kling-mcp

Server Quality Checklist

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Latest release: v0.1.14
Disambiguation5/5
Each tool has a clearly distinct purpose: login for authentication, five different video generation types (ai_avatar, extend_video, image_to_video, motion_control, text_to_video), get_task for status retrieval, and check_pricing for cost lookup. No overlap in functionality.
Naming Consistency3/5
All names use lowercase with underscores, but the pattern is inconsistent: some are verb_noun (extend_video, get_task, check_pricing) while others are just task type names (ai_avatar, image_to_video, motion_control, text_to_video). This mixed convention could cause minor confusion about which are actions vs. resource types.
Tool Count4/5
With 8 tools, the server covers authentication, five distinct generation operations, status retrieval, and pricing. This is a reasonable scope for a video generation API, though slightly on the higher side but still well-scoped.
Completeness3/5
Core operations are present: login, generation, status, pricing. However, missing cancel or delete for tasks, and there is no listing of past tasks. These gaps may force agents to rely on external tracking or default to polling, limiting workflow completeness.
Average 3.3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 18 commits in the last 12 weeks
- Last stable release on July 31, 2026
- 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.
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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, the description carries full burden. It only states that the tool creates a task and returns an ID, status, and URLs. There is no disclosure about destructive actions, authorization requirements, rate limits, or whether the task is synchronous or asynchronous beyond the wait parameter in the schema.
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 sentence and very concise. However, conciseness is slightly compromised by underspecification; it could be a bit more informative without becoming verbose.
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 (11 parameters, no output schema, no annotations), the description is too brief. It fails to cover important contextual details such as the lifecycle of the task, expected behavior, or error handling.
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 high (82%), so baseline is 3. The description adds no additional meaning beyond what the schema provides; it does not explain any parameters. For this score, it is adequate given the schema's own descriptions.
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 creates a Kling task specifically for motion control, and mentions the output (task id, status, output URLs). However, it does not differentiate this tool from siblings like image_to_video or text_to_video, which are also video generation tasks.
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 such as image_to_video or text_to_video. The description does not specify prerequisites, exclusions, or context where this tool should be preferred.
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 discloses that the tool creates a task and returns specific fields, but it fails to mention behavioral traits like whether the operation is destructive (e.g., overwriting previous tasks), any authentication or rate limit constraints, or the nature of the 'status' field (e.g., polling behavior).
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, direct sentence that conveys the core action and outputs. While efficient, it could be improved by front-loading the return values or structuring into multiple sentences for clarity.
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 has 8 parameters and no output schema, the description is insufficient. It does not explain what constitutes a 'Kling task,' how to interpret the output fields, or the behavior of the 'wait' parameter. For a creator tool of this complexity, more context is needed to ensure correct 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 75%, meaning most parameters are already documented in the schema. The description adds no additional parameter context beyond the schema, so it meets the baseline of 3. It does not explain the purpose or constraints of parameters like 'wait' or 'timeout_ms' in relation to the task creation process.
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 action (Create a Kling task), the specific resource (ai avatar), and the return values (task id, status, output URLs). This distinguishes it from sibling tools like text_to_video or image_to_video, which focus on other video generation tasks.
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 does not mention prerequisites, such as requiring a specific source image or audio format, nor does it clarify when other sibling tools like text_to_video would 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, and the description only states it fetches data, without discussing side effects, idempotency, or error handling. Minimal behavioral disclosure.
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?
One sentence with no wasted words, efficiently conveying the tool's purpose.
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?
Adequate for a simple polling tool with two parameters, but lacks mention of error states or that it is a read-only operation.
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 the description adds no additional meaning beyond what the schema already provides for both 'action' and 'task_id'.
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 it fetches status and payload for a kling task with a specific verb and resource. However, it does not differentiate from sibling tools like ai_avatar or extend_video, which are separate actions.
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 on when to use this tool, such as after task creation or for polling. The description lacks context for appropriate usage vs alternatives.
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 must fully disclose behavioral traits. It only states the return values (task id, status, output URLs) but omits critical details such as whether the operation is synchronous or asynchronous, authentication requirements, error handling, or what happens to existing tasks. The default polling behavior (implied by wait parameter) is not mentioned.
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 at two sentences, with the primary action in the first sentence and the return values in the second. Every sentence adds value without unnecessary words.
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 complexity (19 parameters, no output schema), the description is insufficiently complete. It does not explain the main input fields (first_frame_image_url, prompt), the task lifecycle, or how to interpret the return values in conjunction with sibling tools like get_task. More context would be needed for an agent to use the tool effectively.
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 high (89%), so the schema itself documents most parameters. The description adds no additional parameter-specific meaning beyond the high-level action. A score of 3 is appropriate as the description does not compensate for any gaps in the schema coverage.
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 verb ('Create'), the resource ('Kling task'), and the specific domain ('image to video'). It effectively distinguishes the tool from siblings like text_to_video (text to video) and extend_video (extend existing video) by emphasizing the image-to-video focus.
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 does not provide any explicit guidance on when to use this tool versus alternatives such as text_to_video or extend_video. It lacks information about prerequisites (e.g., image URL requirements) or scenarios where the tool is inappropriate.
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 provided; description mentions returning task id, status, and output URLs but does not disclose async behavior, polling (via wait parameter), or side effects. Lacks detail on what happens behind the scenes.
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?
Single sentence, 15 words, directly states purpose. No unnecessary information.
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?
With 22 parameters, no output schema, and no annotations, the description is too sparse to fully inform an agent. Lacks explanation of which parameters are essential, how the task lifecycle works, or common use patterns.
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 91% with descriptions on most parameters; the description adds no extra meaning beyond 'create a task'. 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 creates a Kling text-to-video task on RunAPI, which distinguishes it from siblings like image_to_video and extend_video.
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 on when to use this tool vs alternatives (e.g., image_to_video, motion_control). No context about prerequisites or exclusions.
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 fails to mention whether the operation is read-only, requires authentication, has rate limits, or what kind of pricing data is returned (e.g., current or historical). This is insufficient for an agent to understand the tool's side effects or 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, concise sentence that front-loads the core purpose. It wastes no words, but could be slightly more informative without losing conciseness.
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 lack of output schema and annotations, the description is too minimal. It does not explain the return value format, whether the output is a list or single price, or any other contextual details that an agent would need to interpret the tool's result.
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% since both parameters have descriptions in the schema. The tool description adds no additional meaning beyond what the schema already provides. Baseline score 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's function: 'Look up RunAPI pricing for the kling model line.' It uses a specific verb ('look up') and resource ('pricing'), and the scope is narrowed to the kling model line, distinguishing it from sibling tools that perform actions (e.g., ai_avatar) or manage tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used when pricing information is needed, but it provides no explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. However, since no other sibling tool handles pricing, the lack of guidance is somewhat mitigated.
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 present, so the description must fully disclose behaviors. It only states it creates a task and returns outputs, but omits details like asynchronous nature, required permissions, error handling, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that conveys the tool's core function and outputs, with no unnecessary words.
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?
With 8 parameters and no output schema, the description lacks details on parameter interactions, task lifecycle, what constitutes a successful extension, and how output URLs are structured. It is too minimal for the tool's complexity.
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 75%, and the description adds no additional meaning beyond 'extend video'. The schema already documents parameters adequately, so the description does not improve parameter understanding.
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 creates a Kling task to extend a video, and mentions what it returns. This distinguishes it from sibling tools like text_to_video and image_to_video which are for generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for extending an existing video, but it does not explicitly say when to use it versus the generative siblings, nor does it mention prerequisites or exclusions.
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 provides good transparency: it mentions opening a browser (interactive flow), PKCE method, and saving to a config file. It also hints at the 'force' parameter for re-authentication, but doesn't cover idempotency or existing credential handling.
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 sentence that front-loads the core action ('Authenticate RunAPI') and efficiently conveys method and output location without extraneous 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 the tool's simplicity (one optional boolean parameter, no output schema, no nested objects), the description fully covers the essential context: what it does, how it does it, and where it stores results. No gaps remain.
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% for the single 'force' parameter. The tool description does not add further meaning beyond what the schema already provides, so baseline 3 applies.
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: authenticating RunAPI via a PKCE login flow and saving the API key to a config file. It distinctly differs from sibling tools (e.g., video-related), making selection unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description lacks explicit guidance on when or when not to use this tool vs alternatives. However, the context of sibling tools (all non-authentication) makes usage fairly clear, meeting a minimal viable standard.
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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