MCP-Claude

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
The tools have distinct purposes (math, image generation, weather alerts, weather forecast), but 'add' is overly generic and could be confused with other mathematical operations if more were added. The image-related tools ('display_generated_image' and 'generate-image') have clear but overlapping domains, which might cause minor confusion about which to use for image handling.
Naming Consistency2/5
Naming is inconsistent with mixed conventions: 'add' uses a simple verb, 'display_generated_image' uses snake_case with a verb-object pattern, 'generate-image' uses kebab-case, and 'get-alerts'/'get-forecast' use kebab-case with a verb-noun pattern. This lack of a unified style makes the set harder to predict and use.
Tool Count3/5
With 5 tools, the count is reasonable for a general-purpose server, but it feels thin and scattered across unrelated domains (math, images, weather). There's no clear thematic focus, making the set appear under-scoped for any single purpose.
Completeness2/5
The tool set is severely incomplete for any coherent domain. For math, only 'add' is provided with no other operations. For images, generation and display are covered but lack editing or management tools. For weather, alerts and forecasts are included but miss basics like current conditions or historical data, leading to significant gaps in functionality.
Average 2.9/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
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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 states the tool displays an image in a new tab, which implies a read-only, non-destructive action, but doesn't cover potential side effects (e.g., browser behavior, errors for invalid URLs), authentication needs, or rate limits. It's minimal and lacks detail for safe invocation.
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 function without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse. Every part of the sentence contributes to understanding the purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, no annotations), the description is incomplete. It lacks details on error handling, what constitutes a valid 'generated image', or the expected user experience (e.g., tab behavior). For a tool that interacts with external resources (URLs), more context is needed to ensure 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?
The input schema has 100% description coverage, with the 'imageUrl' parameter clearly documented. The description adds no additional meaning beyond the schema, such as URL format requirements or validation rules. Since schema coverage is high, the baseline score of 3 is appropriate, 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 action ('display') and the resource ('generated image'), specifying it opens in a new tab. It distinguishes from siblings like 'generate-image' (which creates images) and 'add' (unrelated), but doesn't explicitly contrast with other display or viewing tools if they existed. The purpose is specific but could be more precise about the display mechanism.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a generated image URL from 'generate-image'), exclusions, or comparisons to other tools for viewing images. The context is implied (use after generation), but no explicit usage instructions are given.
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 only states the tool generates an image using Replicate, without disclosing behavioral traits like cost implications, rate limits, quality settings, or what happens on failure. This leaves significant gaps for a tool that likely involves external API calls and resource usage.
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. It is front-loaded, directly stating the tool's purpose without unnecessary details. Every word earns its place, making it highly concise and well-structured for quick understanding.
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 image generation (external API, potential costs, quality variations) and lack of annotations or output schema, the description is incomplete. It doesn't cover return values, error handling, or operational context, leaving the agent with insufficient information to use the tool effectively beyond basic invocation.
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 'prompt' parameter well-documented in the schema. The description adds no additional meaning beyond the schema, such as examples or constraints on prompt formatting. With high schema coverage, the baseline is 3, as the description doesn't compensate but doesn't detract either.
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 ('generate') and resource ('image'), specifying it uses Replicate as the service. It distinguishes from sibling tools like 'add' or 'get-forecast' by focusing on image generation, though it doesn't explicitly differentiate from 'display_generated_image' which might be related. The purpose is specific but lacks sibling differentiation details.
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 does not mention any context, prerequisites, or exclusions, such as when to choose this over other image generation methods or how it relates to 'display_generated_image'. It offers no usage instructions beyond the basic action.
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 cover critical aspects like whether it's a read-only operation, potential rate limits, authentication needs, error handling, or what the output format might be. 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 a single, clear sentence that efficiently conveys the core purpose without any unnecessary words. It's front-loaded and wastes no space, 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?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'weather alerts' entail (e.g., types, severity, format), how results are returned, or any limitations. Given the complexity of weather data and lack of structured context, more detail is needed for the agent to use this tool 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 schema description coverage is 100%, with the parameter 'state' fully documented in the schema. The description adds no additional semantic context beyond what the schema provides (e.g., it doesn't clarify if 'state' refers to U.S. states only or includes other regions). Given the high 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 action ('Get') and resource ('weather alerts for a state'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get-forecast' (which might provide different weather data), so it doesn't reach the highest 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 like 'get-forecast' or other siblings. It lacks any mention of prerequisites, exclusions, or specific contexts for usage, leaving the agent with minimal 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool gets a forecast but doesn't cover critical aspects like whether it requires authentication, rate limits, data freshness, or what the forecast includes (e.g., temperature, precipitation). This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste—'Get weather forecast for a location'—making it front-loaded and appropriately sized for its purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the forecast returns (e.g., time periods, metrics) or behavioral traits like error handling. For a tool with no structured output, this leaves the agent under-informed.
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, clearly documenting latitude and longitude with ranges. The description adds no parameter semantics beyond what the schema provides, such as coordinate formats or location examples. 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 tool's purpose with a specific verb ('Get') and resource ('weather forecast for a location'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'get-alerts', which might also relate to weather information, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get-alerts' for weather alerts or explain if this is for current vs. future forecasts, leaving the agent without usage context.
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. 'Add two numbers' implies a simple computation but doesn't disclose any behavioral traits like error handling, precision limits, or what happens with non-numeric inputs. For a 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.
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 purpose with zero waste. Every word earns its place, making it immediately understandable 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 simplicity (basic arithmetic with 2 parameters, 100% schema coverage, no output schema), the description is complete enough for its intended function. However, the lack of annotations and output schema means the agent must assume behavior like return format and error handling, leaving some gaps in completeness for a tool that could have edge cases.
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 both parameters 'a' and 'b' documented as 'First number' and 'Second number' respectively. The description 'Add two numbers' adds minimal semantic value beyond what the schema already provides, confirming the operation but not adding format details or constraints. 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 'Add two numbers' clearly states the verb ('Add') and resource ('two numbers'), making the purpose immediately understandable. It's specific about the operation being performed, though it doesn't distinguish from sibling tools since this appears to be the only mathematical operation among the listed 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. There's no mention of context, prerequisites, or comparisons with other tools. The agent must infer usage purely from the name and description without any explicit guidelines.
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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