Codescene MCP Server vs Kiprio MCP
A side-by-side look at the Codescene MCP Server and Kiprio 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 Kiprio 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 Kiprio MCP when
- You need dedicated capabilities in the Developer Tools domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
KIPRIO_API_KEY. - You want tools like
validate_email, dns_lookup, whois_lookup.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Quality signal | 65/100 · Great | 56/100 · Good |
| 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 | KIPRIO_API_KEY |
| Verified owner | Community listing | Community listing |
| GitHub stars | 72 | 0 |
| Directory activity | 3 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 28 | 27 |
| Full listing | View Codescene MCP Server | View Kiprio 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
Kiprio MCP tools 27
validate_emailValidate an email address for deliverability and quality. Performs multi-layer validation: RFC-5322 syntax check, MX record lookup to confirm the domain accepts mail, disposable/temporary domain detection (against 50,000+ known throwaway providers), and role-address detection (e.g. admin@, noreply@). Use this before sending email campaigns, checking signups, or cleaning a mailing list to reduce bounce rates and spam complaints. Args: email: The email address to validate (e.g. 'user@example.com'). Returns: valid (bool): True if the address appears deliverable. reason: Human-readable explanation of the result. mx_found (bool): Whether the domain has working mail servers. is_disposable (bool): True if a known throwaway email provider. is_role_address (bool): True if a shared/role address (admin@, info@).dns_lookupLook up DNS records for a domain across multiple record types. Queries authoritative DNS servers for the specified record types and returns all matching records with TTL values. Useful for diagnosing email deliverability (MX records), verifying domain ownership (TXT), tracing CDN configuration (CNAME), and checking propagation status. Args: domain: Domain name to look up (e.g. 'example.com', 'mail.example.com'). record_types: Comma-separated record types to query. Supported: A (IPv4 addresses), AAAA (IPv6), MX (mail servers), TXT (verification, SPF, DKIM), NS (nameservers), CNAME (aliases), SOA (zone authority). Default: 'A'. Example: 'A,MX,TXT' to check all email-related records at once. Returns: records: List of DNS records, each with type, value, and ttl. query_time_ms: How long the DNS resolution took.whois_lookupRetrieve WHOIS registration data for a domain name. Returns publicly available registration details from WHOIS databases. Useful for domain due diligence, checking expiry before purchasing, identifying registrar, or investigating domain ownership. Note: Many registrars apply GDPR redaction to registrant contact details. Technical data (nameservers, dates, registrar) is always available. Args: domain: Domain to look up (e.g. 'example.com'). Supports most TLDs. Returns: registrar: Registrar name and IANA ID. registered_at: Domain creation date (ISO 8601). expires_at: Expiry date — domain may be purchased after this. updated_at: Last modification date. nameservers: List of authoritative nameservers. status: EPP status codes (e.g. clientTransferProhibited). registrant: Registrant contact (may be redacted under GDPR).ssl_checkInspect the SSL/TLS certificate for a host. Connects to the host on port 443 and retrieves the full certificate chain. Returns validity status, expiry countdown (critical for alerting), issuer details, Subject Alternative Names (SANs), and cipher suite information. Use to monitor certificate expiry, verify correct certificate installation, check SANs cover all required subdomains, or audit cipher strength. Args: host: Hostname to check (e.g. 'example.com', 'api.example.com'). Do not include https:// or path components. Returns: valid (bool): Whether the certificate is currently valid and trusted. subject: Common name on the certificate. issuer: Certificate Authority that issued it. issued_at: Certificate issuance date. expires_at: Certificate expiry date. days_remaining: Days until expiry (negative = already expired). sans: List of Subject Alternative Names (all covered hostnames). chain_length: Number of certificates in the chain.ip_lookupGeolocate an IP address and detect VPN, Tor, and datacenter usage. Returns geolocation data (country, city, coordinates, timezone, ISP) plus threat intelligence signals: whether the IP is a known VPN exit node, Tor relay, or datacenter/hosting provider. Includes a composite threat score. Use for fraud detection, access control, personalising content by region, or investigating suspicious traffic in server logs. Args: ip: IPv4 or IPv6 address to look up (e.g. '8.8.8.8', '2001:4860:4860::8888'). Returns: ip: The queried IP. country, country_code: Full country name and ISO-3166 code. region, city: Geographic subdivision and city name. latitude, longitude: Approximate coordinates (city-level precision). timezone: IANA timezone (e.g. 'America/New_York'). isp: Internet Service Provider name. is_vpn (bool): Known VPN exit node. is_tor (bool): Tor exit relay. is_datacenter (bool): Hosted in a cloud/datacenter AS. threat_score: 0.0–1.0 composite risk score.screenshot_urlCapture a screenshot of a web page and return it as a base64-encoded PNG. Renders the page in a headless Chromium browser with JavaScript execution, waits for network idle (all resources loaded), then captures the viewport or full page. Useful for visual regression checks, archiving page state, generating previews, or letting an AI model analyse page layout. Args: url: Full URL of the page to capture (must include https://). width: Viewport width in pixels (default 1280, range 320–3840). height: Viewport height in pixels (default 800, range 200–2160). full_page: If True, capture the full scrollable page height rather than just the visible viewport. May be slow for long pages. Returns: png_b64: Base64-encoded PNG image data. w: Actual image width in pixels. h: Actual image height in pixels. bytes: File size of the PNG in bytes. ms: Time taken to render and capture in milliseconds.readabilityExtract the main article text and metadata from a web page. Uses Mozilla's Readability algorithm (the same engine as Firefox Reader Mode) to strip navigation, ads, and boilerplate and return only the article content. Ideal for summarising articles, building reading lists, or feeding clean text into further AI processing without HTML noise. Args: url: Full URL of the article or page to extract (must include https://). Returns: title: Article or page title. byline: Author name(s) if detected. content: Plain text of the main article body. word_count: Number of words in the extracted content. published_at: Publication date if found in page metadata (may be null). excerpt: Short description/lead paragraph if available.tech_stackDetect the technology stack powering a website. Analyses HTTP headers, HTML meta tags, JavaScript fingerprints, and resource paths to identify the CMS, frontend framework, CDN, analytics platform, e-commerce engine, and other technologies in use. Useful for competitive research, sales prospecting, migration planning, or understanding what stack a client is running before proposing solutions. Args: url: Full URL of the site to analyse (must include https://). The homepage is usually sufficient; the scan does not crawl. Returns: technologies: List of detected technologies, each with: name: Technology name (e.g. 'WordPress', 'Cloudflare', 'React'). category: Category (CMS, CDN, Analytics, Framework, etc.). confidence: Detection confidence 0.0–1.0. version: Version string if detectable (may be null).parse_og_tagsExtract Open Graph, Twitter Card, and standard meta tags from a URL. Fetches the page and parses all social-sharing metadata. Returns the full set of og:*, twitter:*, and standard HTML meta tags in a structured format. Use for link preview generation, SEO auditing, or validating that social sharing thumbnails and descriptions are correctly set. Args: url: Full URL of the page to parse (must include https://). Returns: title: Page title (og:title or <title>). description: Meta description or og:description. image: Primary social sharing image URL (og:image). site_name: og:site_name if set. type: og:type (e.g. 'article', 'website'). open_graph: Full dict of all og:* key-value pairs. twitter: Full dict of all twitter:* key-value pairs. favicon: URL of the site favicon. lang: Page language code (e.g. 'en', 'fr'). author: Author meta tag if present. published_at: article:published_time if present. keywords: Meta keywords if present.sentiment_analysisAnalyse the emotional tone and sentiment of a piece of text. Uses a fine-tuned language model to classify the overall sentiment and return a continuous compound score. Handles negation, sarcasm signals, and mixed-sentiment text. Works well on reviews, social posts, support tickets, survey responses, and news excerpts. Args: text: Text to analyse. Optimal range: 10–2000 characters. For longer documents, pass key paragraphs separately. Returns: label: Overall sentiment — 'positive', 'negative', or 'neutral'. compound: Score from -1.0 (most negative) to +1.0 (most positive). confidence: Model confidence in the classification, 0.0–1.0. positive: Proportion of positive signal in the text. negative: Proportion of negative signal in the text. neutral: Proportion of neutral signal in the text.check_grammarCheck and rewrite text for grammar, spelling, punctuation, and style. Runs the text through a language model tuned for editing. Returns the corrected text and an itemised list of changes with explanations. Supports multiple style modes for different audiences and registers. Free tier: up to 500 characters. Pro tier: up to 10,000 characters. Args: text: Text to check and rewrite. style: Rewrite target style — one of: 'concise' (default): Remove filler words, tighten sentences. 'formal': Business or academic register. 'friendly': Warm, conversational tone. 'UK-en': British English spelling and idioms. 'US-en': American English spelling and idioms. Returns: corrected: Rewritten text applying all suggested changes. edits: List of individual changes, each with: original: The original phrase. replacement: The corrected phrase. explanation: Why this change was made. chars_in: Character count of the input. chars_out: Character count of the corrected output.translate_textTranslate text between languages using neural machine translation. Supports 100+ languages. Auto-detection identifies the source language if not specified. Preserves paragraph structure and handles common HTML entities. Suitable for document translation, multilingual content generation, and supporting international users. Args: text: Text to translate. May include multiple paragraphs. target: Target language as a BCP-47 code (e.g. 'fr', 'de', 'es', 'zh', 'ja', 'ar', 'pt', 'ru', 'ko', 'hi'). source: Source language code, or 'auto' to detect automatically (default: 'auto'). Returns: translated: The translated text. source_language: Detected or specified source language code. target_language: Target language code. confidence: Source language detection confidence (0.0–1.0).- +15 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"
]
}
}
}
Kiprio MCP
{
"mcpServers": {
"forevertools-kiprio-mcp": {
"command": "uvx",
"args": [
"kiprio-mcp"
],
"env": {
"KIPRIO_API_KEY": "YOUR_KIPRIO_API_KEY_HERE"
}
}
}
}
Frequently asked questions
Codescene MCP Server is categorized under Developer Tools and uses a local stdio subprocess. Kiprio MCP belongs to Developer Tools and uses a local stdio subprocess. Pick Codescene MCP Server for developer tools work and Kiprio 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. Kiprio 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 forevertools-kiprio-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 72 GitHub stars. Kiprio MCP has 56/100 (Good) with 1 views, 0 installs and 0 GitHub stars.