Codescene MCP Server vs Qwen DAP MCP
A side-by-side look at the Codescene MCP Server and Qwen DAP MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Codescene MCP Server for developer tools work as a local process; choose Qwen DAP MCP for developer tools as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Codescene MCP Server when
- You need dedicated capabilities in the Developer Tools domain.
- You prefer a local stdio subprocess transport.
- You want tools like
analyze_change_set, code_health_refactoring_business_case, code_health_review.
Choose Qwen DAP MCP when
- You need dedicated capabilities in the Developer Tools domain.
- You prefer a local stdio subprocess transport.
- You want tools like
debug_sessions, debug_compare_runs, debug_snapshot.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Quality signal | 65/100 · Great | 45/100 · Fair |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 71 | 3 |
| Directory activity | 3 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 28 | 18 |
| Full listing | View Codescene MCP Server | View Qwen DAP MCP |
Tools & capabilities
Codescene MCP Server tools 28
analyze_change_setRun a branch-level Code Health review for all files that differ between current HEAD and base_ref. When to use: Use this as a local PR pre-flight check before opening a pull request, so regressions are caught across the full change set. Limitations: - Requires a valid git repository path. - base_ref must exist and be resolvable by git in that repository. - Reviews only files that differ from base_ref. - Output is JSON text from the CLI command. The result can be used to decide whether to refactor before creating or updating a pull request. Returns: A JSON object containing: - quality_gates: the central outcome, summarizing whether the change set passes or fails Code Health thresholds ("passed" or "failed"). - metadata: scope and coverage details from the CLI, including `status` plus file counts such as `checked-file-count`. Use this to distinguish an empty `results` array caused by checked files with no degradations from one caused by no applicable branch changes. - results: an array of objects for each affected file with: - name: the name of the file whose Code Health is impacted. - verdict: "improved", "degraded", "stable", or "unknown". - findings: an array describing improvements/degradation for each code smell. Setup hints: If the result contains `codescene_setup_hint`, do not mention that field. Follow its `message`: briefly explain the benefit, ask permission to add the suggested repository instructions, and wait before editing files. On failure: If this tool returns an error (e.g., the CLI cannot run, or base_ref cannot be resolved by git), run the verify_installation tool to diagnose the cause, then report the failing checks and how to fix them. Example: Compare against base_ref="main" for git_repository_path="/repo" and fail the local PR check if any file verdict is "degraded".code_health_refactoring_business_caseGenerate a data-driven business case for refactoring a source file. When to use: Use this tool to justify refactoring investment with quantified predictions tied to the file's current Code Health. Limitations: - Estimates are model-based projections, not guarantees. - Evaluates one file at a time. - Requires an analyzable source file. Returns: A JSON object with: - scenario: Recommended target Code Health level. - optimistic_outcome: Upper bound estimate for improvements in development speed and defect reduction. - pessimistic_outcome: Lower bound estimate for improvements. - confidence_interval: The optimistic to pessimistic range, representing a 90% confidence interval for the expected impact. On failure: If this tool returns an error (e.g., the CLI cannot run, or the file cannot be analyzed), run the verify_installation tool to diagnose the cause, then report the failing checks and how to fix them. Example: Call with file_path="/repo/src/service.py" and use the optimistic and pessimistic outcomes to frame a refactoring proposal.code_health_reviewReview the Code Health of a single source file and return a detailed CLI review output that includes the score and code smell findings. When to use: Use this tool when you need actionable maintainability diagnostics for one file (not just the score). Limitations: - Analyzes one file at a time. - Requires a supported source file. - Returns CLI review text, not a normalized JSON schema. Returns: A review string from the CodeScene CLI. The output typically includes a Code Health score and code smell details to explain why the score is high or low. The Code Health scores are interpreted as: * Optimal Code: a Code Health 10.0 is optimized for both human and AI comprehension * Green Code: high quality with a score of 9.0-9.9 * Yellow Code: problematic technical debt with a score of 4.0-8.9 * Red Code: severe technical debt, maintainability issues, and expensive onboarding with a score of 1.0-3.9 Setup hints: If the result contains `codescene_setup_hint`, do not mention that field. Follow its `message`: briefly explain the benefit, ask permission to add the suggested repository instructions, and wait before editing files. On failure: If this tool returns an error (e.g., the CLI cannot run, or Code Health cannot be computed), run the verify_installation tool to diagnose the cause, then report the failing checks and how to fix them. Example: Call with file_path="/repo/src/app.py" and summarize the returned smells into prioritized refactoring actions.code_health_scoreCalculate the Code Health score for a single source file. The tool returns one numeric score from 10.0 (optimal) to 1.0 (worst). When to use: Use this tool for quick triage, ranking files by maintainability, or checking whether a refactoring improved file-level quality. Limitations: - Analyzes one file at a time. - Returns only the score summary, not the full smell breakdown. - Requires a supported source file. Returns: A string in the format "Code Health score: <score>". The Code Health scores are interpreted as: * Optimal Code: Code Health 10.0 optimized for human and AI comprehension * Green Code: high quality with a score of 9.0-9.9 * Yellow Code: problematic technical debt with a score of 4.0-8.9 * Red Code: severe technical debt with a score of 1.0-3.9 On failure: If this tool returns an error (e.g., the CLI cannot run, or the score cannot be computed), run the verify_installation tool to diagnose the cause, then report the failing checks and how to fix them. Example: Call with file_path="/repo/src/module.py" and compare the score before and after a refactoring.code_ownership_for_pathFind the owner or owners of a specific path in a project. When to use: Use this tool to identify likely reviewers or domain experts for code reviews and technical questions about a file or directory. Limitations: - Requires a valid project_id. - Uses the latest project analysis data available in CodeScene. - If no matching ownership data is found, an empty JSON array is returned. Returns: A list of owners and their paths that they own. The name of the owner who can be responsible for code reviews or inquiries about the file and a link to the CodeScene System Map page filtered by the owner. Explain that this link can be used to see more details about the owner's contributions and interactions within the project. You MUST always show a link after every owner. Show resulting data in a Markdown table with columns: Owner, Key Areas, Link. On failure: If this tool returns an error (e.g., the CodeScene API cannot be reached, or the access token is missing/invalid), run the verify_installation tool to diagnose the cause, then report the failing checks and how to fix them. Example: Call with project_id=42 and path="/repo/src/service.py", then present each owner row with its corresponding system-map link.download_skillDownload a single skill to a local directory. When to use: Use this tool to install a specific skill into your local skills directory (e.g., ~/.claude/skills/). Limitations: - By default, refuses to overwrite existing skills. - Set overwrite=true to replace an existing skill. - Creates the destination directory if it does not exist. Returns: A confirmation message with the path where the skill was written. Example: Call with skill_name="safeguarding-ai-generated-code" and destination_dir="~/.claude/skills" to install the skill.explain_code_healthExplains CodeScene's Code Health metric for assessing code quality and maintainability for both human devs and AI. When to use: Use this tool when a user asks what Code Health means, how scores are interpreted, or why Code Health matters in day-to-day development. Limitations: - Returns static documentation text from this MCP server package. - Does not analyze a specific repository or file. Returns: Markdown content explaining the Code Health model and core concepts. Example: Call this tool, then summarize the returned Markdown into a short explanation tailored to the user's current question.explain_code_health_productivityDescribes how to build a business case for Code Health improvements. Covers empirical data on how healthy code lets you ship faster with fewer defects. When to use: Use this tool when a user asks for ROI, productivity impact, or management-facing framing for refactoring investments. Limitations: - Returns static documentation text from this MCP server package. - Does not compute project-specific forecasts. Returns: Markdown content describing productivity and defect-risk implications of improving Code Health. Example: Call this tool and extract 2-3 evidence-based points to support a proposal for incremental refactoring.get_configRead current CodeScene MCP Server configuration values. When to use: Use this tool to discover available configuration keys, inspect effective values, and understand where each value comes from. Limitations: - Returns JSON text only; callers must format it for display. - Sensitive values (tokens) are masked. - Effective values can be overridden by client-provided env vars. When called without a key, lists every available configuration option together with its current effective value, the source of that value (environment variable vs. config file), and a short description. When called with a specific key, returns details for that option only. Sensitive values (tokens) are masked in the output. Returns: A JSON string. When querying a single key, the object has: key, env_var, value, source, description, aliases, and docs_url. When listing all, the object has: config_dir and options (array of the same shape). Use the aliases array to match user intent to the correct key. Present the data clearly and always include docs_url links. Example: Call with key="access_token" to inspect one setting, or call without key to list all configurable options.get_skill_manifestGet the file manifest for a specific skill. When to use: Use this tool to inspect what files a skill contains, their sizes, and SHA256 hashes before downloading. Limitations: - Requires a valid skill name from list_skills. Returns: A JSON manifest with the skill name and an array of files, each with path, size in bytes, and sha256 hash. Example: Call with skill_name="safeguarding-ai-generated-code" to see the manifest, then use download_skill to install it.list_skillsList all available skills embedded in this MCP server. When to use: Use this tool to discover what skills are available for download or inspection. Limitations: - Returns only skills embedded at compile time. - Does not scan external skill directories. Returns: A formatted list of skill names with their descriptions. Example: Call this tool to see all available skills, then use download_skill or sync_skills to install them locally.list_technical_debt_goals_for_projectLists the technical debt goals for a project. When to use: Use this tool to see all files in a project that currently have explicit technical debt goals in CodeScene. Limitations: - Requires a valid project_id. - Returns goal data from the latest available analysis. - Includes only files with non-empty goals. Returns: A JSON object with two fields: - `data`: an array of objects each containing the path of a file and its goals. - `link`: a URL to the CodeScene Code Biomarkers page for the project. Show the goals for each file in a structured format that is easy to read and explain the goal description for each file. It also includes a description, please include that in your output. Always include the `link` in the output and explain that more details about the technical debt goals can be found on that page. On failure: If this tool returns an error (e.g., the CodeScene API cannot be reached, or the access token is missing/invalid), run the verify_installation tool to diagnose the cause, then report the failing checks and how to fix them. Example: Call with project_id=42 and summarize each file's goals with the biomarkers link for deeper inspection.- +16 more on the listing page
Qwen DAP MCP tools 18
debug_sessionsUse this tool to list, create, or close isolated DAP sessions before routing other debugger tools with sessionId. Omit sessionId on normal debug tools only when the backward-compatible default session is intended; do not use a global session selector for concurrent work.debug_compare_runsCompare bounded runtime evidence from two existing stopped DAP sessions. Use this for baseline or known-good versus candidate or failing differential debugging after both sessions have reached comparable stops. It compares stack identities, locals, registers, exception state, symbol health, and modules semantically; non-null raw address-only changes are marked unstable rather than causal evidence. Do not use it to launch, resume, or alter either target; prepare both runtime states with the normal lifecycle tools first.debug_snapshotCapture bounded evidence from the current stopped debug state without resuming execution. Use this when an agent needs raw stack, locals, registers, exception details, modules, or nearby instructions; prefer debug_diagnose_stop when you also want ranked crash hypotheses and project-frame selection. This is read-only with respect to the debuggee and returns best-effort evidence plus collection errors for optional data that an adapter cannot provide.debug_statusInspect debugger lifecycle state, the selected stop, recent DAP events, and bounded adapter stderr without changing target execution. Use this to determine whether a session is initialized, running, stopped, postmortem, exited, or failed before choosing another debug tool; it is diagnostic only and does not resume, launch, attach, or terminate the debuggee.debug_disconnectEnd the active DAP session and stop its local adapter process. Use this when debugging is finished or the session must be reset; do not call it when more runtime evidence is still needed. With terminateDebuggee=true it may also terminate a live target process, so this operation can destroy the current runtime state and cannot be treated as read-only.debug_run_to_stopRun one live launch or attach through an already initialized DAP adapter until the first stopped, exited, or terminated event. Use this when you need deterministic runtime evidence from a reproduction; do not use it for a postmortem dump or when executing/attaching to the local target is not authorized. Launch mode executes application code and attach mode changes debugger control of an existing process, so normal target side effects may occur before the stop. Returns the request result, terminal/stopped outcome, session status, and a bounded snapshot only when a stopped event is captured.debug_diagnose_stopDiagnose an already stopped live or postmortem debug session without changing execution state. Use this when a debugger has captured the failure and you want project-frame selection, crash classification, operand/register/variable bindings, call-chain provenance, ranked hypotheses, and a fix/rebuild/reproduce/verify plan; use debug_snapshot instead when only raw evidence is needed. The tool is read-only and returns both the bounded snapshot and the derived diagnosis, including collection limitations when optional debugger evidence is unavailable.debug_source_disassemblyCorrelate source locations, disassembly, registers, and pointer-like locals for the raw fault frame and the first likely project-controlled frame. Use this for low-level root-cause evidence when a crash is already stopped and instruction/operand provenance matters; prefer debug_diagnose_stop for a broader ranked diagnosis. This is read-only, does not resume the target, and returns frame-selection reasoning, both correlations, operand bindings, and any best-effort collection errors.debug_this_crashPreferred high-level native crash workflow. Use mode=current for an already stopped session, dump for read-only postmortem analysis, codelldb to discover CodeLLDB, lldb-dap to discover upstream LLVM lldb-dap, gdb to discover GNU GDB built-in DAP interpreter, or live with an already initialized generic DAP adapter. Do not use codelldb/lldb-dap/gdb/live when executing or attaching to the target is not authorized: those modes can run application code and cause its normal side effects, while current/dump only inspect stopped evidence. workflow.stage selects initial diagnosis, verification against a prior baseline, or the bounded autonomous cycle. Returns runtime evidence, diagnosis, verification/autonomous metadata, and debugger status with deterministic fixed/blocked/budget-exhausted outcomes where applicable.debug_causal_traceBuild a bounded consumer-to-writer evidence chain for one suspicious debugger-visible value. Use it after crash or differential evidence identifies a value whose runtime producers matter. It combines the current stopped snapshot with repeated watchpoint writer tracing. Do not use it for frozen dumps or unsafe-to-resume targets, and do not treat an observed writer as automatic proof of root cause.debug_progress_probeSample a live target across short resume/pause intervals to distinguish no observed progress, same-frame execution movement, and probable busy loops. Use it when a process appears hung but a single thread snapshot cannot distinguish blocking from spinning. Do not use it when resuming the target is unsafe; sampling perturbs scheduling and is not proof of application-level progress.debug_runtime_reportCreate a shareable structured report from the current stopped target with normalized crash fingerprinting, Symbol Doctor status, sanitizer stderr correlation, poison-pattern memory hazard detection, ABI register-to-argument mapping, and recent debugger output. Use it only for evidence-driven triage and issue handoff. Do not treat heuristic poison patterns, ABI mappings, or sanitizer text correlation as standalone proof.- +6 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Codescene MCP Server
{
"mcpServers": {
"codescene-mcp-server": {
"command": "npx",
"args": [
"-y",
"@codescene/codehealth-mcp"
]
}
}
}
Qwen DAP MCP
{
"mcpServers": {
"qwen-dap-mcp": {
"command": "npx",
"args": [
"-y",
"@slp-dev1/qwen-dap-mcp"
]
}
}
}
Frequently asked questions
Codescene MCP Server is categorized under Developer Tools and uses a local stdio subprocess. Qwen DAP MCP belongs to Developer Tools and uses a local stdio subprocess. Pick Codescene MCP Server for developer tools work and Qwen DAP MCP for developer tools.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Codescene MCP Server has no declared pricing — check the project's own site. Qwen DAP MCP has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add codescene-mcp-server and qwen-dap-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Codescene MCP Server has a quality signal of 65/100 (Great) with 3 views, 0 installs and 71 GitHub stars. Qwen DAP MCP has 45/100 (Fair) with 3 views, 0 installs and 3 GitHub stars.