claude-code-mcp

by KunihiroS

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
There is significant overlap between edit_code, fix_code, and review_code, as all involve modifying or analyzing code for improvements, which could confuse an agent about which to use for specific tasks. However, explain_code, simulate_command, test_code, and your_own_query have more distinct purposes, providing some clarity.
Naming Consistency4/5
Most tools follow a consistent verb_noun pattern (e.g., edit_code, explain_code, fix_code), which is predictable and readable. The only deviation is your_own_query, which uses a different style but doesn't severely disrupt the overall consistency.
Tool Count5/5
With 7 tools, the count is well-scoped for a code-focused server, falling within the typical 3-15 range. Each tool appears to serve a specific purpose in code development and analysis, making the set appropriately sized for the domain.
Completeness4/5
The tool set covers key aspects of code interaction such as editing, explaining, fixing, reviewing, simulating, testing, and custom queries, which aligns well with a code assistant domain. Minor gaps might include operations like code generation or version control integration, but core workflows are adequately supported.
Average 2.6/5 across 7 of 7 tools scored. Lowest: 1.7/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on March 16, 2025
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but offers none. 'Reviews the given code' doesn't reveal whether this is a read-only analysis, whether it modifies code, what permissions might be needed, what format the review takes, or any limitations. For a tool that presumably analyzes code, this lack of behavioral context is completely inadequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While technically concise with just 4 words, this is under-specification rather than effective conciseness. The single sentence 'Reviews the given code' fails to provide necessary information that would help an agent use the tool correctly. Every word should earn its place, but here the minimal text creates ambiguity rather than clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of code review (which could involve security, performance, style, correctness, etc.) and the presence of multiple sibling tools with overlapping functions, this description is completely inadequate. With no annotations, no output schema, and multiple similar tools available, the description fails to provide the contextual information needed to select and use this tool appropriately.
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 ('code' and 'focus_areas') adequately. The description adds no parameter information beyond what the schema provides - it doesn't explain what constitutes valid 'focus_areas' or how they affect the review. Baseline 3 is appropriate when the schema does the heavy lifting, though the description adds zero value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Reviews the given code' is a tautology that restates the tool name 'review_code' without adding meaningful specificity. It doesn't distinguish this tool from siblings like 'explain_code' or 'fix_code' - both could involve reviewing code. The description lacks a clear verb+resource combination that defines what 'reviewing' entails versus other code analysis operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides absolutely no guidance about when to use this tool versus alternatives. With siblings like 'explain_code', 'fix_code', and 'test_code' available, there's no indication whether this tool is for code quality assessment, security review, performance analysis, or general explanation. The agent receives no help in selecting between these closely related code analysis tools.
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 edits code but doesn't explain how—whether it's a direct mutation, requires permissions, has side effects, or what the output looks like. For a tool that modifies code without annotations, this leaves critical behavioral traits unspecified.
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 directly states the tool's function without unnecessary words. It's appropriately sized for a simple tool, though it could be more front-loaded with key details. There's no wasted language, making it concise.
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 editing code, lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like mutation safety, error handling, or result format, which are essential for an agent to use this tool correctly. The description fails to compensate for the missing structured data.
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 documentation for both parameters ('code' and 'instructions'). The description adds no additional meaning beyond what the schema provides, such as format examples or constraints. With high schema coverage, 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose as editing code based on instructions, which is clear but vague. It specifies the verb ('edits') and resource ('code'), but doesn't distinguish it from sibling tools like 'fix_code' or 'review_code' that might also modify code. The purpose is understandable but lacks specificity about what type of editing it performs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'fix_code', 'review_code', and 'explain_code', there's no indication whether this is for general code modifications, bug fixes, or something else. No prerequisites, exclusions, or comparative context 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?
No annotations are provided, so the description carries the full burden. It mentions 'detailed explanation' but doesn't disclose behavioral traits like response format, depth of analysis, potential rate limits, or error conditions. This is inadequate 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It's appropriately sized for a simple tool, though it could be more front-loaded with key details. Every sentence earns its place, but it's slightly under-specified.
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 doesn't explain what the explanation includes (e.g., syntax, logic, dependencies) or the return format. For a code explanation tool with rich potential outputs, this lacks necessary 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 both parameters ('code' and 'context'). The description adds no meaning beyond what the schema provides, such as examples or usage tips. 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.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'provides detailed explanation of the given code,' which clearly indicates its function. However, it doesn't differentiate from siblings like 'review_code' or 'fix_code' that might also involve code analysis. The purpose is clear but lacks sibling distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as 'review_code' or 'fix_code.' The description implies usage for code explanation but offers no context on prerequisites, exclusions, or comparisons to sibling tools.
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 only states the action ('sends a custom query with context') without explaining what happens after sending (e.g., response format, error handling, side effects, or rate limits). For a tool with no annotations, this is insufficient to inform the agent about its behavior.
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 ('Sends a custom query with context.') that is front-loaded and wastes no words. However, it could be more structured by including 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 the complexity (a query tool with no annotations and no output schema), the description is incomplete. It doesn't explain what the tool returns, how errors are handled, or the context of use (e.g., related to code or commands). With siblings like code-related tools, more context is needed to guide the agent 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?
Schema description coverage is 100%, with clear descriptions for both parameters ('query' as query text, 'context' as additional context). The description adds no additional meaning beyond the schema, such as examples or constraints. Since the schema does the heavy lifting, 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 states the tool 'sends a custom query with context', which provides a basic verb+resource combination. However, it's vague about what type of query this is (e.g., database query, API query, natural language query) and doesn't distinguish it from sibling tools like 'simulate_command' or 'explain_code' that might also involve queries. The purpose is understandable but lacks specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'explain_code' and 'simulate_command', it's unclear if this tool is for general-purpose queries or specific contexts. There are no explicit when/when-not instructions or named alternatives mentioned, leaving usage ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool simulates execution but doesn't clarify what 'simulates' entails—e.g., whether it's a dry run, sandboxed execution, or mock output—nor does it address permissions, side effects, or rate limits.
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, making it appropriately sized and front-loaded. Every word contributes directly to stating the tool's 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 for a tool that simulates commands. It doesn't explain what the simulation returns, potential errors, or how it differs from actual execution, leaving significant gaps for 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?
The input schema has 100% description coverage, clearly documenting both parameters. The description adds no additional meaning beyond the schema, such as examples or constraints, so it meets the baseline for high schema coverage without compensating further.
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 the tool 'simulates the execution of a given command,' which provides a basic verb+action but lacks specificity about what type of command or simulation is involved. It doesn't differentiate from sibling tools like 'test_code' or 'your_own_query,' making it vague in context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention any context, prerequisites, or exclusions, leaving the agent to infer usage from 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 the full burden of behavioral disclosure. It states the tool 'fixes bugs or issues' but doesn't explain how it behaves—e.g., whether it modifies code in place, returns suggestions, requires specific permissions, or has rate limits. This leaves critical operational details unclear for a mutation tool.
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: 'Fixes bugs or issues in the given code.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity.
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 code-fixing tool with no annotations and no output schema, the description is incomplete. It lacks details on behavior, output format, error handling, or how it interacts with siblings. This makes it inadequate for an agent to use the tool effectively without additional 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 documentation for 'code' and 'issue_description'. The description adds no additional meaning beyond what the schema provides, such as examples or constraints. According to the rules, when schema coverage is high (>80%), the baseline score is 3, which applies here.
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: 'Fixes bugs or issues in the given code.' It specifies the verb ('fixes') and resource ('bugs or issues in the given code'), making the intent understandable. However, it doesn't explicitly differentiate from sibling tools like 'edit_code' or 'review_code', which might have overlapping purposes, 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. It doesn't mention prerequisites, exclusions, or compare it to siblings such as 'edit_code' or 'test_code'. Without this context, an agent might struggle to select the appropriate tool for code-related tasks.
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 what the tool does ('generates tests') but doesn't explain how it behaves—e.g., whether it overwrites existing tests, requires specific permissions, handles errors, or produces structured output. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary 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 complexity of generating tests (which could involve language-specific frameworks or output formats), the lack of annotations and output schema means the description is incomplete. It doesn't address behavioral aspects, return values, or error handling, leaving gaps for the agent to navigate.
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 ('code' and 'test_framework') adequately. The description doesn't add any meaning beyond what the schema provides, such as examples or constraints, but the high schema coverage justifies the baseline score of 3.
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 ('generates') and resource ('tests for the given code'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'review_code' or 'fix_code', which might also involve testing-related functionality, so 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 'review_code' or 'fix_code'. There's no mention of prerequisites, context, or exclusions, leaving the agent to infer usage based solely on the tool name and description.
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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