Codescene MCP Server vs Mellos Mapping
A side-by-side look at the Codescene MCP Server and Mellos Mapping 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 Mellos Mapping 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 Mellos Mapping when
- You need dedicated capabilities in the Developer Tools domain.
- You prefer a local stdio subprocess transport.
- You want tools like
mmap_declare, mmap_update, mmap_read.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Quality signal | 65/100 · Great | 67/100 · Great |
| 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 | 114 |
| Directory activity | 3 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 28 | 8 |
| Full listing | View Codescene MCP Server | View Mellos Mapping |
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
Mellos Mapping tools 8
mmap_declareGrow the Mellos map: set the title and diagram kind, add layer bands, lanes and groups (labeled subsystems within ONE band — declare them when a single band grows crowded, roughly five or more nodes in that band; a group must be a strict subset of its band, and a map spread thin across many bands needs none), add nodes, add dependency edges. Declare the missing design after reading existing pages with mmap_read; reuse verified nodes. Edges must point strictly downward (a node may only use nodes on lower layers); the batch is all-or-nothing. Title and kind can also be changed with mmap_update; this legacy form remains supported. Revising what already exists (moving, renaming, relabeling, clearing) is mmap_update.mmap_updateThe revision tool, all-or-nothing. Record progress on nodes: in-progress when starting a node (the pane spins), done with evidence when its verification passes, regressed with evidence when a done node breaks. Revise what the ghost design got wrong: move a node to another band, join or leave a group or lane, rename a band (or re-rank it, which reorders the whole map), relabel a group or a lane. Every clearable field takes null to empty it — that is how a field is cleared, never an empty string. Bands, groups and lanes are applied before the node updates, and within one node update `layer` moves the node before its other fields. The map is a ledger: report honestly, it never blocks you.mmap_readRead existing pages before creating a map. Returns structured IDs, revisions and bounded results. pages lists summaries; map reads page metadata/context; nodes/edges/layers/groups/lanes read editable records; neighborhood reads related nodes; changes compares saved source hashes with local files. New conversations and compacted context should resume the existing effort. mmap_view remains the picture.mmap_batchAtomically combine additions, updates and removals on ONE page. Checks the final graph, so a coordinated move or edge replacement needs no intermediate saves. Read IDs and revision with mmap_read first. Cross-page deletion is excluded.mmap_removeRemove edges, nodes, groups and empty layer bands (in that order, all-or-nothing). Removing a node also removes every edge touching it; removing a group merely ungroups its members. Use when the ghost design turns out wrong — the map is a hypothesis, revising it is honest work. `pages` is the other scale: it DELETES whole page files, so a finished effort can be cleaned up instead of accumulating tabs forever. A bare `{pages: ["slug"]}` with no other field is the normal form; combined with map edits, the edits are applied first and the pages are deleted after. The deletion is permanent and cannot be undone, so delete only pages whose effort is over — and only ever with the user behind it. An unknown slug is refused with the project's real page list (naming a page that does not exist is a typo, not a request). The default page has no slug and is not deletable here. A node elsewhere still pointing at a deleted page with `submap` stays legal — a submap reference has no existence invariant — but it has nowhere to dive until the page comes back.mmap_setupGet or set the mapping policy — WHEN the assistant opens a Mellos map. Call with no arguments to read it: the reply names the policy chosen for the USER (every project), the one this PROJECT overrides it with if any, and which of them is in effect. If it reports "not set", ask the USER to choose (never pick for them): always = map every structured task — workflows, designs, architecture, technical dependencies; complex = map only medium or complex tasks — several modules, a new subsystem, roughly an hour or more; on-request = map only when the user explicitly asks. Then call again with their choice to persist it. It defaults to user scope, which is the normal one — the question is about how someone works, so it is asked once ever, not once per repository. Pass scope: "project" only when the user wants THIS project to differ from that habit. The policy guides you; it never blocks the tools, and an explicit user request for a map always wins.mmap_viewRender the current Mellos map as monochrome text — the same picture the split-pane watcher shows live. Use it to check the map state or to show it inline in conversation. Every response ends with a `pages:` line naming the pages this project actually has and which one you are looking at, so this is also how you discover whether a map exists at all and under which slugs — never probe the files.mmap_openFor automatic display beside the current ChatGPT desktop conversation in Codex mode, use surface: "web-terminal", then call open_in_codex with the returned browser hostOpen object. No paste or Computer Use is needed. For the native host terminal, use surface: "codex-terminal": prepare absolute watcher commands for the current project, then ask the host to open its right terminal. This does not launch the watcher or type into that terminal. Agent exec PTYs cannot be attached using their numeric session ids. For a document panel, use surface: "markdown": generate MD + SVG files, enable automatic preview updates after successful map writes, then use the HOST file-opening tool to display the returned absolute Markdown path on the right of the current conversation. Generated does not mean visible: this server cannot open or observe the desktop side panel. For interactive maps, choose surface: "web": start or reuse a project-local web viewer and pass the returned URL to the host browser-opening tool. Markdown and terminal remain available. The default surface is "terminal", preserving the terminal workflow. Put the live map on the user's screen: a terminal pane beside this conversation that redraws on every write. Call it whenever a result says `pane: CLOSED` — and do NOT ask permission first, because a user who has set a mapping policy has already said they want to see the map. With a pane already open this RETARGETS it to `page` instead of opening a second one, so it is also how you show the user a particular page when they ask for one. It never closes a pane: taking the map off the screen belongs to the user (the `q` key in the pane, or typing `mmap` in a terminal). The reply says whether a pane actually reported itself in afterwards, not merely that a command was run. Automatic terminal opening supports Windows Terminal, tmux on Linux/macOS, and Pebrel, which splits the conversation's own pane. omp (Oh My Pi) has no side panel of its own: there the routes are this same terminal pane (a Windows Terminal split, or a tmux split on Linux/macOS) and the web viewer, whose URL the session opens with its own browser tool. If opening fails, relay the reason and copyable command; retry only after the environment changes or the user asks.
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"
]
}
}
}
Mellos Mapping
{
"mcpServers": {
"mellos-mapping": {
"command": "npx",
"args": [
"-y",
"mellos-mapping"
]
}
}
}
Frequently asked questions
Codescene MCP Server is categorized under Developer Tools and uses a local stdio subprocess. Mellos Mapping belongs to Developer Tools and uses a local stdio subprocess. Pick Codescene MCP Server for developer tools work and Mellos Mapping 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. Mellos Mapping 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 mellos-mapping 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. Mellos Mapping has 67/100 (Great) with 1 views, 0 installs and 114 GitHub stars.