MCP Tools
Server Quality Checklist
Latest release: v0.6.1
- Disambiguation2/5
All three tools have nearly identical descriptions, only differing by the AI model name. An agent would struggle to choose between them without additional context on model preferences.
Naming Consistency5/5All tools follow a consistent 'ask_{model}' pattern, making naming predictable and clear.
Tool Count4/5Three tools is reasonable for a server that simply provides access to multiple AI models. It is slightly minimal but not out of place.
Completeness3/5The tool set covers the core function of querying different AI models, but lacks features like conversation history, context management, or parameter customization, which are notable gaps.
Average 3.2/5 across 3 of 3 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 3 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and description fails to disclose behavioral traits like response format, error handling, or required permissions. Only states basic function without additional context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Description is one clear sentence that front-loads the main action. Could be slightly expanded but is efficiently concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity and no output schema, the description is minimal but adequate for a simple query tool. However, it lacks details on response behavior, limiting full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers both parameters with descriptions, and the tool description adds value by clarifying the optional nature of question (general opinion when omitted). Baseline 3 with added context yields 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Ask DeepSeek a question' and differentiates from general second opinion. It implicitly distinguishes from siblings (ask_chatgpt, ask_gemini) by naming DeepSeek but does not explicitly contrast use cases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs siblings. The phrase 'get a general second opinion' hints at use case but no when-not-to-use or alternatives 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, the description carries the full burden of disclosing behavioral traits. It only mentions the action and modes, omitting details such as whether the tool is stateless, rate limits, permission requirements, or what happens if context is malformed. Minimal behavioral information is provided.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It efficiently conveys the core purpose and primary use cases, earning a perfect score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no output schema, the description minimally covers the input and purpose but omits what the agent can expect as a return value or any error handling. Given the lack of annotations and output schema, the description could be more complete, though it is adequate for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema already explains both parameters (context and question) adequately. The description's phrase 'general second opinion' partly echoes the schema's default behavior for missing question, adding little extra meaning. A baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (ask Gemini) and resource (Google Gemini), with two distinct use cases: asking a question about a context or getting a general second opinion. However, it does not explicitly differentiate from sibling tools like ask_chatgpt or ask_deepseek, relying on the name for distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies two usage modes but provides no guidance on when to use this tool over its siblings (ask_chatgpt, ask_deepseek) or when to choose one mode over the other. There are no explicit when-to-use or when-not-to-use instructions, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as response format, latency, or limitations. The description is minimal, leaving the agent uninformed about important behavioral aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that is front-loaded and efficient, though very short.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema and annotations, the description should cover return values or usage constraints. It does not, leaving the agent with incomplete context for invoking the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds value beyond the schema by explaining that the 'question' parameter is optional and that omitting it results in a general second opinion. This enhances understanding of parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Ask ChatGPT a question about a context, or get a general second opinion'. It explicitly names the AI model and differentiates from sibling tools by name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for asking questions or obtaining second opinions, but lacks explicit guidance on when to prefer this tool over siblings or any exclusions.
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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