Ontology MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
There is significant overlap and confusion among tools, particularly in image generation (mcp_gemini_create_image, mcp_gemini_generate_image, mcp_gemini_generate_images, mcp_imagen_generate, mcp_openai_image) and text generation (mcp_gemini_chat_completion, mcp_gemini_generate_text, mcp_ollama_chat_completion, mcp_openai_chat). An agent would struggle to choose the right tool for tasks like 'generate an image' or 'chat with AI' due to unclear boundaries and redundant functionality across different providers.
Naming Consistency4/5
Tool names follow a consistent mcp_<provider>_<action>_<target> pattern throughout, with clear prefixes for providers (gemini, ollama, openai, sparql, http) and descriptive actions. Minor deviations exist (e.g., mcp_gemini_generate_images vs. mcp_gemini_generate_image, or mcp_openai_chat vs. mcp_gemini_chat_completion), but the overall structure is predictable and readable.
Tool Count2/5
With 28 tools, the count is excessive for a single server, indicating poor scoping. The server combines multiple domains (AI model interactions, HTTP requests, SPARQL queries) without clear integration, making it feel like a collection of unrelated utilities rather than a cohesive set. This many tools will overwhelm agents and increase misselection risk.
Completeness3/5
For each sub-domain (e.g., Gemini AI, Ollama, SPARQL), there is reasonable coverage of core operations like generation, listing, and updates. However, gaps exist, such as missing model management for OpenAI (no list or delete tools) and inconsistent support across providers (e.g., video generation only for Gemini). The broad scope makes it hard to assess completeness, but within sub-domains, agents can work around minor omissions.
Average 3.1/5 across 28 of 28 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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This repository includes a README.md file.
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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

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 mentions using an 'OpenAI-compatible API' but doesn't specify authentication requirements, rate limits, error handling, or what the response format looks like. For a chat completion tool with zero 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in Korean that directly states the tool's function. It's appropriately sized for a straightforward tool, though it could be slightly more informative without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a chat completion tool with no annotations and no output schema, the description is incomplete. It doesn't explain the return format, error conditions, or behavioral traits like whether it's read-only or destructive. For a tool with 4 parameters and significant functionality, more context is needed.
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 schema already documents all parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema, such as explaining the purpose of the 'model' parameter or how 'temperature' affects responses. Baseline 3 is appropriate when the schema does the heavy lifting.
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 states it 'generates responses using OpenAI-compatible chat completion API', which identifies the verb (generate) and resource (responses) but is vague about the specific implementation (Ollama). It doesn't distinguish from siblings like 'mcp_openai_chat' which likely serves a similar purpose, nor does it clarify it's specifically for Ollama models rather than general OpenAI API calls.
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 'mcp_openai_chat' or 'mcp_gemini_chat_completion'. The description doesn't mention any prerequisites, context, or exclusions for usage, leaving the agent to infer based on the tool 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?
No annotations are provided, so the description carries full burden for behavioral disclosure. While '텍스트를 생성합니다' (generates text) implies a non-destructive operation, it lacks critical details: required authentication, rate limits, cost implications, output format, error handling, or whether it's synchronous/asynchronous. For a generative AI tool with no annotation coverage, this is a significant gap.
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 in Korean that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality.
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 generative AI tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It lacks context about the tool's behavior (e.g., response format, limitations), usage scenarios, and differentiation from sibling tools, leaving significant gaps for an AI agent to understand when and how to invoke it correctly.
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%, providing clear documentation for all 6 parameters (model, prompt, max_tokens, temperature, topK, topP). The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.
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 states 'Gemini AI 모델을 사용하여 텍스트를 생성합니다' (Generates text using Gemini AI model), which clearly identifies the verb (generate) and resource (text) with the specific technology (Gemini). However, it doesn't distinguish this from sibling tools like 'mcp_gemini_chat_completion' or 'mcp_gemini_generate_multimodal_content', leaving ambiguity about when to use this versus other text generation options.
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 for text generation (e.g., mcp_gemini_chat_completion, mcp_gemini_generate_multimodal_content) and other AI services (e.g., mcp_openai_chat), there's no indication of this tool's specific use case, 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 full burden. '채팅 대화를 완성합니다' indicates a generative operation but doesn't disclose rate limits, authentication requirements, cost implications, response formats, or error behaviors. For a complex AI model interaction tool, 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 a single, efficient Korean sentence that states the core purpose without any wasted words. It's appropriately sized for a tool with comprehensive schema documentation.
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 complex AI model interaction tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'completes chat conversations' means in practice, what the response format looks like, error conditions, or how this differs from similar chat completion tools. The 100% schema coverage helps with parameters but doesn't compensate for missing behavioral context.
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 schema already documents all 6 parameters thoroughly. The description adds no additional parameter context beyond what's in the schema. The baseline of 3 is appropriate when the schema does all the parameter documentation work.
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 ('채팅 대화를 완성합니다' - completes chat conversations) and the resource (Gemini AI 모델). It distinguishes from image/video generation siblings but doesn't explicitly differentiate from other chat completion tools like mcp_ollama_chat_completion or mcp_openai_chat, which is why it's not a 5.
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 chat completion tools available (Gemini, Ollama, OpenAI), there's no indication of when this specific Gemini implementation should be chosen over others, nor any prerequisites or constraints mentioned.
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 mentions that generated text and image file paths 'must be communicated to the user,' which is a behavioral constraint, but lacks critical information about permissions, rate limits, costs, whether images are saved permanently, or what happens when generation fails. For a complex generation tool with 7 parameters, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences that efficiently convey the core functionality and a key behavioral requirement. It's front-loaded with the main purpose. However, the second sentence about communicating results to users could be integrated more smoothly with the first.
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 complex multimodal generation tool with 7 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns beyond 'generated text and image file paths' - no format details, error handling, or example outputs. The behavioral requirement to communicate results is noted, but other critical context like authentication, costs, or limitations is missing.
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 schema fully documents all 7 parameters. The description adds no parameter-specific information beyond what's in the schema descriptions. It mentions multimodal content generation generally but doesn't explain parameter interactions or provide examples. The baseline of 3 is appropriate when the schema does all the work.
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 uses Gemini models to generate multimodal content (text and images) and returns generated text and image file paths. It specifies the verb 'generate' and resource 'multimodal content', though it doesn't explicitly differentiate from siblings like mcp_gemini_generate_text or mcp_gemini_generate_image which handle single modalities.
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 mcp_gemini_generate_text (text-only) or mcp_gemini_generate_image (image-only), nor does it specify use cases where multimodal generation is preferred over single-modality approaches.
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 mentions that the tool returns a generated video file path that must be communicated to the user, which is useful. However, it lacks critical behavioral details: whether this is a read-only or write operation, potential rate limits, authentication requirements, file size implications, or what happens if generation fails. For a complex video generation tool with 9 parameters, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that each serve a purpose: stating the core function and specifying the return value requirement. It's front-loaded with the main purpose. However, the second sentence about file paths could be more integrated with the first for better flow.
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 complex video generation tool with 9 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns beyond file paths, doesn't mention error conditions, doesn't provide context about the Google Veo model's capabilities/limitations, and doesn't guide usage relative to similar tools. The agent would need to infer too much from the schema alone.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-specific information beyond what's already in the schema (which has 100% coverage). It doesn't explain relationships between parameters (e.g., how 'image' interacts with 'prompt'), provide examples, or clarify edge cases. With complete schema documentation, the baseline is 3, but the description doesn't enhance parameter understanding.
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: 'Google Veo 모델을 사용하여 비디오를 생성합니다' (uses Google Veo model to generate videos). It specifies the verb (generate) and resource (videos) with the model context. However, it doesn't explicitly differentiate from sibling tools like mcp_gemini_create_image or mcp_imagen_generate, which are also media 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 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 when video generation is appropriate compared to image generation tools (like mcp_gemini_generate_image) or other video-related tools. The only usage hint is about returning the file path, which is operational rather than contextual.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool downloads a model but lacks critical behavioral details: it doesn't specify if this is a network-intensive operation, whether it requires authentication, what happens if the model already exists, or what the output looks like (e.g., success/failure status). For a download tool with zero annotation coverage, this is a significant gap.
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 in Korean that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every part contributing essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (a download operation with potential network/authentication implications), no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like error handling, performance, or return values, leaving the agent with insufficient context for reliable use.
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%, with the single parameter 'name' documented as '다운로드할 모델 이름' (model name to download). The description doesn't add meaning beyond this, such as format examples or constraints (e.g., case sensitivity). Baseline 3 is appropriate when the schema does the heavy lifting.
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 ('다운로드합니다' - downloads) and resource ('모델' - model) from 'Ollama 레지스트리' (Ollama registry). It's specific about what the tool does, though it doesn't explicitly differentiate from sibling tools like mcp_ollama_list or mcp_ollama_run, 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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing an existing model name), exclusions, or comparisons to siblings like mcp_ollama_list (for listing models) or mcp_ollama_run (for running models).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the tool deletes a model but lacks critical behavioral details: whether deletion is permanent, requires specific permissions, affects other resources, or has side effects (e.g., freeing disk space). For a destructive operation with zero 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 a single, clear sentence in Korean that directly states the tool's purpose without unnecessary words. It is front-loaded and efficiently conveys the core action, making it easy to understand at a glance. No extraneous information is included.
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 destructive nature, lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permanence or permissions, return values (e.g., success confirmation or error details), or error handling. For a delete operation, more context is needed to ensure safe and correct usage.
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%, with the parameter 'name' documented as '삭제할 모델 이름' (name of the model to delete). The description adds no additional meaning beyond this, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema adequately defines the parameter.
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 'Ollama 모델을 삭제합니다' (Deletes an Ollama model) clearly states the action (삭제합니다/delete) and resource (Ollama 모델/Ollama model). It distinguishes from siblings like mcp_ollama_list, mcp_ollama_pull, and mcp_ollama_run, which perform different operations. However, it doesn't specify if it's for local or remote models, which could enhance 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. It doesn't mention prerequisites (e.g., needing an existing model), exclusions, or comparisons to siblings like mcp_ollama_list (to check available models) or mcp_ollama_pull (to add models). Usage is implied only by the verb 'delete'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions running a model to generate a response, implying a read-only operation, but lacks details on behavioral traits like rate limits, error handling, authentication needs, or what the response format looks like. This is inadequate for a tool with potential complexity.
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 in Korean that directly states the tool's function without any wasted words. It is 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.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It fails to address key contextual aspects like the response format, error conditions, or how it differs from similar sibling tools (e.g., 'mcp_ollama_chat_completion'), leaving significant gaps for an AI agent to understand its full behavior.
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%, with clear descriptions for all parameters (name, prompt, timeout). The description adds no additional meaning beyond the schema, such as examples or constraints, so it meets the baseline of 3 where the schema does the heavy lifting.
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 ('실행하여 응답을 생성합니다' - run to generate a response) and resource ('Ollama 모델' - Ollama model), making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'mcp_ollama_chat_completion' which likely serves a similar purpose, preventing 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 multiple sibling tools for model interactions (e.g., 'mcp_ollama_chat_completion', 'mcp_gemini_generate_text'), there's no indication of specific contexts, prerequisites, or exclusions for this tool's use.
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 offers minimal behavioral insight. It states what the tool does but doesn't disclose whether this is a read-only operation, what permissions are needed, what format the information is returned in, or any rate limits. The description is too basic 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 extremely concise - a single Korean sentence that directly states the tool's purpose. There's zero waste or unnecessary elaboration, making it efficiently front-loaded 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?
For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what information is returned about the model, the response format, error conditions, or how this differs from related tools. The agent would need to guess about the tool's behavior and output.
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% with the single parameter 'name' well-documented in the schema. The description doesn't add any additional parameter semantics beyond what's already in the schema ('정보를 조회할 모델 이름' - model name to retrieve information). Baseline score of 3 is appropriate when the schema does the heavy lifting.
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 ('표시합니다' - displays/shows) and resource ('Ollama 모델의 정보' - information of Ollama model), providing a specific purpose. However, it doesn't differentiate from sibling tools like 'mcp_ollama_list' or 'mcp_ollama_status', which might have overlapping functionality for model information retrieval.
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 for usage, or how it differs from sibling tools like 'mcp_ollama_list' (which might list models) or 'mcp_ollama_status' (which might show system status).
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 tool checks server status, implying a read-only operation, but doesn't disclose any behavioral traits such as what 'status' includes (e.g., uptime, version, health), whether it requires authentication, rate limits, or what the response looks like. This leaves significant gaps for an agent to understand how to use it effectively.
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 phrase 'Ollama 서버 상태 확인', which is front-loaded and wastes no words. Every part of the sentence directly contributes to stating the tool's purpose, making it efficient and well-structured for its minimal content.
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 (a status check with a dummy parameter) and no annotations or output schema, the description is incomplete. It doesn't explain what 'status' entails, how results are returned, or any prerequisites, leaving the agent with insufficient context to use the tool effectively. While concise, it lacks necessary details for full 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 input schema has 1 parameter with 100% description coverage, documenting it as a 'Dummy parameter for no-parameter tools'. The description adds no parameter information beyond this, but since the schema fully covers the parameter and it's a dummy parameter, the baseline is high. With 0 meaningful parameters, the description doesn't need to compensate, making a score of 4 appropriate as it doesn't detract from the schema's clarity.
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 'Ollama 서버 상태 확인' (Ollama server status check) states the tool's purpose clearly with a specific verb ('확인' - check) and resource ('Ollama 서버' - Ollama server). However, it doesn't distinguish this from sibling tools like 'mcp_ollama_list' or 'mcp_ollama_show', which might also provide status-related information. The purpose is understandable but lacks sibling 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 it should be used (e.g., for health checks, before other operations) or when not to use it, nor does it reference any sibling tools like 'mcp_ollama_list' that might serve similar purposes. Usage is implied by the name but not explicitly stated.
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 this is a text generation tool but doesn't mention any behavioral traits: no information about rate limits, authentication requirements, costs, response formats, error handling, or whether this is a read-only or mutating operation. For an API call tool with zero annotation coverage, this is a significant gap in behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core functionality without unnecessary words. It's appropriately sized for a straightforward tool. However, it could be slightly more front-loaded by mentioning it's specifically for chat completions rather than just 'text completions,' which might be ambiguous given other OpenAI tools like embeddings or image generation.
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 this is an API call tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how errors are handled, authentication requirements, rate limits, or costs. For a tool that interacts with an external API and has multiple parameters, more contextual information is needed to help an agent use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter information beyond what's already in the schema, which has 100% coverage. All 4 parameters (model, messages, max_tokens, temperature) are documented in the schema with descriptions. The description doesn't provide additional context about parameter usage, relationships, or examples. With complete schema coverage, the baseline score of 3 is appropriate.
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: 'OpenAI ChatGPT API를 사용하여 텍스트 완성을 생성합니다' (Generate text completions using the OpenAI ChatGPT API). It specifies the verb ('생성합니다' - generate), resource ('텍스트 완성' - text completions), and technology ('OpenAI ChatGPT API'). However, it doesn't differentiate from its sibling tools like mcp_gemini_chat_completion or mcp_ollama_chat_completion, which offer similar chat completion functionality through different providers.
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 mcp_gemini_chat_completion or mcp_ollama_chat_completion, nor does it provide any context about when OpenAI's API would be preferable to other options. The description is purely functional without any comparative or contextual information.
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 offers minimal behavioral insight. It mentions the API usage but doesn't disclose rate limits, authentication needs, cost implications, error handling, or output format. This is inadequate for a tool that likely involves API calls and computational resources.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in Korean that directly states the tool's purpose. It's appropriately sized and front-loaded with no wasted words, though it could be slightly more informative.
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 a tool that interacts with an external API, the description is incomplete. It lacks critical information about behavioral traits, return values, and usage context, making it insufficient for effective agent operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional semantic context about parameters beyond what's in the schema, such as typical model choices or text formatting. Baseline 3 is appropriate when the schema does the heavy lifting.
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 ('create embeddings') and resource ('using OpenAI Embeddings API'), with specific mention of 'text embeddings'. However, it doesn't distinguish this from sibling tools like mcp_openai_chat or mcp_openai_transcribe, which also use OpenAI APIs but for 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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention any context, prerequisites, 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 mentions that the tool 'returns the generated image file path' and that 'this path must be communicated to the user,' which provides some behavioral context about output format and user communication requirements. However, it doesn't disclose important behavioral traits like whether this is a read-only operation, potential costs/rate limits, authentication needs, error handling, or what happens with the generated files. For a tool that creates external resources (image files), this is insufficient behavioral transparency.
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 appropriately concise with two sentences that efficiently convey the core functionality and a key behavioral requirement. The first sentence states the primary purpose, and the second adds important output handling information. There's no wasted language or unnecessary elaboration. However, it could be slightly more structured by explicitly separating purpose from behavioral requirements.
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 (8 parameters, image generation with file creation) and the absence of both annotations and output schema, the description is minimally adequate. It covers the basic purpose and mentions the return value format (file path), but doesn't address important contextual aspects like error conditions, file system implications, or how this tool differs from the many sibling image generation tools. The 100% schema coverage helps, but for a tool that creates external resources, more behavioral context would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with all 8 parameters well-documented in the schema itself. The description adds no parameter-specific information beyond what's already in the schema. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description. The description doesn't compensate with additional parameter semantics, but doesn't need to since the schema is comprehensive.
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: 'OpenAI DALL-E API를 사용하여 이미지를 생성합니다' (uses OpenAI DALL-E API to generate images). It specifies the verb ('생성합니다' - generate) and resource ('이미지' - images). However, it doesn't explicitly differentiate from sibling tools like 'mcp_gemini_create_image' or 'mcp_imagen_generate', which appear to serve similar image generation 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. With multiple image generation tools in the sibling list (mcp_gemini_create_image, mcp_gemini_generate_image, mcp_gemini_generate_images, mcp_imagen_generate), there's no indication of when this OpenAI-specific tool is preferred over Gemini or Imagen alternatives. The description only states what the tool does, not when to choose it.
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 only states the basic function and return value ('변환된 텍스트를 반환합니다' - returns transcribed text). It lacks critical behavioral details: whether this is a read/write operation, rate limits, authentication requirements, error handling, or what happens with large audio files. For a tool with 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two clear sentences that state the core function and return value. It's front-loaded with the main purpose. However, it could be slightly more structured by separating functional description from behavioral aspects.
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 this is a transcription tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the return format (just '텍스트'), error conditions, performance characteristics, or limitations. For an API tool with moderate complexity, more context about behavior and output would be needed.
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 schema already documents all 4 parameters thoroughly. The description adds no parameter information beyond what's in the schema - it doesn't explain parameter interactions, default values, or provide examples beyond what's already in the schema descriptions. Baseline 3 is appropriate when schema does the heavy lifting.
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: 'OpenAI Whisper API를 사용하여 음성을 텍스트로 변환합니다' (transcribes speech to text using OpenAI Whisper API). It specifies the verb (transcribe) and resource (speech/audio), but doesn't differentiate from siblings like mcp_openai_tts (text-to-speech) or mcp_openai_chat. The purpose is clear but lacks explicit sibling 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. It doesn't mention when to choose this over other transcription tools (none in siblings) or when to use other audio-related tools like mcp_openai_tts. There's no context about prerequisites, limitations, or typical use cases.
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. While it mentions executing queries and returning results, it doesn't describe important behavioral aspects: whether this is read-only or can modify data, authentication requirements, rate limits, error handling, or what format the results take. For a query execution tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 - a single sentence that directly states the tool's core function. There's zero wasted language or unnecessary elaboration. It's appropriately sized for what it communicates and is front-loaded with the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a SPARQL query execution tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what kind of results are returned, error conditions, authentication requirements, or how it differs from other SPARQL tools on the server. For a tool that executes potentially complex queries against endpoints, more context is needed for effective use.
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 schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description doesn't add any parameter semantics beyond what's already in the schema - it doesn't explain parameter relationships, provide examples, or clarify usage patterns. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in the description.
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: 'SPARQL 쿼리를 실행하고 결과를 반환합니다' (Execute SPARQL query and return results). This specifies both the verb (execute) and resource (SPARQL query). However, it doesn't differentiate from sibling SPARQL tools like mcp_sparql_update or mcp_sparql_list_graphs, which prevents a score of 5.
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 are multiple sibling SPARQL tools (get_resource_info, list_graphs, list_repositories, update) that likely serve different purposes, but the description doesn't indicate when this execute_query tool is appropriate versus those other options. No usage context or exclusions are mentioned.
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 what the tool does but doesn't mention whether it's read-only, what permissions are needed, what format the output returns, or any rate limits. This leaves significant behavioral gaps for a tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
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 wasted words. It's appropriately sized and front-loaded with the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., list format, error handling) or provide behavioral context needed for proper tool invocation, especially for a tool with sibling alternatives in the same domain.
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 schema already documents both parameters (endpoint and repository) adequately. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline for high schema coverage.
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 ('lists all named graphs') and the target resource ('specified repository'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like mcp_sparql_list_repositories or mcp_sparql_get_resource_info, 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. There are no explicit when/when-not instructions or references to sibling tools like mcp_sparql_list_repositories or mcp_sparql_execute_query, leaving usage context unclear.
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 the tool lists repositories but doesn't disclose behavioral traits such as whether it's read-only, requires authentication, has rate limits, or what the output format looks like. 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 efficiently conveys the tool's purpose without any wasted words. 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., list format, error handling) or behavioral aspects like safety or performance. For a tool with no structured data support, this leaves critical gaps for an agent.
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 100% description coverage, with the 'endpoint' parameter documented as 'SPARQL 엔드포인트 URL'. The description adds no additional meaning beyond this, so the baseline score of 3 is appropriate as the schema does the heavy lifting.
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 ('나열합니다' - list) and resource ('GraphDB 서버의 모든 리포지토리' - all repositories of a GraphDB server). It's specific about what the tool does, though it doesn't explicitly differentiate from sibling tools like mcp_sparql_list_graphs, which lists graphs rather than repositories.
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 mcp_sparql_list_graphs or mcp_sparql_execute_query, nor does it specify prerequisites or contexts for usage, leaving the agent without clear 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 full burden for behavioral disclosure. It only states what the tool does (retrieves model list) but doesn't describe how it behaves: no information about response format, whether it requires authentication, rate limits, freshness of data, or error conditions. For a tool with zero annotation coverage, this is insufficient 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 a single, efficient sentence in Korean that directly states the tool's function. It's appropriately sized for a simple list operation with no parameters. While concise, it could be slightly more informative about the tool's role in the broader context.
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 tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the return value looks like (list format, model attributes), authentication requirements, or how this information should be used with sibling tools. Given the complexity of model selection in AI systems, more context about the output would be valuable.
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 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the schema fully documents the empty input structure. No additional parameter information is needed or provided.
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 ('조회합니다' - retrieves/list) and resource ('사용 가능한 Gemini 모델 목록' - available Gemini model list), making the purpose understandable. It doesn't explicitly differentiate from siblings like mcp_ollama_list, but the Gemini-specific focus provides some distinction. The purpose is specific enough for a list operation.
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 (like authentication), timing (when model availability matters), or relationships to sibling tools (like mcp_gemini_chat_completion which would need a model). There's no explicit 'when' or 'when not' context provided.
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 the tool generates images but lacks critical behavioral details: it doesn't mention whether this is a read-only or destructive operation, any authentication requirements, rate limits, or what the output looks like (e.g., file paths, error handling). The deprecation warning adds some context but doesn't cover core behavioral traits.
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 two sentences: one states the purpose, and the other provides a deprecation warning. It's front-loaded with the core function, and both sentences earn their place by adding value (purpose and context). However, it could be slightly more structured by explicitly separating usage notes.
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 (image generation tool with 6 parameters, no annotations, and no output schema), the description is incomplete. It lacks details on behavioral aspects (e.g., side effects, permissions), output handling (e.g., what's returned), and differentiation from siblings. The deprecation note adds some context but doesn't fill these gaps, making it inadequate for a tool of this nature.
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 schema fully documents all 6 parameters (e.g., prompt, model, size). The description adds no parameter-specific information beyond what's in the schema, such as explaining prompt best practices or model differences. With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.
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: 'Google Imagen 모델을 사용하여 이미지를 생성합니다' (uses Google Imagen model to generate images). It specifies the verb (generate) and resource (images) with the technology (Google Imagen). However, it doesn't distinguish itself from sibling tools like 'mcp_gemini_create_image' or 'mcp_gemini_generate_image', which likely have similar functions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by mentioning it will soon be replaced by 'mcp_gemini_generate_image', suggesting this is a legacy or alternative version. However, it doesn't provide explicit guidance on when to use this tool versus other image-generation siblings (e.g., 'mcp_gemini_create_image', 'mcp_imagen_generate'), nor does it mention any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions what the tool can do (send requests with various methods/headers/data) but doesn't describe error handling, rate limits, timeout behavior beyond the parameter, authentication needs, or what the response format looks like. This is inadequate for a general-purpose HTTP tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently structured in two sentences that cover the core functionality. However, it could be more front-loaded by explicitly stating it's a general-purpose HTTP client tool. No wasted words, but slightly lacks optimal structure.
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 complex, general-purpose HTTP tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain response formats, error conditions, authentication requirements, or practical usage scenarios. The agent would struggle to use this effectively without trial and error.
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 schema already documents all 6 parameters thoroughly. The description adds minimal value by mentioning HTTP methods, headers, and data in general terms but provides no additional syntax, format details, or constraints beyond what's in the schema descriptions. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('send HTTP request' and 'return response') and resources (HTTP methods, headers, data). It distinguishes itself from sibling tools by focusing on general HTTP operations rather than specific API integrations like Gemini or OpenAI.
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 like network access, authentication requirements, or when to choose this over more specialized sibling tools (e.g., mcp_openai_chat for OpenAI API calls).
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. While '조회합니다' (retrieves/query) implies a read-only operation, it doesn't explicitly state whether this requires authentication, has rate limits, returns paginated results, or what format the output takes. For a tool with 3 parameters and 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 a single, efficient sentence in Korean that directly states the tool's core function. It's appropriately sized for a retrieval tool and front-loads the essential information without any wasted words or unnecessary elaboration.
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 3 parameters with 100% schema coverage but no annotations and no output schema, the description is minimally adequate. It explains what the tool does at a high level but doesn't provide behavioral context (authentication, rate limits, output format) or usage guidance relative to sibling tools. For a SPARQL query tool with no output schema, more context about return values 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%, with all 3 parameters well-documented in the schema itself. The description mentions '지정된 URI' (specified URI) which aligns with the 'uri' parameter, but adds no additional semantic context beyond what the schema already provides. With complete schema coverage, the baseline score of 3 is appropriate.
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: '조회합니다' (retrieves/query) '모든 속성과 값을' (all properties and values) for a '지정된 URI' (specified URI). It uses specific verb+resource language that tells what the tool does. However, it doesn't distinguish from its SPARQL siblings like mcp_sparql_execute_query or mcp_sparql_list_graphs, 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 sibling tools like mcp_sparql_execute_query (which could also retrieve resource info with a custom query) or mcp_sparql_list_graphs (which might list resources). There's no context about when this specific retrieval method is preferred over other approaches.
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 retrieval operation ('조회합니다'), implying it's likely read-only, but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, returns paginated results, or what format the output takes. For a tool with zero annotation coverage, 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 a single, efficient sentence in Korean 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 (0 parameters, no output schema) and lack of annotations, the description is minimally adequate but has clear gaps. It explains what the tool does but misses behavioral context and usage guidelines, which are important for an agent to operate 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.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description doesn't need to add parameter information, and it appropriately doesn't mention any. Baseline is 4 for tools with no parameters.
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 ('조회합니다' - retrieve/list) and resource ('Ollama 모델 목록' - Ollama model list), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'mcp_ollama_show' or 'mcp_ollama_status', which might also provide model-related 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?
No guidance is provided on when to use this tool versus alternatives. With siblings like 'mcp_ollama_show' (which might show details of a specific model) and 'mcp_ollama_status' (which might show system status), the description lacks 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.
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 reveals that the tool returns a file path ('생성된 이미지 파일 경로를 반환하며') and that this path must be communicated to the user ('이 경로는 반드시 사용자에게 알려주어야 합니다'). However, it doesn't disclose important behavioral aspects like whether this is a read-only or mutating operation, potential rate limits, authentication requirements, error conditions, or what happens when files are saved. The description adds some value but leaves significant 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 reasonably concise with two sentences. The first sentence states the core functionality, and the second provides important behavioral information about the return value. There's no obvious fluff or redundancy. However, it could be slightly more front-loaded by immediately clarifying the tool's scope relative to siblings.
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 complexity (11 parameters, no annotations, no output schema), the description is moderately complete. It covers the basic purpose and return behavior but lacks important context. Without annotations or output schema, the description should ideally explain more about what kind of operation this is (read vs write), error handling, and the format/meaning of the returned file path. The current description provides a foundation but leaves the agent with significant uncertainty about the tool's full behavior.
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 schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. It mentions model selection automation but doesn't explain parameter implications or interactions. With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't enhance parameter understanding beyond the structured data.
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: 'Google Gemini 또는 Imagen 모델을 사용하여 이미지를 생성합니다' (uses Google Gemini or Imagen models to generate images). It specifies the verb ('생성합니다' - generates) and resource ('이미지' - images). However, it doesn't explicitly differentiate from sibling tools like 'mcp_gemini_create_image' or 'mcp_imagen_generate', which appear to have similar functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some implied usage context: '모델 이름에 따라 적절한 API가 자동으로 선택됩니다' (the appropriate API is automatically selected based on the model name). This suggests the tool handles model selection automatically. However, it doesn't explicitly state when to use this tool versus alternatives like 'mcp_gemini_create_image' or 'mcp_imagen_generate', nor does it provide any exclusion criteria or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool '생성된 오디오 파일 경로를 반환하며, 이 경로는 반드시 사용자에게 알려주어야 합니다' (returns the generated audio file path, which must be communicated to the user), which adds some context about the return value and a user-facing requirement. However, it lacks details on permissions, rate limits, error handling, or whether the operation is read-only or destructive, leaving significant gaps for a tool that creates files.
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 in two sentences: the first states the tool's purpose, and the second explains the return value and a user requirement. There is no wasted language, and key information is front-loaded, 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.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, file creation) and lack of annotations and output schema, the description is minimally adequate. It covers the basic purpose and return behavior but misses details like error cases, file format, or operational constraints. Without annotations or output schema, more context would be helpful for safe and effective use.
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 schema already documents all parameters thoroughly. The description does not add any parameter-specific information beyond what the schema provides (e.g., it doesn't explain the significance of 'voice' choices or 'speed' ranges). This meets the baseline of 3, as the schema handles the heavy lifting without additional value from the description.
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: 'OpenAI TTS API를 사용하여 텍스트를 음성으로 변환합니다' (converts text to speech using OpenAI TTS API). It specifies the exact action (convert text to speech) and resource (OpenAI TTS API), and distinguishes itself from sibling tools like chat completion or image generation tools by focusing specifically on text-to-speech conversion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through '텍스트를 음성으로 변환합니다' (converts text to speech), suggesting it should be used when audio output is needed from text. However, it provides no explicit guidance on when to use this tool versus alternatives (e.g., other TTS tools or audio generation methods), nor does it mention 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.
Behavior4/5
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 does well by describing key behavioral traits: SynthID watermarking on all generated images, capability for interactive image modification, ability to generate mixed text-image outputs, and the important caveat that image generation isn't always triggered automatically. This provides substantial behavioral context beyond basic functionality.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately concise but could be better structured. It starts with the core purpose, then lists features in a numbered format, and ends with usage recommendations. While informative, the numbered feature list feels somewhat disconnected from the flow, and some sentences could be more tightly integrated. Every sentence adds value, but the organization could be improved.
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 complexity of an image generation tool with no annotations and no output schema, the description does a reasonably complete job. It covers the core functionality, key behavioral characteristics, and important usage considerations. However, it doesn't describe the output format (what exactly is returned beyond '텍스트와 이미지를 함께 반환합니다'), which would be helpful given the lack of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the baseline is 3. The description doesn't add significant parameter semantics beyond what's already in the schema. It mentions the importance of explicit image requests in prompts, which relates to the 'prompt' parameter, but this is already covered in the schema's description. No additional parameter context is provided.
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: 'Gemini 모델을 사용하여 텍스트 프롬프트에서 이미지를 생성합니다' (Generates images from text prompts using Gemini model). It specifies the verb (생성/creates) and resource (이미지/images). However, it doesn't explicitly differentiate from sibling tools like 'mcp_gemini_generate_image' or 'mcp_gemini_edit_image', which appear to have similar functions.
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 for when to use this tool: for generating images from text prompts using Gemini. It offers specific guidance on language preferences ('EN, es-MX, ja-JP, zh-CN, hi-IN') and explicit prompting techniques ('이미지 생성', '이미지 제공'). However, it doesn't explicitly mention when NOT to use it or name alternative tools for different scenarios.
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 provided, the description carries the full burden of behavioral disclosure. It does an excellent job describing important behavioral traits: limitations (no audio/video input, image generation not always triggered), potential failures (model may stop generation), and effective usage patterns (multi-turn editing, context preservation, text generation strategy). This goes well beyond basic functionality 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?
The description is appropriately sized and well-structured. It starts with the core purpose, then lists requirements, limitations, and effective strategies. Each sentence adds value, though some sentences could be more concise (e.g., the text generation strategy explanation is somewhat verbose). Overall, it's efficient without being terse.
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 complexity of an image editing tool with no annotations and no output schema, the description provides substantial context. It covers purpose, requirements, limitations, behavioral patterns, and usage strategies. The main gap is the lack of information about return values or what happens after editing (though the saveDir parameter suggests files are saved). For a tool with this complexity, it's quite complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description mentions that text prompts and Base64-encoded image data are required, which aligns with the required parameters in the schema, but doesn't add significant semantic value beyond what the schema already provides. The baseline of 3 is appropriate when the schema does the heavy lifting.
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: 'Gemini 모델을 사용하여 기존 이미지를 편집합니다' (Uses Gemini model to edit existing images). It specifies the verb (edit) and resource (images) with the Gemini model context. However, it doesn't explicitly differentiate from sibling tools like mcp_gemini_create_image or mcp_gemini_generate_image, which might also involve image manipulation.
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 for when to use this tool: it requires text prompts and Base64-encoded image data for editing existing images. It mentions multi-turn image editing capabilities and suggests effective strategies (e.g., generating text first for text-in-image edits). However, it doesn't explicitly state when NOT to use this tool or name specific alternatives among the many sibling tools.
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. It clearly indicates this is a data modification tool ('데이터를 수정합니다', '그래프 수정 작업'), which implies mutation and potential destructive changes. However, it doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or what happens on success/failure. The description adds basic context about supported SPARQL update syntax but lacks operational details.
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 perfectly concise and well-structured. Two sentences efficiently convey the tool's purpose, supported syntax, and capabilities. Every sentence earns its place: the first states the core function, the second elaborates on supported operations. No wasted words or redundancy.
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 this is a mutation tool with no annotations and no output schema, the description is somewhat incomplete. It adequately explains what the tool does at a high level but lacks important context about behavioral aspects (permissions, side effects, error conditions) and what the tool returns. The description compensates somewhat by specifying the types of SPARQL update operations supported, but more operational context would be helpful for a data modification 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 description coverage is 100%, so the schema already documents all three parameters (endpoint, query, repository) with their descriptions. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions SPARQL update query examples generally but doesn't provide additional syntax or format details for the 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's purpose: executing SPARQL update queries to modify data. It specifies the exact action ('실행하여 데이터를 수정합니다' - execute to modify data) and resource (SPARQL data), distinguishing it from sibling tools like mcp_sparql_execute_query (which likely handles read-only queries) by focusing on updates rather than 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?
The description provides clear context for when to use this tool: for SPARQL 1.1 Update operations like INSERT DATA, DELETE DATA, etc. It implicitly distinguishes from mcp_sparql_execute_query by specifying update operations, but doesn't explicitly state when NOT to use it or name alternatives, though the sibling list suggests mcp_sparql_execute_query as a likely read-only alternative.
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 provided, the description carries the full burden of behavioral disclosure. It does well by mentioning: 1) the SynthID watermark that will always be included, 2) the current limitation to English prompts only, and 3) the model's specific strengths. However, it doesn't mention potential rate limits, authentication requirements, or error conditions that might be relevant for an image generation 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 perfectly structured and concise - four sentences that each add distinct value: 1) core functionality, 2) model capabilities, 3) watermark information, 4) language limitation. No wasted words, front-loaded with the most important information first.
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 no annotations and no output schema, the description does quite well. It covers the model's capabilities, important behavioral constraints (watermark, language), and gives context about when to use it. The main gap is the lack of information about what the tool returns (image format, file location, error responses) since there's no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions English prompts, which aligns with the prompt parameter's description, but this is redundant. Baseline 3 is appropriate when the schema does all the parameter documentation work.
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: 'Google Imagen 3 모델을 사용하여 텍스트 프롬프트에서 고품질 이미지를 생성합니다' (generates high-quality images from text prompts using Google Imagen 3 model). It specifies the exact model and distinguishes itself from sibling tools by mentioning Imagen 3's specific strengths (photorealism, artistic detail, specific art styles).
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 about when to use this tool: for generating images with photorealism, artistic detail, or specific art styles using Imagen 3. It also mentions that '현재 영어 프롬프트만 지원됩니다' (only English prompts are currently supported), which is important usage guidance. However, it doesn't explicitly compare with alternatives like the various Gemini image generation siblings.
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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