VeoMCP

Server Quality Checklist

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Latest release: v0.1.10
Disambiguation4/5
Most tools are clearly distinct, especially the generation and informational tools. The only potential confusion is between veo_get_task and veo_get_tasks_batch, though the descriptions do clarify single vs. batch querying.
Naming Consistency3/5
The veo_ prefix is consistent, but naming patterns diverge: most tools use verb_noun (get_task, list_models), while others use noun_to_video (image_to_video, text_to_video) and get_1080p breaks the noun convention. This mixed style is still readable but not fully consistent.
Tool Count5/5
8 tools is a well-scoped set for a video generation API, covering generation, status checking, model listing, prompt guidance, and resolution upgrades. Each tool serves a clear purpose without bloat.
Completeness4/5
Core workflows are covered: text-to-video, image-to-video, status polling (single and batch), and 1080p retrieval. Minor gaps exist, such as no explicit cancel or delete operation, but the essential lifecycle for generating and retrieving videos is present.
Average 4.2/5 across 7 of 8 tools scored. Lowest: 3.6/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 14 commits in the last 12 weeks
- Last stable release on August 28, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any side effects, costs, rate limits, or asynchronous behavior beyond vague mention of returning a task ID. It lacks transparency about operational implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose, redundantly repeating schema details in narrative form. Although structured with sections, it could be much more concise without losing information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the schema's richness, the description covers purpose, usage, and return values adequately. However, it omits potential error scenarios, limitations, or additional operational context, making it only moderately 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?
Schema covers all parameters with descriptions, achieving 100% coverage. The description adds minimal new meaning—it repeats schema info but does not clarify undefined aspects. Baseline of 3 is appropriate.
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 AI video from reference images and explicitly distinguishes it from text-to-video by mentioning the alternative veo_text_to_video. The purpose is unambiguous.
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?
Provides explicit 'Use this when' conditions and contrasts with text-only generation. Clearly guides when to choose this tool over alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral transparency. It mentions that the tool 'creates a video from scratch' and returns a 'Task ID and generated video information including URLs and state,' which implies asynchronous behavior, but it does not explicitly state that generation is non-blocking or that polling is required. It also fails to mention authentication, rate limits, or costs. The description gives some context but misses key behavioral traits for a generation tool.
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 well-structured with a clear opening, a 'Use this when' bullet list, and a 'Returns' section. It is concise—two paragraphs plus bullets—and front-loaded with the main purpose. The only minor inefficiency is that the first sentence is somewhat redundant with the tool name, but overall it earns its place without excessive verbosity.
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 complexity (6 parameters, video generation, potential async), the description covers the usage context and alternatives well, but it lacks explicit details on asynchronous workflow (polling vs. callback), any limitations, and does not describe the output schema beyond a one-line mention of 'Task ID and generated video information including URLs and state.' Since there is no output schema provided and no annotations, the description should compensate more by explaining the async nature and return structure in detail.
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 baseline is 3. The description does not add any parameter-specific semantics beyond the schema's already detailed field descriptions (e.g., examples for prompt, model variant differences, resolution options). While the description mentions 'maximum creative freedom' which relates to prompt usage, it doesn't elaborate on any parameter behavior that the schema doesn't already cover. Thus, it adds minimal value 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 the tool's purpose: 'Generate AI video from a text prompt using Veo.' It uses a specific verb ('generate') and resource (text prompt → video), and distinguishes itself from the sibling tool veo_image_to_video by explicitly noting the alternative for image-based generation. The use-case bullets further clarify the intended scope.
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?
Provides explicit usage guidance with a 'Use this when' list that details appropriate scenarios (creating video from text, no reference image, maximum creative freedom). It also gives a direct exclusion and alternative: 'For video generation starting from an image, use veo_image_to_video instead.' This clearly differentiates from the sibling tool.
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 and does well by documenting the three task states ('processing', 'succeeded', 'failed'), noting that failures include an error message, and stating that results contain URLs and metadata. It stops short of explaining data retention or polling implications, but the core behavior is 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?
The description is well organized with a short summary, usage bullets, task-state list, and return-value note. There is minor redundancy between the opening paragraph and the use-when bullets, but it is compact and scannable.
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 simple task-status tool with an output schema, the description is mostly complete: it covers when to use it, expected states, and return content. The main gap is that both parameters are marked optional while the description implies a task ID is needed, but this is minor given the schema and 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 description coverage is 100% and already explains task_id and trace_id. The description adds task-level context but does not provide additional param-specific semantics beyond what the schema already states.
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 opens with a specific verb and resource: 'Query the status and result of a video generation task.' It clearly distinguishes this single-task query tool from generation tools like veo_text_to_video and batch tool veo_get_tasks_batch.
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 explicit 'Use this when' bullets covering completion checks and URL retrieval. It does not explicitly name alternatives or state when not to use this tool, but the context is clear and sufficient.
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 behavioral burden. It discloses the succeeded-state requirement, the return payload (task ID and new video URL), and the lower-resolution default. It does not mention asynchronous behavior beyond the callback_url hint or potential costs, but it covers the most critical operational constraint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose, followed by compact context, a clearly formatted 'Use this when' list, a single note, and a return summary. Every sentence adds distinct value with no filler or redundancy.
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 tool with three parameters, a rich schema, and an output schema, the description adequately covers the main precondition and result. It could be more complete by explicitly contrasting with veo_get_task or noting any asynchronous/cost implications, but the information provided is sufficient for correct use in most cases.
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%, and each parameter is already well documented in the schema, including the distinction between video_id and task_id, the need to pass the same model, and callback_url's optionality. The description adds no parameter-level detail beyond what the schema provides.
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 opens with a specific verb and resource: 'Get the 1080p high-resolution version of a generated video.' This clearly differentiates from sibling tools like veo_text_to_video or veo_get_task by focusing on upscaling an already generated video. The added context about Veo's default lower resolution further clarifies the tool's role.
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 includes an explicit 'Use this when' list covering production needs, completed generation, and clearer output, plus a key precondition (video must be in 'succeeded' state). It does not name alternative tools or explicitly state when not to use it, so it stops short of a 5.
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?
No annotations are provided, so the description must carry the burden. It clearly frames the tool as a non-destructive 'query' operation, states that it returns status and video information, and emphasizes batch efficiency. It does not discuss auth, rate limits, or failure behavior, but the read-only intent is clear.
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 concise and front-loaded, with a clear opening statement and structured 'Use this when' bullets. It is slightly repetitive ('efficiently' appears in two sentences), but every section earns its place and supports quick scanning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has seven optional parameters and an output schema, the description supplies the necessary overall behavior: batch status checking and returned video information. Parameter details are left to the schema, which is acceptable here because the schema descriptions are complete and meaningful.
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 general context about querying multiple tasks, but it does not elaborate on each parameter. Schema description coverage is 100%, so every parameter already has a meaningful description; the tool text simply reinforces the batch-facing purpose.
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 uses a specific verb (query) and clearly identifies the resource (multiple video generation tasks), immediately distinguishing it from the single-task sibling veo_get_task. The phrase 'More efficient than calling veo_get_task multiple times' further clarifies its unique role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit 'Use this when' scenarios, such as checking multiple pending generations or tracking a batch. It also names the alternative veo_get_task and explains when this batch variant is preferable, giving clear usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It implies a read-only operation by stating it lists and categorizes, but it does not explicitly confirm safety, idempotency, or any side effects. With no annotations, the agent lacks explicit behavioral guarantees.
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 three concise sentences, front-loaded with the core purpose, and every sentence adds value. No redundancy or wasted words.
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 zero parameters, a clear purpose, and the existence of an output schema (indicated by 'Returns: ...'), the description sufficiently explains what the tool does and its output. No critical information is missing for a listing operation.
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?
There are no parameters, so schema coverage is 100% by default. The description adds no parameter-specific info (not needed), but explains the return structure, which is adequate. Baseline for 0 parameters is 4.
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 'List all available Veo API actions and corresponding tools', establishing a specific verb and resource. It distinguishes from sibling tools by being a meta-reference rather than an action tool.
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 indicates it is 'Helpful for understanding the full capabilities of the Veo MCP', providing clear context for when to use it. However, it does not explicitly exclude inappropriate uses or mention alternatives, which is acceptable for a listing tool.
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?
Describes the return as a table and includes model specifics, but does not explicitly mention side effects or confirm read-only behavior; no annotations are available to cover this.
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?
Structured with a purpose statement, model comparison list, and return description; concise and well-organized without unnecessary details.
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?
The description covers the purpose, output format, and model capabilities, sufficient for a simple list operation; no error cases are mentioned but not needed for this clarity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, so the description does not need to explain them; it adds clarity about the output, meeting the baseline for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists all available Veo models and their capabilities, with a specific verb and resource, distinguishing it from sibling tools like veo_get_task.
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?
Explicitly instructs 'Use this to understand which model to choose for your video generation', providing a clear use case for when to call this tool.
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?
The description fully discloses that the tool is read-only and returns a guide with prompt structure, examples, and tips, with no side effects or hidden behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, with a clear first sentence and brief elaboration, earning its place without waste.
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 tool's simplicity (no parameters, output schema provided), the description fully covers what the tool does and returns, making it 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?
With no parameters and 100% schema coverage, the description does not need to add parameter semantics; baseline 4 is appropriate.
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 provides guidance on writing effective prompts for Veo video generation, distinguishing it from sibling tools that handle actual video generation or task management.
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 implies the tool should be used before generating videos to improve prompt quality, but does not explicitly state when not to use it or mention alternatives.
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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