ratios). However, does not explicitly compare against sibling tools like generate_video, but context makes it clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that generation takes 1-3 minutes, returns an operationId immediately, and lists limitations like 'Audio file references are not supported'. It is transparent about async behavior but could mention authentication requirements or 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured paragraph. It front-loads the essential action ('Returns an operationId immediately') and then provides detailed options and constraints without redundancy. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite 17 parameters and no output schema, the description covers the necessary context: modes, polling workflow, unsupported features, file types, and combination rules. It equips the agent to select and invoke the tool appropriately.
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?
Schema description coverage is 100%, so baseline is 3. The description adds significant value by summarizing common usage patterns (text-to-video, image-to-video, interpolation) and providing constraints (e.g., 'videoPath must be MP4 from a previous Veo 720p generation'). This helps the agent understand parameter relationships beyond the schema.
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 it starts video generation using Google's Veo models, specifying multiple modes (text-to-video, image-to-video, etc.). It distinguishes itself from sibling tools like generate_image and check_video by focusing on video generation and referencing the polling workflow.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit instructions: 'Use check_video with the operationId to poll for completion' and recommends a 30-second polling interval. It also clarifies what is not supported (audio file references). However, it does not explicitly state when to avoid this tool in favor of alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears full responsibility. It discloses key behaviors: text-only input, 32k-token limit, no streaming, output saved to a specific path and returned as MCP audio content.
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 encapsulate all essential information without redundancy. The most critical purpose is front-loaded, making it easy for an agent to quickly grasp the tool's function.
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 5 parameters and no output schema, the description covers input constraints, output location, and return type. It could mention output length or multi-speaker prompt formatting, but overall it's sufficiently complete.
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?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining model defaults, backend-specific IDs, and the nature of the speakers array, exceeding baseline expectations.
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 it generates speech audio using Gemini TTS models, specifying support for single and two-speaker. It distinguishes from sibling tools like generate_music or generate_video by focusing on text-to-speech.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: input is text-only, no audio/image/video references, 32k-token limit, no streaming. While it doesn't explicitly contrast with alternatives, it clearly defines when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It fully discloses polling behavior, possible statuses ('running', 'completed', 'failed'), and the output location for completed videos. No contradictions.
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, no wasted words, key information front-loaded. Efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (one parameter, no output schema), the description is complete. It covers purpose, behavior, polling guidance, and output location. No gaps.
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 schema covers the single parameter 'operationId' with 100% coverage. The description adds context by linking it to generate_video, which is helpful. While it doesn't add extensive new information, it clarifies usage.
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 checks the status of a video generation operation. It distinguishes from sibling tools like generate_video, which initiates an operation, by focusing on polling for completion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool (after generate_video) and provides polling guidance (wait ~30 seconds if status is 'running'). It does not explicitly state when not to use it, but the context makes it clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations, so description must fully disclose behavior. It covers clip duration, generation limits, language support, output MIME types, image input formats, output path, and return type as MCP audio content. Very transparent.
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?
Description is dense but efficient; every sentence adds value. Could be slightly more structured (e.g., separate paragraphs for models, inputs, outputs), but not overly long.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 11 parameters and no output schema, the description covers all key aspects: model variants, duration limits, languages, input types, output path, and return type. No missing critical information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% coverage, yet description adds significant value: explains fixed 30s for clip vs pro, audio specifications (44.1 kHz, stereo), and limitations (no negative prompting, audio/video refs not accepted). Enhances schema meaning.
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?
Clearly states 'Generate music using Lyria models' – a specific verb+resource. Distinguishes from siblings like generate_image, generate_speech, etc., which are for different modalities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides detailed guidance on model selection (clip vs pro), language directions, output formats, and limitations (no negative prompting, no audio/video references). Does not explicitly state when not to use, but covers context well.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses key behaviors: synchronous execution (no polling), 720p output, auto-generated audio, no audio input, supported image types, and response fields (interactionId, file path). No contradictions exist.
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 dense but well-organized, front-loading the core purpose and then detailing paths, constraints, and response. While slightly verbose, every sentence adds value. Minor conciseness improvements could be made.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains the response format (interactionId, saved file path). It covers all necessary aspects: model, modes, inputs, constraints, process, and output. It also differentiates from sibling tools effectively.
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?
Schema description coverage is 100%, so baseline is 3. The description adds meaningful context beyond the schema, such as the dual role of prompt (oneshot vs. edit), the omission of imagePaths for edits, and the 720p constraint for aspectRatio.
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 generates or conversationally edits short videos using Gemini Omni Flash, distinguishing it from sibling tools like generate_video (which uses a different model and polling). It specifies two distinct paths (oneshot and interactive editing) and lists key capabilities.
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?
Explicit guidance is provided on when to use each path: oneshot for new video (text-to-video or with images) and interactive editing by setting previousInteractionId. Constraints like max 7 images, no audio references, and supported aspect ratios are clearly stated, and it implicitly distinguishes from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It exhaustively discloses return fields (answer, citations, supports, searchQueries, searchSuggestionsHtml) and backend-specific behaviors for parameters like excludeDomains, blockingConfidence, timeRange, and URL context constraints.
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 dense paragraph. It is front-loaded with purpose and all information is relevant, but it could be slightly more scannable with bullet points. Nonetheless, every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description thoroughly explains all return fields and their structure. It covers both backends, parameter constraints, and required markup. The tool is complex (9 params, nested objects), and the description fully informs the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
While schema coverage is 100%, the description adds crucial context beyond schema: it explains backend-specific availability for parameters like urls, timeRange, excludeDomains, blockingConfidence, and the meaning of includeImages. It also details the behavior of each parameter beyond the schema descriptions.
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: 'AI-assisted reference search: answer a question from live web sources using Gemini's Google Search grounding, and return organized citations.' It distinguishes itself from sibling tools like 'search'/'fetch' by noting it composes a synthesized answer and returns structured citations in one call.
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 contrasts this tool with 'search'/'fetch' connector tools, explaining that this composes a synthesized answer with citations. It also provides backend-specific guidance for Vertex AI vs Google AI Studio, helping the agent choose appropriate parameters.
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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