KlingMCP

Server Quality Checklist

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Latest release: v0.1.20
Disambiguation5/5
Each tool has a clearly distinct purpose: text-to-video, image-to-video, motion transfer, video extension, task status retrieval (single and batch), and listing actions/models. No overlap.
Naming Consistency5/5
All tools follow a consistent 'kling_verb_noun' pattern in snake_case, e.g., kling_generate_video, kling_get_task, kling_list_models. Perfectly predictable.
Tool Count5/5
8 tools is ideal for a video generation API. They cover generation, status checking, and information listing without being overwhelming or insufficient.
Completeness4/5
Covers core generation methods (text, image, motion, extension) and status queries. Minor gap: no tool for deleting tasks or listing all tasks, but the batch query covers most use cases.
Average 4.2/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 20 commits in the last 12 weeks
- Last stable release on August 10, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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": [
    "your-github-username"
  ]
}
```
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. It explains that Kling generates smooth motion between frames and returns a task ID with video info. However, it does not disclose async behavior, error handling, or rate limits, leaving some behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with paragraphs and bullet points, but it repeats 'start_image_url is required. end_image_url is optional.' in two places. Otherwise, it is concise and front-loaded.
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?
While the description covers the main purpose and usage scenarios, it lacks details about polling, error codes, or constraints like file size. Given the complexity (15 parameters) and existence of an output schema, more 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?
Schema description coverage is 100%, so baseline is 3. The description adds value by stating that start_image_url is required and end_image_url is optional, but contradicts the schema (which only requires prompt). This slight inconsistency reduces the score.
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 'Generate AI video using reference images as start and/or end frames.' This is a specific verb-resource description that distinguishes this tool from siblings like kling_generate_video (text-to-video) and kling_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 Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a bullet list of explicit use cases (e.g., 'You have a specific image you want to animate'). It does not explicitly contrast with sibling tools, but the context is clear enough for an AI agent to decide when to use this 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?
No annotations are provided, so the description carries full burden. It explains the core behavior (extracting and applying motion) and mentions return value (Task ID and motion information). However, it lacks details on async processing, polling, error states, or auth requirements. The schema covers parameters well, but the description adds minimal extra behavioral context.
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 with 4 sentences plus a bulleted list. It is front-loaded with purpose and use cases. The 'Returns' section adds value. Could be slightly streamlined but overall well-structured without verbosity.
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 7 parameters (2 required), enums, and an output schema, the description provides adequate context: purpose, use cases, return type. It lacks guidance on async behavior, callback usage, or error handling, which would improve completeness for a generation tool. Still, it covers the essential aspects.
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 the schema already provides detailed explanations for all 7 parameters. The description does not add significant meaning beyond the schema; it only implicitly references image_url and video_url in the purpose statement. 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 'Transfer motion from a reference video to a character image' using specific verb+resource. It distinguishes from sibling tools like kling_generate_video and kling_generate_video_from_image by focusing on motion transfer from video to a static image.
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 a 'Use this when' list with three clear use cases (animating character image, creating dance/movement video, transferring specific movements). While it does not explicitly mention when not to use or list alternatives, the guidance is clear and actionable.
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 provided, the description carries the full burden of behavioral disclosure. It mentions the output is a video with synchronized lips/expressions/head movements and returns a Task ID, hinting at asynchronous operation. However, it does not detail the asynchronous workflow, potential failure modes, or any prerequisites beyond providing image and audio URLs.
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: a clear one-sentence summary, a 'Use this when' list with three bullet points, and a 'Returns' line. Every sentence adds value, and the information is front-loaded.
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 output schema exists and the input schema is fully documented, the description provides sufficient context for the tool's core function. It could be improved by explicitly stating the async task workflow (e.g., use kling_get_task to fetch results), but the 'Returns Task ID' line partially covers this.
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 what the schema already provides, which is acceptable since the schema thoroughly documents all seven 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 the tool animates a portrait photo to match an audio track, generating a talking-head video. It uses specific verbs ('Animate', 'generates') and describes the resource (portrait photo + audio). This distinguishes it from generic video generation tools.
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 'Use this when' section provides explicit use cases: creating talking-head videos, making a person speak, and quick avatar videos. It does not explicitly name alternatives or exclusion criteria, but the context is clear enough for an agent to decide when to invoke this 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?
No annotations are provided, so the description bears full responsibility. It mentions generating a video from text and returns a 'Task ID and generated video information'. However, it does not clarify whether the tool is synchronous or asynchronous, nor does it explain the polling workflow implied by sibling tools like kling_get_task.
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, well-structured with a title sentence, context paragraph, bullet points, and a clear returns clause. Every sentence adds value and no extraneous information is present.
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 (13 parameters, no annotations, presence of sibling tools for task retrieval), the description lacks important contextual information about the asynchronous workflow. It does not mention that after generation, one may need to poll with kling_get_task to obtain the final video URL. The output schema exists but does not cover the overall process.
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 baseline is 3. The description adds minimal parameter-specific value beyond generic instructions like 'just describe what you want'. It does not explain constraints or interactions that the schema already covers.
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 'Generate AI video from a text prompt' and distinguishes from the sibling tool kling_generate_video_from_image, which uses reference images. The verb and resource are specific and 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?
The description provides explicit 'Use this when:' bullet points and directly suggests the alternative tool for reference images: 'For using reference images (start/end frames), use kling_generate_video_from_image instead.' This is excellent guidance.
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 provided. The description mentions returning status and video information but does not disclose behaviors like error handling for invalid task IDs, rate limits, or concurrency. The batch size limit is explained in the parameter schema, not the description.
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 concise and well-structured with a main sentence, a bullet list of use cases, and a return statement. It is front-loaded with the primary purpose and contains no redundant information.
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 presence of an output schema (return structure defined elsewhere), the description adequately covers purpose, usage guidance, and efficiency benefits. It could mention potential errors or performance characteristics, but overall it is sufficient for a batch query tool.
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 parameter 'task_ids' well-described (list of IDs, max batch size 50). The tool description adds no additional meaning beyond the schema, so 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 it queries multiple video generation tasks at once, using a specific verb ('query') and resource ('multiple tasks'). It also distinguishes itself from the sibling tool 'kling_get_task' by noting it is more efficient for batch checking.
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 'Use this when' bullet points provide clear guidance on when to use this tool, such as checking multiple pending generations or tracking a batch. It implies when not to use (single task scenarios) by referencing the alternative 'kling_get_task'.
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 carry behavioral context. It mentions returning a Task ID (implying async) and appending content after original, but omits details on side effects, permissions, or failure modes.
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: one opening sentence, three bullet points, and a returns line. Every sentence adds value without 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 7-parameter tool with output schema, the description covers purpose, use cases, and return type. It lacks explicit limitations (e.g., max length, cost) but is otherwise adequate.
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% with good descriptions, but the tool description adds value beyond the schema by clarifying video_id source ('from a previous generation result') and valid duration values ('Supports 5 or 10').
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 explicitly states the tool extends an existing video with additional content, using clear verb-resource ('Extend an existing video'). It distinguishes from sibling generation tools by emphasizing 'continue a previously generated video'.
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 bullet points for when to use (e.g., video too short, continue story) but lacks explicit 'when not to use' or alternative tool references. The context is clear but exclusions are missing.
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 behavioral burden. It transparently explains the transformation ('replaces the speaker's lip movements'), the mode behavior (text2video generates audio via TTS), and the return shape (Task ID and lip-sync video information). It does not explicitly describe async behavior or error conditions, but the return of a Task ID implies polling/callback, and the callback_url parameter is present 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the core action, followed by a brief explanation, a 'Use this when' bulleted list, and a Returns line. Every sentence adds value and there is no fluff 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 11 parameters and an output schema, the description covers the essential context: purpose, input types (URL, task ID, audio, text), modes, use cases, and return. It omits explicit async execution notes and prerequisites (e.g., video must contain a face), but given the schema richness and output schema, this is adequate and above minimal viability.
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 every parameter having a description. The description adds only high-level context (e.g., 'takes an existing video by URL or task ID') that maps directly to video_url/video_id and audio_url/audio_file. It does not add meaning beyond the schema's parameter descriptions, earning the baseline of 3.
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: 'Synchronize lip movements in a video to match a given audio track or text.' It clearly distinguishes the tool from siblings like kling_generate_video and kling_talking_photo by explaining it replaces lips in an existing video. The two modes (audio2video and text2video) are explicitly described.
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 'Use this when' section explicitly lists three concrete scenarios (dubbing, replacing audio, talking-head from text), providing clear context for when to use the tool. However, it does not mention when not to use it or name alternative sibling tools, so it stops short of a full 5.
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 the disclosure burden. It states the tool returns a categorized list, implying a read-only operation, but does not explicitly confirm no side effects, authentication needs, or rate limits. This is minimally adequate.
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, but contains minor redundancy ('Reference guide...' adds little over the first sentence). It could be slightly more structured, but overall efficient.
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 has no parameters and an output schema exists, the description sufficiently covers its role. It explains the purpose, output nature (categorized list), and context (reference guide). No additional information is needed for agent understanding.
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 zero parameters and schema coverage is 100%. Per guidance, baseline is 4. The description adds no extra parameter info because none exist, but confirms the tool takes no inputs.
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: listing all available Kling API actions and corresponding tools. It is a specific verb-resource pair ('list actions') and distinguishes itself from sibling tools (e.g., kling_generate_video) by being a meta-reference.
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 ('Reference guide', 'helpful for understanding the full capabilities'), but does not explicitly state when not to use it or mention alternatives like kling_list_models. However, for a listing tool, this is adequate.
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, description carries full burden. It describes task states and that the tool returns video URLs and metadata. It does not cover rate limits or authentication, but for a simple query tool this 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections (Use this to, Use this when, Task states, Returns). It is front-loaded and 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 the tool has one parameter, an output schema, and clear task states, the description is complete. It covers purpose, usage, states, and expected return, meeting all contextual needs.
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 has 100% coverage on the single parameter, with a clear description of how to obtain task_id. The description adds little beyond the schema, so a 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 'Query the status and result of a video generation task' with a specific verb and resource. It distinguishes itself from sibling tools like kling_get_tasks_batch by focusing on a single task.
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 explicit when-to-use scenarios (check completion, retrieve URLs, get details). However, it lacks explicit when-not-to-use guidance or mention of alternatives for batch queries.
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 but description fully discloses behavior: it shows available models with capabilities and returns a table, with no side effects. It is transparent about being a read-only listing.
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?
Three succinct sentences that front-load the purpose, add usage context, and mention the return format. Every sentence is informative and no fluff.
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?
The tool has no parameters and an output schema, so the description adequately explains its purpose and usage. It is complete for a listing tool with no complexity.
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?
No parameters exist, so schema coverage is 100%. The description adds value by explaining the return table and use cases, which is not in 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 'List all available Kling models for video generation', which is specific and distinguishes it from sibling tools that handle generation or task queries.
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?
Explicitly says 'Use this to understand which model to choose for your video', providing clear guidance on when to use the tool, though it does not mention when not to use it or 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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