Kobold MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
Most tools have distinct purposes (e.g., text generation vs. image generation vs. utility functions), but there is some overlap between kobold_chat and kobold_complete (both for text generation) and kobold_token_count and kobold_detokenize (both token-related). Descriptions help clarify, but an agent might occasionally confuse similar tools.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with a 'kobold_' prefix and descriptive suffixes (e.g., kobold_generate, kobold_model_info). This uniformity makes the set predictable and easy to navigate, with no deviations in style.
Tool Count3/5
With 20 tools, the count is borderline high for a single server, as it covers diverse domains like text generation, image processing, audio, and utilities. While each tool serves a purpose, the scope feels broad, potentially overwhelming for agents focused on specific tasks.
Completeness4/5
The tool set covers core AI functionalities well, including text and image generation, audio processing, and model management. Minor gaps exist, such as no explicit tool for updating settings or managing generation parameters beyond aborting, but agents can work around these with the available tools.
Average 2.8/5 across 20 of 20 tools scored. Lowest: 2.1/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
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- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states 'Generate text with KoboldAI' without mentioning any behavioral traits such as rate limits, authentication needs, output format, or potential side effects. This leaves the agent with no insight into how the tool behaves beyond its basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence, 'Generate text with KoboldAI', which is front-loaded and wastes no words. It efficiently conveys the core purpose without unnecessary elaboration, though this brevity contributes to gaps in other dimensions.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the high complexity (10 parameters, 0% schema coverage, no annotations, no output schema), the description is completely inadequate. It doesn't explain parameter usage, behavioral context, or output expectations, leaving the agent ill-equipped to use this tool effectively in a server with many sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 10 parameters and 0% schema description coverage, the description adds no meaning beyond the input schema. It doesn't explain what parameters like 'max_length', 'temperature', or 'stop_sequence' do, nor does it provide context for the required 'prompt' parameter. The description fails to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate text with KoboldAI' states the basic action (generate text) and the system (KoboldAI), but it's vague about what type of text generation this provides compared to siblings like kobold_chat or kobold_complete. It doesn't specify if this is for story generation, completion, or another specific use case, leaving ambiguity.
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 kobold_chat or kobold_complete. The description offers no context, exclusions, or prerequisites, making it unclear how this tool differs from its siblings in the server list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It doesn't describe what the search returns (e.g., results format, limitations), rate limits, authentication needs, or error conditions. 'Search the web via DuckDuckGo' gives only basic function without 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 5 words, with zero wasted language. It's front-loaded with the core function, though this brevity comes at the cost of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 parameters with 0% schema coverage, no annotations, no output schema, and a web search function that typically returns complex results, the description is severely incomplete. It doesn't explain what the tool actually returns, how to interpret results, or any operational constraints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but fails to do so. It mentions no parameters at all, leaving both 'apiUrl' and 'query' undocumented in meaning or usage. The description adds no value beyond what the bare schema 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 action ('Search') and target ('the web via DuckDuckGo'), providing a specific verb+resource combination. However, it doesn't distinguish this web search tool from its siblings (which are mostly AI model interaction tools), missing full differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of when web search is appropriate compared to the sibling tools (like kobold_chat or kobold_generate), nor any prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool is for 'chat completion' and 'OpenAI-compatible,' which implies it generates text responses in a conversational format, but it lacks details on behavioral traits like rate limits, authentication needs, response format, or any side effects. This is a significant gap for a tool with multiple parameters and no output 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 extremely concise with just 'Chat completion (OpenAI-compatible)', which is front-loaded and wastes no words. Every part of the sentence contributes to the tool's identity, making it efficient and well-structured.
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 (6 parameters, no annotations, no output schema), the description is incomplete. It doesn't cover behavioral aspects, parameter usage, or output expectations, leaving gaps that could hinder an AI agent's ability to invoke the tool correctly. The brevity doesn't compensate for the lack of necessary details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate by explaining parameters. However, it adds no meaning beyond the schema—it doesn't clarify what 'apiUrl' defaults to, how 'messages' should be structured for chat, or the effects of 'temperature' and other settings. With 6 parameters and no param info in the description, it fails to provide necessary context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Chat completion (OpenAI-compatible)' states the tool's function as a chat completion service and mentions compatibility, which gives a general purpose. However, it's vague about what 'chat completion' specifically entails (e.g., generating responses in a conversation) and doesn't clearly distinguish it from sibling tools like kobold_generate or kobold_complete, which might have overlapping functionalities. It avoids tautology by not just repeating the name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, such as when to choose kobold_chat over kobold_generate or other siblings for chat-like interactions. This leaves the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the basic function without any information about side effects (e.g., whether it modifies state), performance characteristics (e.g., speed, limitations), error handling, or output format. For a tool with no annotation coverage, this is inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose, making it easy to parse quickly. Every word earns its place by conveying essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a conversion tool with 2 parameters), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It lacks details on behavior, parameters, output format, and usage context. While concise, it doesn't provide enough information for an agent to use the tool effectively without additional guesswork.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no descriptions for the two parameters. The description adds no parameter semantics beyond the schema—it doesn't explain what 'tokens' represents (e.g., token IDs from a specific model), what format 'apiUrl' expects, or any constraints. With low coverage, the description fails to compensate, leaving parameters poorly documented.
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 'Convert token IDs to text' clearly states the verb ('Convert') and resource ('token IDs to text'), making the purpose immediately understandable. It distinguishes from siblings like 'kobold_token_count' (which counts tokens) or 'kobold_complete' (which generates text), though it doesn't explicitly mention these distinctions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing token IDs from another process), context (e.g., post-processing after tokenization), or exclusions (e.g., not for generating new text). With many sibling tools for text generation and processing, this lack of guidance is a significant gap.
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. 'Transform existing image' implies mutation of input images, but it doesn't disclose behavioral traits like whether this is a generative AI process, what happens to the original images (e.g., are they modified in place or used as reference), potential rate limits, or authentication needs. The description is minimal and leaves critical behavioral aspects unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single four-word sentence that's front-loaded with the core action. There's no wasted language or unnecessary elaboration, making it efficient for quick scanning.
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, AI image generation tool), lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how parameters interact, or the generative nature of the transformation. For a tool with this many configuration options, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 11 parameters and 0% schema description coverage, the description 'Transform existing image using prompt' adds minimal semantic value. It mentions 'prompt' and implies 'init_images' through 'existing image', but doesn't explain the purpose of other parameters like negative_prompt, denoising_strength, or sampler_name. The description fails to compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Transform existing image using prompt' clearly states the action (transform) and resource (existing image), but it's vague about what transformation entails and doesn't differentiate from sibling tools like kobold_txt2img (text-to-image) or kobold_interrogate (image analysis). It provides basic purpose but lacks specificity about the AI image generation context.
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 about when to use this tool versus alternatives. With siblings like kobold_txt2img (text-to-image generation) and kobold_interrogate (image analysis), the description doesn't indicate this is specifically for image-to-image transformation or when one would choose this over text-to-image generation. Usage context is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool generates images but doesn't mention performance aspects (e.g., generation time, rate limits), authentication needs, or output behavior (e.g., image format, size limits). This leaves significant gaps for a tool with 9 parameters and no output 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 extremely concise with a single, front-loaded sentence: 'Generate image from text prompt'. There is no wasted verbiage, and it directly communicates the core function without unnecessary elaboration.
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 (9 parameters, no annotations, no output schema), the description is incomplete. It doesn't address key aspects like the tool's dependencies (e.g., API setup), output format, error handling, or how parameters interact. For a generative AI tool with multiple configuration options, this leaves too much unspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for its 9 parameters, and the description adds no semantic information beyond the tool's purpose. It doesn't explain what parameters like 'cfg_scale', 'sampler_name', or 'seed' mean, nor does it provide context for defaults or constraints (e.g., valid ranges for width/height). This fails to compensate for the low schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Generate image from text prompt' clearly states the tool's function (text-to-image generation) but is vague about specifics like the AI model or output format. It distinguishes from siblings like kobold_chat or kobold_tts by focusing on image generation, but lacks detail on how it differs from kobold_img2img (text-to-image vs. image-to-image).
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. It doesn't mention sibling tools like kobold_img2img for image-to-image tasks or kobold_generate for text generation, nor does it specify prerequisites (e.g., needing an API running). The description implies usage for image generation but offers no context for selection.
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 full burden for behavioral disclosure. It only states the basic function and compatibility, missing critical information like whether this is a read-only operation, what authentication is required, rate limits, response format, or error behavior. For a tool with 6 parameters and no annotations, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just 4 words, with zero wasted language. It's front-loaded with the core function and includes important compatibility information. Every word earns its place in this minimal description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a text completion tool with 6 parameters, no annotations, no output schema, and 0% schema description coverage, the description is inadequate. It doesn't explain what the tool returns, how to interpret parameters, or provide any behavioral context. The OpenAI-compatibility hint is helpful but insufficient for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for all 6 parameters, the description provides no information about what apiUrl, prompt, max_tokens, temperature, top_p, or stop mean. The description doesn't compensate for this complete lack of parameter documentation in the schema, leaving all parameters semantically undefined.
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 'Text completion (OpenAI-compatible)' clearly states the tool's function as text completion and provides important context about API compatibility. It distinguishes itself from siblings like kobold_chat (likely conversational) and kobold_generate (potentially broader generation), though it doesn't explicitly contrast with 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 guidance is provided about when to use this tool versus alternatives like kobold_chat or kobold_generate. The description mentions OpenAI-compatibility which implies usage patterns, but doesn't explicitly state when this tool is appropriate versus other completion/generation tools in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Generate caption') but lacks details on permissions, rate limits, error handling, or what the output looks like (e.g., format, length). For a tool with no annotation coverage, this is insufficient to inform the agent about key behavioral traits beyond the basic operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at three words, front-loading the core purpose without any fluff. Every word ('Generate', 'caption', 'image') earns its place by directly contributing to understanding the tool's function, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (image processing with 2 parameters), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't address parameter meanings, behavioral aspects, or output expectations, leaving significant gaps for the agent to infer usage correctly in this context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning parameters 'apiUrl' and 'image' are undocumented in the schema. The description adds no information about these parameters—it doesn't explain what 'image' should contain (e.g., file path, base64 data) or what 'apiUrl' is for. With low coverage and no compensation in the description, this leaves parameters largely ambiguous.
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 'Generate caption for image' clearly states the verb ('Generate') and resource ('caption for image'), making the purpose immediately understandable. It distinguishes from most siblings (e.g., kobold_chat, kobold_generate) by specifying image captioning, though it doesn't explicitly differentiate from kobold_img2img or kobold_txt2img which are also image-related but serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like kobold_img2img (for image-to-image generation) or kobold_transcribe (for audio transcription), nor does it specify prerequisites such as needing an image input or when caption generation is appropriate. This leaves the agent with minimal context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. 'Count tokens in text' implies a read-only operation that returns a number, but it doesn't specify whether this requires API authentication, has rate limits, what format the count is returned in, or potential errors (e.g., for empty text). For a tool with no annotation coverage, this leaves significant 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at just three words, with zero wasted language. It's front-loaded with the core purpose and avoids unnecessary elaboration. For a simple tool, this brevity is appropriate and efficient.
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 moderate complexity (2 parameters, no output schema, no annotations), the description is incomplete. It doesn't address what the tool returns, how errors are handled, or the role of the apiUrl parameter. While conciseness is good, the description lacks essential context for reliable agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'text' implicitly but doesn't explain the 'apiUrl' parameter or provide any details about parameter formats, constraints, or examples. With 2 parameters (one required) and no schema descriptions, the description adds minimal value beyond what's inferred from the tool name.
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 'Count tokens in text' clearly states the verb ('Count') and resource ('tokens in text'), making the purpose immediately understandable. It distinguishes from siblings like kobold_chat or kobold_generate by focusing on token counting rather than generation or conversation. However, it doesn't specify what 'tokens' refer to (e.g., AI model tokens vs. other types), which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention use cases like preprocessing text for model input, checking token limits, or comparing with siblings like kobold_detokenize. Without any context about when this tool is appropriate, the agent must infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the core function but omits critical details: it doesn't specify if this is a read-only operation, potential rate limits, authentication needs, or what happens with invalid inputs. For a tool with 3 parameters and no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at three words, front-loaded with the core action. There is zero waste or redundancy, making it efficient for quick scanning, though this brevity contributes to gaps in other dimensions.
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 (3 parameters, no output schema, no annotations), the description is incomplete. It doesn't cover parameter meanings, behavioral traits, or usage context. For a transcription tool that likely involves audio processing and API interaction, more detail is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate but adds no parameter information. It doesn't explain what 'audio' expects (e.g., file path, base64 data, URL), what 'apiUrl' is for, or how 'language' affects transcription. With 3 undocumented parameters, the description fails to provide necessary semantics beyond the bare schema.
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 'Transcribe audio using Whisper' clearly states the verb ('transcribe') and resource ('audio'), specifying the technology used ('Whisper'). It distinguishes from siblings like 'kobold_tts' (text-to-speech) and 'kobold_chat' (chat), but doesn't explicitly differentiate from other audio-related tools like 'kobold_interrogate' (which might analyze audio differently).
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. It doesn't mention prerequisites, limitations, or scenarios where other tools (e.g., 'kobold_interrogate' for audio analysis or 'kobold_tts' for speech generation) might be more appropriate. The description lacks context for selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool generates audio but fails to mention critical traits: whether it's a read-only or mutating operation (though 'Generate' implies creation), potential rate limits, authentication needs (implied by apiUrl but not explicit), or what happens on failure. This is a significant gap for a tool with multiple parameters and no output 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 a single, efficient sentence with zero waste—'Generate text-to-speech audio' is front-loaded and directly conveys the core function. Every word earns its place, making it easy to parse quickly without unnecessary elaboration.
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 (4 parameters, no output schema, no annotations), the description is incomplete. It doesn't cover behavioral aspects like error handling, output format (e.g., audio file type), or integration details (e.g., how apiUrl connects to Kobold). For a TTS tool with multiple configurable inputs, this minimal description fails to provide sufficient context for reliable use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'text-to-speech' but adds no meaning beyond what the schema names imply (e.g., 'text' is the input, 'voice' and 'speed' affect output). It doesn't explain parameter roles, valid values (e.g., voice options), or defaults (apiUrl has a default in schema but not described). This leaves key semantics unclear.
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 'Generate text-to-speech audio' clearly states the verb ('Generate') and resource ('text-to-speech audio'), making the tool's purpose immediately understandable. It distinguishes itself from siblings like kobold_chat or kobold_transcribe by focusing on speech synthesis rather than text generation or transcription. However, it doesn't specify the exact output format or quality, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a running Kobold server), compare it to similar tools like kobold_generate for text, or specify use cases (e.g., converting text to audio for accessibility). This lack of context leaves the agent guessing about appropriate scenarios.
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. 'Get performance information' implies a read-only operation, but it doesn't disclose behavioral traits like authentication needs, rate limits, response format, or what 'performance' entails (e.g., system metrics, model speed). This leaves significant gaps for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise ('Get performance information')—a single phrase with no wasted words. It's front-loaded and appropriately sized for a simple tool, though this brevity contributes to vagueness in other dimensions.
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 no annotations, no output schema, and low parameter coverage, the description is incomplete. For a tool that likely returns performance data, it should specify what information is retrieved, format, or use cases to aid the agent 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?
With 0% schema description coverage and only 1 parameter (apiUrl), the description doesn't add parameter semantics, but the parameter count is low (1). The baseline for 0 parameters is 4, as minimal compensation is needed, though the description doesn't explain the apiUrl's purpose or default value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get performance information' states a clear verb ('Get') and resource ('performance information'), but it's vague about what specific performance metrics are retrieved. It doesn't distinguish from siblings like 'kobold_model_info' or 'kobold_version' that might also provide system-related data.
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. With many sibling tools for different operations (e.g., 'kobold_model_info', 'kobold_version'), the description offers no context on use cases, 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action but doesn't explain what 'token logprobs' are, whether this is a read-only operation, if it requires a prior request, or any performance implications. This leaves significant gaps in understanding the tool's behavior.
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 with no wasted words, making it highly concise and front-loaded. Every word contributes directly to stating the tool's purpose, earning its place efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of token logprobs in AI contexts, no annotations, no output schema, and low schema coverage, the description is insufficient. It doesn't explain the return format, prerequisites (e.g., needing a prior request), or how this fits with sibling tools, leaving the agent with incomplete context for effective use.
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 input schema has only one parameter ('apiUrl') with 0% description coverage, and the description doesn't mention parameters at all. Since there are zero parameters documented in the description, this meets the baseline of 4 for minimal parameter burden, but it doesn't add value beyond the schema's limited information.
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 ('Get') and the resource ('token logprobs from the last request'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'kobold_complete' or 'kobold_generate' that might also involve token processing, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as whether it's for debugging, analysis, or specific contexts. With many sibling tools like 'kobold_complete' and 'kobold_generate' that might overlap in token-related tasks, this lack of differentiation leaves the agent without clear usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states this is a 'Get' operation, implying it's read-only, but doesn't disclose behavioral traits like authentication needs, rate limits, or what the return value looks like (e.g., a numeric value or structured data). This is a significant gap for a tool with no 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that front-loads the essential information ('Get current max context length setting') with zero wasted words. It's appropriately sized for a simple retrieval tool.
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 low complexity (1 optional parameter, no output schema, no annotations), the description is incomplete. It doesn't explain the return value (e.g., what format the 'max context length' is in), which is critical for an agent to use the tool effectively. Without annotations or output schema, more context is needed.
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 description adds no parameter information beyond the input schema, which has 1 parameter with 0% schema description coverage. However, since there's only 1 optional parameter ('apiUrl'), the tool's core function doesn't rely on parameters, so the description adequately covers the semantics without needing to detail parameters. This justifies a score above the baseline.
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 ('Get') and the resource ('current max context length setting'), making the purpose understandable. It doesn't explicitly differentiate from sibling tools like 'kobold_max_length' or 'kobold_model_info', which might provide related settings, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'kobold_max_length' (which might handle a different max length parameter) and 'kobold_model_info' (which could include context info), there's no indication of when this specific tool is appropriate, leaving the agent to guess.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states what the tool does ('Get current max length setting') without explaining what 'max length' refers to (e.g., token length, context length), whether this is a read-only operation, what the output format might be, or any error conditions. This leaves significant gaps in understanding the tool's behavior.
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 with no wasted words. It's front-loaded with the core purpose and efficiently communicates the tool's function without unnecessary elaboration, making it easy to parse quickly.
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 context: no annotations, no output schema, and a simple but undocumented parameter, the description is incomplete. It doesn't explain what 'max length' means in this context (e.g., related to AI model generation), what the return value looks like, or how this tool fits with siblings like 'kobold_max_context_length'. For a tool in a complex AI-related server, this leaves too much unspecified.
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 input schema has 1 parameter (apiUrl) with 0% schema description coverage, meaning the schema provides no documentation for this parameter. The description doesn't mention parameters at all, which is acceptable since there's only one optional parameter (0 required) and the tool likely functions without it. However, it doesn't explain what the apiUrl parameter does or when it should be used, leaving a minor gap.
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 'Get current max length setting' clearly states the action (Get) and resource (max length setting), making the purpose understandable. However, it doesn't distinguish this tool from its sibling 'kobold_max_context_length', which appears related but has a different name, leaving some ambiguity about what specifically distinguishes these two tools.
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. With sibling tools like 'kobold_max_context_length' and 'kobold_model_info' that might provide related information, there's no indication of when this specific tool is appropriate or what context it serves compared to others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Get current model information,' implying a read-only operation, but doesn't specify what 'current model' means (e.g., active inference model, default settings), whether it requires authentication, rate limits, or what the output format might be. This is a significant gap for a tool with no 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description 'Get current model information' is a single, efficient sentence that front-loads the core action and resource. There's no wasted wording, making it appropriately concise and well-structured for its purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a tool with no annotations, no output schema, and 0% schema description coverage), the description is incomplete. It doesn't explain what 'model information' includes, how it differs from sibling tools, or behavioral aspects like response format. This leaves the agent with insufficient context to use the tool 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?
The input schema has one parameter ('apiUrl') with 0% description coverage, and the description doesn't mention parameters at all. However, since there's only one optional parameter (0 required), the baseline is high. The description doesn't add meaning beyond the schema, but the low parameter count minimizes the impact, keeping it from a lower score.
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 'Get current model information' clearly states the verb ('Get') and resource ('current model information'), making the purpose understandable. However, it doesn't differentiate from potential sibling tools that might also retrieve model-related data, such as 'kobold_sd_models' or 'kobold_version', which could provide overlapping or complementary information.
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. With multiple sibling tools like 'kobold_sd_models' (which might list available models) and 'kobold_version' (which could provide version info), there's no indication of whether this tool is for active model status, configuration details, or other specifics, leaving usage ambiguous.
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 full burden but only states it's a list operation. It doesn't disclose behavioral traits like whether it's read-only, requires authentication, has rate limits, or what the output format might be (e.g., list of model names or details). This is a significant gap for a tool with no 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 optional parameter) but lack of annotations and output schema, the description is incomplete. It doesn't cover what the tool returns (e.g., model list format), behavioral aspects, or usage context, leaving gaps for the agent to infer.
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 input schema has 1 parameter (apiUrl) with 0% description coverage, but the description doesn't mention parameters at all. Since there's only one optional parameter, the baseline is high, but the description doesn't add any semantic context beyond the schema, such as explaining the default API URL's purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'available Stable Diffusion models', providing a specific action and target. However, it doesn't differentiate from sibling tools like 'kobold_model_info' which might provide similar model-related information, keeping it from a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives such as 'kobold_model_info' or other model-related tools. There's no mention of prerequisites, context, or exclusions, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves version information but doesn't specify if this is a read-only operation, requires authentication, has rate limits, or what the output format might be. This is a significant gap for a tool with no structured safety hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter, no output schema, no annotations), the description is minimally adequate but incomplete. It covers the basic purpose but lacks details on behavior, output, or usage context, which are needed for full agent understanding in this server environment.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter (apiUrl) with 0% description coverage, but the tool description doesn't mention any parameters or add meaning beyond the schema. Since there's only one parameter and the tool likely has a simple purpose, the baseline score of 3 is appropriate, though no extra value is added.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('KoboldAI version information'), making it immediately understandable. However, it doesn't differentiate from siblings like 'kobold_model_info' or 'kobold_perf_info' that might also provide version-related data, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for version checks, or how it differs from sibling tools that might overlap in functionality, leaving usage decisions ambiguous.
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 full burden but only states the action without behavioral details. It doesn't disclose effects like whether it's destructive, requires specific permissions, or has side effects (e.g., stopping generation mid-process), leaving significant gaps in understanding tool behavior.
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, direct sentence with zero waste, front-loading the key action. It's appropriately sized for a simple tool, making it easy to parse and understand quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, no output schema, no annotations), the description is minimally adequate but incomplete. It lacks context on what 'generation' refers to or the outcome of aborting, which could be inferred from siblings but isn't explicitly covered.
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 input schema has one parameter (apiUrl) with 0% description coverage, but the tool likely requires no user-provided parameters for its core function. The description doesn't mention parameters, which is acceptable here as the default apiUrl suffices, but it doesn't add semantic value beyond the schema.
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 ('abort') and target ('currently ongoing generation'), which is specific and unambiguous. However, it doesn't explicitly differentiate from siblings like 'kobold_generate' or 'kobold_complete' that might initiate generations, though the purpose is distinct enough to infer.
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, such as whether it's for interrupting long-running processes or handling errors. It lacks context on prerequisites like needing an active generation, making it minimally helpful for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states it's a list operation, implying read-only behavior, but doesn't cover aspects like rate limits, authentication needs, error handling, or response format. This leaves significant gaps for a tool that interacts with an external API.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (single optional parameter, no output schema, no annotations), the description is adequate as a minimum viable explanation. However, it lacks details on behavioral traits like API interaction patterns or error handling, which would be helpful for completeness.
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 description doesn't mention parameters, but with only one parameter (apiUrl) and 0% schema description coverage, this is acceptable. The tool's purpose as a listing operation suggests the parameter is for API configuration, and the description doesn't need to compensate heavily for the minimal parameter set.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and resource ('available Stable Diffusion samplers'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'kobold_sd_models' which lists models rather than samplers, missing explicit sibling distinction.
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. The description doesn't mention prerequisites, context, or exclusions, leaving the agent with no usage instructions beyond the basic purpose.
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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