Demo MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: mathematical operations (add, subtract, multiply, divide) are separate from text operations (analyze-text, transform-text). The descriptions reinforce this separation, making tool selection unambiguous.
Naming Consistency4/5
The naming is mostly consistent with clear verb-based patterns: mathematical tools use simple verbs (add, subtract, multiply, divide), while text tools use hyphenated verb-noun forms (analyze-text, transform-text). The minor deviation is the mix of simple verbs and hyphenated forms, but all names are readable and follow logical conventions.
Tool Count5/5
With 6 tools, the count is well-scoped for a demo server covering basic mathematical and text operations. Each tool earns its place by addressing a core function without redundancy, making the set manageable and purposeful.
Completeness4/5
The tool set covers fundamental operations in two domains: mathematics (addition, subtraction, multiplication, division) and text (analysis, transformation). A minor gap is the lack of more advanced text or math functions, but for a demo server, the coverage is sufficient for basic workflows without dead ends.
Average 2.8/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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 are provided, so the description carries full burden. It mentions 'provide statistics' but doesn't disclose behavioral traits such as what statistics are returned, format of output, performance characteristics, rate limits, or error handling. This leaves significant gaps in understanding how the tool behaves 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a single sentence 'Analyze text and provide statistics', which is front-loaded and wastes no words. However, it could be more structured by specifying key details, but it earns high marks for brevity and clarity within its limited scope.
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 simple input schema, the description is incomplete. It doesn't explain what statistics are provided, how results are formatted, or any behavioral context. For a tool with undefined output and no annotations, more detail is needed to make it fully usable by an AI 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?
Schema description coverage is 100% with one parameter 'text' fully documented in the schema. The description adds no meaning beyond the schema, as it doesn't elaborate on parameter usage, constraints, or examples. Baseline is 3 since the schema does the heavy lifting, but no extra value is added.
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 'Analyze text and provide statistics' states a general purpose but lacks specificity. It mentions the verb 'analyze' and resource 'text' but doesn't specify what kind of analysis or statistics (e.g., word count, sentiment, readability). It distinguishes from sibling tools (math operations and text transformation) but not clearly from other potential text analysis 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?
No guidance on when to use this tool versus alternatives is provided. The description doesn't mention context, prerequisites, or exclusions. It implies usage for text analysis but doesn't differentiate from other text-related tools like 'transform-text' or specify scenarios where this is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Divide two numbers' reveals nothing about error handling (division by zero), return format (integer vs. float), precision, or side effects. The description doesn't mention whether this is a pure mathematical operation or has any system dependencies. For a tool with zero annotation coverage, this minimal description leaves critical behavioral aspects unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is maximally concise at three words with zero wasted language. It's appropriately sized for a simple mathematical operation and gets straight to the point without unnecessary elaboration. Every word earns its place in communicating 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 mathematical operation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address critical context like error conditions (division by zero), return value format, precision limitations, or mathematical domain constraints. While the operation is conceptually simple, the description leaves too many practical implementation questions unanswered for reliable agent 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 both parameters clearly documented as 'Dividend' and 'Divisor' in the schema. The description adds no additional parameter information beyond what's already in the structured schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
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 'Divide two numbers' clearly states the verb (divide) and resource (numbers), making the basic purpose understandable. However, it doesn't differentiate this tool from its sibling 'multiply' beyond the obvious mathematical operation difference, nor does it specify what kind of division is performed (integer vs. floating point, error handling for division by zero). The title 'Division Tool' adds no additional clarity beyond the description.
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 like 'multiply' or 'add'. While the mathematical context is obvious, there's no explicit mention of use cases, prerequisites, or limitations. The agent must infer usage purely from the tool name and mathematical knowledge, with no tool-specific guidance 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. 'Subtract two numbers' implies a mathematical operation but doesn't disclose error handling (e.g., for non-numeric inputs), performance characteristics, or any side effects. It's minimal and lacks context beyond the basic operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description 'Subtract two numbers' is extremely concise—three words that directly state the tool's function with zero waste. It's front-loaded and efficiently communicates the core purpose without 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 the tool's low complexity (simple arithmetic), no annotations, no output schema, and high schema coverage, the description is minimally adequate. It states what the tool does but lacks details on usage, behavior, or output, making it incomplete for richer agent understanding.
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 parameters 'a' and 'b' well-documented as 'First number (minuend)' and 'Second number (subtrahend)'. The description doesn't add meaning beyond the schema, but the schema provides sufficient detail, so 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Subtract two numbers' clearly states the verb (subtract) and resource (numbers), but it's vague about scope and doesn't distinguish from sibling tools like 'add' or 'divide'. It specifies the operation but lacks detail about what makes subtraction unique compared to other arithmetic operations available.
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 'add' or 'divide'. The description doesn't mention any context, prerequisites, or exclusions for using subtraction, leaving the agent to infer usage based solely on the tool name.
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. 'Multiply two numbers' implies a mathematical operation but reveals nothing about error handling, numeric limits, precision, or return format. It doesn't address whether it handles integers, floats, or specific edge cases, leaving 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 perfectly concise at three words, front-loading the core action without any wasted text. Every word earns its place, making it immediately understandable while maintaining brevity.
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 mathematical operation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (product, error messages), numeric constraints, or behavioral characteristics. The simplicity of the operation doesn't compensate for these omissions in 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?
The input schema has 100% description coverage with clear parameter documentation, so the baseline is 3. The description 'Multiply two numbers' adds no additional parameter semantics beyond what the schema already provides (two numbers labeled 'a' and 'b'), maintaining the adequate baseline without enhancement.
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 'Multiply two numbers' clearly states the verb ('Multiply') and resource ('two numbers'), making the purpose immediately understandable. However, it doesn't explicitly distinguish this tool from its sibling 'divide' beyond the obvious mathematical operation difference, which is why it doesn't reach the highest 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 like 'add', 'subtract', or 'divide'. There's no mention of context, prerequisites, or comparative use cases, leaving the agent to infer usage purely from the tool name.
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 lacks details on traits like whether transformations are reversible, performance characteristics, error handling, or output format. For a tool with 2 parameters and no output schema, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at three words, front-loaded with the core purpose ('Transform text case and format'). Every word earns its place without redundancy or unnecessary elaboration, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral traits, output format, and usage guidelines, which are essential for an agent to invoke it correctly without structured support from annotations or 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?
The schema description coverage is 100%, with clear descriptions for both parameters ('text' and 'operation'), including an enum for 'operation'. The description adds no additional parameter semantics beyond what the schema provides, such as examples or edge cases, so it meets 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 'Transform text case and format' clearly states the verb ('transform') and resource ('text'), specifying the domain of operations (case and format). It distinguishes from sibling tools like 'analyze-text' by focusing on transformation rather than analysis, though it doesn't explicitly differentiate from all siblings.
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 'analyze-text' for different text operations or specify contexts where transformation is preferred over other text-handling tools, leaving usage entirely implicit.
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. 'Add two numbers together' implies a simple, deterministic operation but doesn't cover potential errors (e.g., overflow, invalid inputs), performance, or output format. For a tool with no annotations, this lacks detail on behavior beyond the basic action, warranting a 2.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste: 'Add two numbers together'. It is front-loaded and appropriately sized for this simple tool, earning a 5 for conciseness.
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 (basic arithmetic), 100% schema coverage, and no output schema, the description is minimally complete. It states the core action but lacks details on usage, behavior, or output. For such a simple tool, this is adequate but with clear gaps, scoring a 3 as the minimum viable.
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 parameters 'a' and 'b' as 'First number' and 'Second number'. The description 'Add two numbers together' aligns with this but adds no additional semantic context beyond what the schema provides. With high schema coverage, the baseline is 3, as 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 'Add two numbers together' clearly states the verb ('add') and resource ('two numbers'), making the purpose immediately understandable. It distinguishes from siblings like 'subtract' or 'multiply' by specifying addition, though it doesn't explicitly contrast them. The title 'Addition Tool' reinforces this, but the description itself is specific enough for a 4.
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 like 'subtract' or 'multiply', nor does it mention any context or prerequisites. It simply states what the tool does, leaving the agent to infer usage based on the operation name alone. This is minimal guidance, scoring a 2.
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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