pycharm-code-quality-mcp

by yingsf

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v1.0.3
Disambiguation3/5
There is some overlap between jetbrains_inspect_git_changes and code_quality_analyze_git_changes, both operating on git changes but with different backends. However, code_quality_status and code_quality_clear_cache are distinct, and descriptions help differentiate.
Naming Consistency3/5
Tool names use two prefixes (jetbrains_inspect, code_quality_) and mix verbs (inspect, status, analyze, clear) inconsistently. For example, 'code_quality_status' lacks a verb, while others follow verb_noun pattern. Mixed but readable.
Tool Count5/5
Five tools cover code quality status, git change analysis, file inspection, and cache clearing—well-scoped for the server's purpose without being excessive or insufficient.
Completeness3/5
Missing a unified backend analysis for arbitrary files (only git changes). Also lacks configuration or project-level analysis tools, but core workflows for inspecting git changes and files are covered.
Average 4.1/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 29 commits in the last 12 weeks
- Last stable release on July 23, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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{
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  ]
}
```
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses scanning scope (all files, tracked+untracked, .gitignore-respected), file type defaults, and constraints (200-file limit, dedup options). However, it does not disclose whether the tool mutates state, authorization needs, or detailed backend 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 three sentences, front-loaded with the main action. It is efficient without wasted words, though it could be structured more (e.g., bullet points for parameters).
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?
With 6 parameters and no annotations, the description needs to cover key behavior. It mentions limits and dedup, but omits explanations for backend_mode and errors_only. It lacks differentiation from jetbrains tools. Output schema exists, so return values are covered, but overall completeness is moderate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description must compensate. It adds meaning for project_root and extensions, but fails to explain errors_only, backend_mode, include_untracked (though consistent with scope), and deduplication_mode. Only 2 of 6 parameters get extra context, leaving significant gaps.
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 verb 'Scan all files' and the resource 'git repository under project_root'. It differentiates from sibling tools by specifying 'Use this when you want the whole repo rather than just git changes', contrasting with code_quality_analyze_git_changes.
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 on when to use: 'when you want the whole repo'. It mentions extending file types via extensions and references code_quality_analyze_files for limits and dedup options. However, it lacks explicit when-not-to-use or full listing of alternatives.
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?
No annotations provided, so description bears full burden. It discloses that deleted files are excluded, uses unified backend strategy, and mentions options. However, it does not describe what the analysis entails (e.g., violations, metrics) or potential side effects.
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?
Two sentences that efficiently convey the core operation, exclusions, and reference to sibling tool options. No redundancy or wasted words.
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?
Adequately explains input collection but lacks details about output schema (though it exists), parameter behavior for most fields, and what 'analyze' produces. Sufficient for basic understanding but incomplete for nuanced use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but description only explains deduplication_mode and backend_mode by reference to another tool. The other 6 parameters (base_ref, errors_only, include_staged, etc.) are not described, leaving them undefined.
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?
Description clearly states it collects changed files (staged, unstaged, untracked) relative to base_ref and analyzes them. It distinguishes from sibling 'code_quality_analyze_files' by referencing same deduplication and backend_mode options.
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?
Provides context about what changes are included (staged, unstaged, untracked) and excludes deleted files. References another tool's options but does not explicitly state when to use this tool over siblings like 'jetbrains_inspect_git_changes' or when not to use it.
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?
No annotations are provided, so the description must carry the full behavioral burden. It discloses that the tool only inspects changed files, excludes deleted files, and uses a base ref. However, it does not mention side effects (likely read-only), performance implications, or required permissions, leaving some behavioral aspects unclear.
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?
Two sentences provide all essential information: the core operation (collect and inspect), scope (staged/unstaged/untracked relative to base_ref), and a notable exclusion (deleted files). No unnecessary words.
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?
The description adequately covers the main workflow for a tool with an output schema. It could elaborate on what 'inspect' entails or list potential prerequisites (e.g., a valid git repo), but for most use cases it provides sufficient context.
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?
With 0% schema description coverage, the description adds significant value by clarifying that parameters relate to staged, unstaged, and untracked files relative to base_ref, and that deleted files are excluded. It does not detail each parameter individually but provides enough context to understand their roles.
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 function: collect changed files from git (staged, unstaged, untracked) relative to a base ref and inspect them using JetBrains inspections. It also notes exclusions (deleted files), making the purpose specific and unambiguous.
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 does not provide any guidance on when to use this tool versus alternatives like jetbrains_inspect_files or code_quality_analyze_git_changes. There is no mention of prerequisites or scenarios where this tool is preferred or should be avoided.
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, the description carries full burden. It discloses key behaviors: file count limit, backend selection logic, cross-backend deduplication, and configurable filtering. However, it omits prerequisites (e.g., running IDE, Sonar installation) and performance implications, which would be helpful for a tool integrating external services.
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 two well-structured sentences. The first sentence presents the core action and constraints; the second adds configurable options. Every phrase contributes value without redundancy.
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 sophisticated tool with multiple backends and deduplication, the description covers the main workflow and configurable modes. It reasonably explains the output ('unified findings'). While an output schema exists (so return value detail is handled structurally), the description could be more complete by mentioning prerequisites or typical use 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?
Schema description coverage is 0%. The description clarifies two parameters (errors_only, deduplication_mode) and explains backend_mode's default and auto behavior. However, project_root and file_absolute_paths are not mentioned beyond the initial statement, leaving gaps for a 5-parameter tool.
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 action ('Analyze'), resource ('absolute file paths'), constraints ('1 to 200'), default backend behavior, and output characteristics. It effectively distinguishes from siblings like code_quality_analyze_git_changes and code_quality_analyze_project.
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?
While the description explains the default backend strategy, it does not provide explicit guidance on when to choose this tool over alternatives like jetbrains_inspect_files or code_quality_analyze_git_changes. Usage context is implied but not articulated with exclusions.
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 full burden. It discloses the file count limit, session reuse, non-abort behavior on file failure, and output format (inspection problems with 1-based ranges). It does not explicitly state read-only nature or permissions, but covers key 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two sentences that front-load the core purpose and include key behavioral notes. Every sentence adds value without 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 the absence of annotations and 0% schema coverage, the description is reasonably complete for core usage but lacks parameter details for timeout_ms, errors_only, and project_root. The existence of an output schema mitigates the need to describe return format, but the missing parameter semantics reduce completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description only elaborates on 'file_absolute_paths' (noting they must be absolute). The other parameters (timeout_ms, errors_only, project_root) are left completely unexplained, forcing the agent to infer from names alone. This is insufficient compensation for missing schema descriptions.
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 action ('inspect'), the resource ('absolute file paths'), and the mechanism ('PyCharm's built-in JetBrains inspections via local JetBrains MCP Server'). It distinguishes from sibling tools like jetbrains_inspect_git_changes by specifying file paths rather than git changes.
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 implies usage for inspecting specific file paths (1-200) and mentions session reuse and independent file failure handling, but does not explicitly state when to avoid this tool in favor of alternatives like code_quality_status or code_quality_analyze_git_changes. However, the context provides sufficient differentiation via sibling names.
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, the description discloses the behavior: it clears caches for two backends and allows scoping via project_root. This adds transparency beyond the name, though it does not mention potential side effects or safety implications.
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 consists of two sentences with no redundancy. The first sentence states the main action, and the second provides detail on the parameter. Every word adds value.
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 tool's simplicity (one optional parameter, output schema exists), the description is nearly complete. It could mention if the operation is safe to call repeatedly, but this is non-critical.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The parameter 'project_root' has no schema description (0% coverage), but the description explains its effect: it limits clearing to one Sonar project's port mapping. This fully compensates for the schema gap.
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 clears in-memory caches for specific backends (Sonar port discovery, JetBrains session). This distinguishes it from sibling tools which focus on inspection and analysis, not cache management.
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 the tool is used for cache clearing but does not explicitly state when to use it versus alternatives, nor does it provide conditions for not using it. The sibling tools are different in function, but no guidance is given.
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, the description carries full burden. It details what is checked (configured, reachable, project-ready, tool exposed) and notes optional status. However, it doesn't state whether the probe is read-only, has side effects, or requires authentication, leaving some transparency 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?
Two concise sentences with all critical information front-loaded. First sentence enumerates checks; second adds optional behavior and usage. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless diagnostic tool with an output schema, the description covers all essential aspects: what is probed, optional behavior, and usage intent. No missing context given the tool's simplicity.
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 zero parameters, so schema coverage is 100%. The description adds context about the tool's checks (configuration, reachability, etc.) beyond what the schema shows, adding value.
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?
Description clearly states it probes the JetBrains MCP Server and reports on configuration, reachability, project-readiness, and exposure of specific tool get_file_problems. It distinguishes from sibling tools like code_quality_* and jetbrains_inspect_* by being diagnostic instead of analytical.
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?
Explicitly states 'Use this for diagnostics,' providing clear usage intent. While it doesn't compare to alternatives, the diagnostic nature sets it apart from sibling action tools, and no exclusion criteria are needed.
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?
The description accurately reflects a read-only status check with no destructive hints. It could be improved by noting whether it makes network calls or handles errors, but it sufficiently conveys the non-mutating nature given the lack of annotations.
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?
Two sentences with no wasted words: the purpose and usage are front-loaded, and every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (not shown) and zero parameters, the description is complete—it explains what the tool does and when to use it, without needing to detail return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the description need not add parameter information. The schema coverage is 100% (vacuous), and the description is adequate.
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 that the tool reports availability and status of two specific backends (JetBrains MCP Server and SonarQube), which distinguishes it from sibling tools that perform inspections or analyses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs 'Use this first to see which backends are configured and ready,' providing clear guidance on when to use this tool before others.
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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