Codescene MCP Server vs Entity Enricher
A side-by-side look at the Codescene MCP Server and Entity Enricher 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 Entity Enricher for developer tools over a hosted endpoint. 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 Entity Enricher when
- You need dedicated capabilities in the Developer Tools domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
upload_attachment, delete_attachment, start_batch_enrichment.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Quality signal | 65/100 · Great | 57/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Remote HTTP/SSE |
| 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 | 72 | 0 |
| Directory activity | 3 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 28 | 58 |
| Full listing | View Codescene MCP Server | View Entity Enricher |
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
Entity Enricher tools 58
upload_attachmentUpload base64 file bytes as reusable source material; returns id and requires_capability. Pass the ID in attachment_ids to sample/schema generation, enrichment or benchmarks. Supported format handling depends on server MIME policy: extracted text or model-readable binary. Prefer auto model selection for attachment capabilities. Uploading does not itself run an LLM. A generated sample with attachments is source-only; see enricher://docs/documents for formats, multiple-file behavior and research workflows. Retain attachments needed for later runs or regeneration.delete_attachmentPermanently delete an attachment in your organization, including its stored file. No LLM call. Existing records remain, but future calls or schema regeneration cannot reuse the deleted source. Delete only when that source is no longer needed; this is not a required post-enrichment step. Returns the deleted ID and filename.start_batch_enrichmentStart billed asynchronous enrichment of an entity list against exactly one of schema_id or target_schema. Returns job_id and total. No fixed entity-count cap; live prompt quotas and credits can stop remaining work. Each entity follows the enrichment/fusion pipeline; every model must succeed for its automatic fusion and database admission. A confident classification mismatch skips that entity without enrichment, never pauses the batch. Poll get_job_status, then list_records(job_id=...). Attachments apply to every entity. Unlike enrich_entity, this tool exposes neither database_sync=false nor web-search activation. Input contracts, partial results and recovery: enricher://docs/batch-enrichment.list_benchmark_scenariosList compact benchmark scenario summaries and total. Scenarios test enrichment, sample generation or schema generation. No LLM call. Read one with get_benchmark_scenario or create one with create_benchmark_scenario. Lifecycle and reference requirements: enricher://docs/model-benchmark.get_benchmark_scenarioRead one benchmark scenario with per-model quality, cost and speed results. include_reference=true adds the reference, the fixed entity_data and the schema-generation entity_samples. Config changes can make existing results stale. No LLM call. For a ranked subset use get_benchmark_scenario_results. Read after run_benchmark completes.get_benchmark_scenario_resultsFilter, rank and limit a scenario's per-model benchmark results. No LLM call. overall blends quality, speed and cost using organization task weights and is null if a component is missing. Status tags are independent: success does not exclude stale or stale_score results. Missing sort metrics come last in either direction. See enricher://docs/model-benchmark for interpretation.create_benchmark_scenarioCreate a reusable benchmark with a mandatory scoring judge. scenario_type='enrichment' needs schema_id and entity_data (the entity to enrich, as enrich_entity takes it — refused when it carries no value; never put it in description); 'sample_generation' needs sample_request; 'schema_generation' needs entity_samples (1..20 samples of one entity type). Enrichment and schema generation need a verified gold reference via set_benchmark_reference before running. Sample generation is rubric-scored and takes no reference. Requires owner and a plan with benchmarks; creating the scenario does not run the models. Returns the scenario and link. Next call run_benchmark when its reference requirements are satisfied. See enricher://docs/model-benchmark.update_benchmark_scenarioEdit a benchmark's test definition or scoring configuration. Requires owner and a plan with benchmarks; no model run. Only supplied fields change, but sample_params and schema_gen_params replace their parameter objects wholesale. The judge may be replaced, not cleared; scenario_type is immutable. Changed test definitions make previous results stale. Re-run affected models to refresh their scores. See enricher://docs/model-benchmark.set_benchmark_referenceSave the gold reference for an enrichment or schema-generation benchmark. Only set reference_verified=true after checking its values against trusted evidence or obtaining human sign-off; a generated answer alone is not verification. Schema-generation references must be GeneratedJsonSchema objects. Sample-generation scenarios reject references because they are rubric-scored. Requires owner and a plan with benchmarks; no LLM call. A verified reference enables run_benchmark. See enricher://docs/model-benchmark.revert_benchmark_reference_updatesUndo automatic edits a scoring pass made to a scenario's reference. Every scoring pass folds what the scored models showed the reference should be (a candidate the judge found better, a rule the samples prove, a value the reference lacked) and writes it into the reference — an edit a pass already made is only replaced by stronger evidence (samples, a wrong verdict, more agreeing models), never by one more model's better verdict; the log is reference_meta.auto_applied on get_benchmark_scenario, each entry with its inverse patch. Pass the revision ids to undo: the inverse is applied, the entry is marked reverted and its (path, attribute) is pinned so no later pass re-applies it (a manual set_benchmark_reference lifts every pin). Scores never go stale from this. Requires owner and a plan with benchmarks. No LLM call.delete_benchmark_scenarioDelete a benchmark scenario and its stored results. Requires owner and a plan with benchmarks. No LLM call; inspect the scenario before deleting it.run_benchmarkStart billed asynchronous execution and scoring of a benchmark. Requires owner, a benchmark-enabled plan and a judge; a verified reference is also required except for sample generation. Supply model_keys or providers; omitting both runs all active models with usable provider keys. Repetitions and judging increase cost. Returns job_id and total_models: poll get_job_status, then read get_benchmark_scenario_results. Re-running replaces each selected model's previous result. An organization's benchmark runs and scoring passes execute one at a time: a launch while another is in flight is queued (queue_position, status 'pending'), and a launch on a scenario whose run is still queued folds its models into that run (merged=true, job_id names the queued run). Reference setup and score interpretation: enricher://docs/model-benchmark.- +46 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"
]
}
}
}
Entity Enricher
{
"mcpServers": {
"entity-enricher": {
"url": "https://entityenricher.ai/api/mcp/"
}
}
}
Frequently asked questions
Codescene MCP Server is categorized under Developer Tools and uses a local stdio subprocess. Entity Enricher belongs to Developer Tools and uses a remote HTTP/SSE transport. Pick Codescene MCP Server for developer tools work and Entity Enricher 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. Entity Enricher 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 entity-enricher 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. Entity Enricher has 57/100 (Good) with 1 views, 0 installs and 0 GitHub stars.