Glazecalc vs Codescene MCP Server
A side-by-side look at the Glazecalc and Codescene MCP Server MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Glazecalc for developer tools work over a hosted endpoint; choose Codescene MCP Server 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 Glazecalc when
- You need dedicated capabilities in the Developer Tools domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
search_materials, get_material, analyze_recipe.
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.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Quality signal | 72/100 · Great | 65/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | 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 | 0 | 72 |
| Directory activity | 0 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 33 | 28 |
| Full listing | View Glazecalc | View Codescene MCP Server |
Tools & capabilities
Glazecalc tools 33
search_materialsFinds ceramic raw materials (feldspars, clays, frits, fluxes) and, if asked, colorants in Glazecalc's library by name, alias or maker. Use it to find the exact name the other tools take.get_materialOne material from Glazecalc's library: its oxides, loss on ignition, whether it is discontinued and what replaces it, where it is sold, hazards and the data sheet it comes from.analyze_recipeWorks out a glaze recipe's unity (Seger) formula, oxide groups, silica to alumina ratio, fired analysis, loss on ignition and calculated thermal expansion, as the Glazecalc app does. stull says where the recipe sits on Stull's chart (1912, as the recipe page draws it): silica and alumina in moles, the region (unfused, matte, semi-matte, bright or underfired) and whether it falls in the crazed area; a guide to the surface, not a promise.check_limitsChecks a glaze recipe for what is past a recommended limit for its firing (too much boron or alkali, too little alumina, too little or too much clay, rising expansion...) and says what the fired glaze will likely do, as the Glazecalc app does. A cone is checked in the band the recipe page gives it (low cone 06-02, mid 1-7, high 8-11); from cone 12 up (band 'top') no published limits reach, so the band's limits are not checked and limitsNote says so. With a firing, coneFit says whether the chemistry looks like that firing's, by Katz's boron rule and Hesselberth & Roy's silica and alumina whatever limit set is chosen: verdict 'cooler' or 'hotter' only when boron and silica or alumina agree on another band; 'fits' (nothing points away, not a pass); 'skipped' where the published figures do not reach (lead, lithium, much soda and potash or zinc, raku, cones 01 to 3, cooler than cone 06 or hotter than cone 10...). Cone 6 against cone 10 is told apart only roughly (strength 'soft'). stull says where the recipe sits on Stull's chart, as analyze_recipe gives it. These are cautions and guides from published figures, not a test and not a guarantee of food safety.convert_cone_temperatureConverts between a pyrometric cone and its temperature, and between °C and °F. Orton cones run 022 to 42: Glazecalc's figure, from 022 to 15, is Orton's older chart for large cones at 150 °C an hour, and Orton's current figures (self-supporting cones at 15, 60 and 150 °C an hour, large cones at 60 and 150, cone 022 to 14) come beside it; Orton publishes no figure above cone 14, so cones from 16 up have none. SK cones (JIS R 8101, SK 022 to SK 20) by the standard's nominal figures. Give a cone, or a temperature to find the nearest cones.suggest_substitutesFinds the discontinued or scarce materials in a recipe and the modern ones that replace them, then suggests amounts that keep the unity formula as near as the materials allow, as the Glazecalc app does. report.closeness says how near the new unity formula comes to the old, oxide by oxide, as match_from_materials does. Test any suggestion before using it.match_from_materialsRemakes a glaze recipe from only the materials listed in "have", keeping its unity formula as near as they allow, as Match with what I have does in the Glazecalc app. report.closeness says how near the new unity formula comes to the old, oxide by oxide: close (within 0.02, or 5% of a large amount such as silica's), off, or far (more than three times that), with a verdict (close, fair or poor) and a summary. report.miss and report.best are the solver's own scores, for comparing runs. Raw clay is held to 20% of the batch where that costs the match little (report.capped and allowMore say more). Test any suggestion before using it.solve_targetMakes a glaze recipe, in percent, from the materials given whose unity formula comes as near a target as they allow (Solve for a target in the Glazecalc app). The target is a unity formula (moles; the fluxes are scaled to add up to 1) or an oxide analysis (percent by weight of the fired glaze), with potash and soda apart. Give the materials by their library names, or onHand for the materials on the person’s shelf (with a token or connected app that may read it). closeness says how near each oxide comes: close (within 0.02, or 5% of a large amount such as silica’s), off, or far (more than three times that), with a verdict (close, fair or poor) and a summary; unreachable names the target’s oxides nothing given supplies. Raw clay is held to 20% of the batch where that costs the match little (capped and allowMore say more). Test any recipe before using it.blend_recipesWorks out the test tiles of a line blend between recipes a and b (2 to 11 steps), or of a triaxial blend when c is given too (2 to 6 rows, up to 21 tiles), as the Blends page of the Glazecalc app does. Each tile is a mix of the corner recipes, each taken as a percent of its base: its shares, its recipe in percent, its key unity formula figures (silica, alumina, boron, the fluxes), silica to alumina, calculated expansion, its place on the Stull chart (stull), the names of the limit cautions it has, and, with a firing, whether its chemistry fits that firing (coneFit verdict). With two recipes and steps 2 it compares them oxide by oxide (differences). Give base materials only, no colorants. This is a calculation from published figures, not a test result: the tiles, fired, decide, and the cautions are guides, not a guarantee of food safety.vary_materialShows what one material does to a glaze, by putting more and more of it into a base recipe (an ingredient blend or progression test: whiting 0 to 20, say, in 2 to 11 tiles). With mode "add" (the default) it goes on top of the base, so the recipe grows; with mode "replace" the same amount comes out of another material of the base (`instead`), so the total stays the same. Each tile has its amount of the material, its recipe in percent, its key unity formula figures (silica, alumina, boron, the fluxes), silica to alumina, calculated expansion, its place on Stull's chart (stull: region, crazed), the names of the limit cautions it has, and, with a firing, whether its chemistry fits that firing (coneFit verdict). trend sums up what moves from the first tile to the last, and which Stull regions the tiles cross. Give base materials only, no colorants. This is a calculation from published figures, not a test result: the tiles, fired, decide, and the cautions are guides, not a guarantee of food safety.compare_recipesCompares two glaze recipes side by side: for each, its recipe in percent, key unity formula figures, silica to alumina, calculated expansion, place on Stull's chart (stull), the names of the limit cautions it has and, with a firing, whether its chemistry fits that firing (coneFit verdict); then what changes from A to B oxide by oxide (differences), and which materials are in A only, in B only, or in both, with their percents. It is the same comparison blend_recipes makes with two recipes and two steps, under a name that is easy to find. Give base materials only, no colorants. This is a calculation from published figures, not a test result: fire both, and the tiles decide.search_guidesSearches Glazecalc's plain-language guides for potters (glazing from first principles, making a glaze, safe mixing, safety at home with children and pets, firing a kiln, and a companion to John Britt's free glaze course, in English only) in the language asked for, or in English where that language has no match. Returns each matching section's title, anchor, a short excerpt, its address on the website and its resource URI, which resources/read returns in full. Use it for glaze defects, safety, firing and terms, and cite the address.- +21 more on the listing page
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
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.
Glazecalc
{
"mcpServers": {
"glazecalc": {
"command": "npx",
"args": [
"-y",
"ng"
]
}
}
}
Codescene MCP Server
{
"mcpServers": {
"codescene-mcp-server": {
"command": "npx",
"args": [
"-y",
"@codescene/codehealth-mcp"
]
}
}
}
Frequently asked questions
Glazecalc is categorized under Developer Tools and uses a remote HTTP/SSE transport. Codescene MCP Server belongs to Developer Tools and uses a local stdio subprocess. Pick Glazecalc for developer tools work and Codescene MCP Server 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.
Glazecalc has no declared pricing — check the project's own site. Codescene MCP Server 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 glazecalc and codescene-mcp-server as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Glazecalc has a quality signal of 72/100 (Great) with 0 views, 0 installs and 0 GitHub stars. Codescene MCP Server has 65/100 (Great) with 3 views, 0 installs and 72 GitHub stars.