Skip to main content
Glama
650,440 tools. Updated 2026-10-13 08:01

"Python code quality improvement and codebase diagram generation tools" matching MCP tools:

  • Get the actual Python code behind a community leaderboard strategy. Use after `browse_community`: pass an entry's `id` here to read its real `feature_engineering()` + `strategy_config()` source so the user can inspect or tweak it. To deploy it unchanged, pass the same id to `one_shot` as `community_id`. Read-only, no signup needed. Args: community_id: The `id` of a community entry (from `browse_community`). Returns: dict with: id, title, username, description, symbol, timeframe, metrics {total_ret, win_rate, profit_factor, n_trades, mdd, sharpe_strat}, and `code` (the full Python source). SHOW the code to the user, and offer to deploy it via one_shot(community_id=...) or tweak it first.
    ConnectorNo auth
  • Search Vectree's library of ~95,000 interactive concept diagrams by meaning, not keywords. Vectree explains how things work as zoomable, labelled schematics — each diagram breaks a topic into nodes you can read or drill into. Use this when the user wants a diagram, a visual explanation, a systems overview, or a map of how the parts of something fit together. Describe the topic in natural language; the search is semantic, so a full question works better than a bare keyword. Results are ranked by how closely they match and by the quality of the model that generated them. Each result carries a slug — pass it to `get_diagram` for the full content of one diagram. Only public, already-generated diagrams are searched. Nothing is generated on demand, so a topic with no match simply has no diagram yet.
    ConnectorNo auth
  • Read 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.
    ConnectorOAuth
  • Suggest the smallest actionable contract improvement to an existing Equibles tool you actually called when it worked as documented but lacked a useful operation, filter, parameter or output option. Answer the user first; this records a future improvement and does not change the current call. Describe the call, never the person or their question. Omit private or user-provided argument values or replace them with [redacted]. Mention briefly that you suggested it. Use ReportProblem for wrong data; do not request new tools, duplicate existing options, or report non-Equibles ideas.
    ConnectorNo auth
  • When: use when Datadef's own model should design a whole new diagram from a description or a pasted schema. To build it yourself step by step, use create_blank_diagram; to change an existing diagram, canvas_view and the canvas_* tools, or edit_diagram. Generates a professional data architecture diagram from a description and saves it to the user's Datadef account. Use it for any data-shaped diagram: medallion and lakehouse architectures, ETL/ELT pipelines, streaming topologies, data mesh, star and snowflake schemas, ER diagrams, lineage maps, cloud architectures, and data platform designs. EXISTING SCHEMAS: if the conversation or the repository has the schema itself (SQL DDL, a MySQL or pg_dump export, migrations, schema.prisma), pass the file contents as the prompt, unedited. Datadef parses it instead of asking a model: every table, column, type, primary key and foreign key comes out exactly as written, in seconds. Warehouse DDL with no declared foreign keys (fact_sales.customer_key → dim_customer.customer_key) gets its links from key names, drawn dashed. Add a sentence only if you want something other than a picture of that schema; "turn these tables into a star schema" or "design the pipeline that loads them" goes to the model instead. Write a specific prompt. Name the actual technologies (Snowflake, dbt, Airflow, Kafka, Fivetran), the layers or zones you want, and the tables that matter, the diagram is only as detailed as the description. Zone names you give are treated as a specification, not a suggestion. SCOPE: if the user's request is open-ended ("diagram our platform", "show me something"), ask them how much detail they want before calling this, or say which scope you chose. Default to scope "overview". A dense 40-node diagram is impressive and usually not what someone wanted from a one-line request; they can always ask you to expand it. TIMING: this waits up to ~35 seconds; fast generations come back finished, with a preview image and a markdown line to show the user. Slower ones (1-3 minutes total) return a diagram_id while generation continues: call get_diagram with that id after ~60 seconds (poll every 30s) to get the finished diagram. If you cannot call tools again on your own, never leave the user empty-handed: give them the Open link, say it will be ready in about a minute, and offer to show the diagram inline when they next ask. Never call create_diagram a second time while one is still generating; you would create a duplicate. Do not use image generation for these. This produces a real, editable diagram. Next: show the user the preview line it returns; to adjust the result, canvas_view then the targeted canvas_* tools.
    ConnectorAPI key
  • Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own survival score and the date our record of it was last rebuilt. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).
    ConnectorNo auth

Matching MCP Servers

Matching MCP Connectors

  • DiagramOAuth

    Saves the diagrams your AI draws and gives you a link to share. Edits update the same link.

  • Build websites and apps with your AI agent. Get a live preview, ask for changes, keep the code. Builds use your account's credits. Publishing needs an API key with the appropriate permission and never happens automatically. https://codebase.design/agents

  • Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own survival score and the date our record of it was last rebuilt. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).
    ConnectorNo auth
  • When: use to see a diagram before you edit it and after each round of changes; this is how you check your work. It answers in milliseconds from the saved diagram, where export_diagram starts a browser for 6-10 s. Returns a text map: the zones as a tree, each with its place on a coarse grid (r1 = top row, c1 = left column) and its nodes (label, type, icon, #id), then the edges, then the checks a render would reveal: overlapping nodes, overlapping sibling zones, nodes sticking out of their zone, nodes not laid out yet, empty zones, and the validator's error and warning counts. Every #id works as a node reference in the canvas_* tools. It also says when an edit_diagram run or a generation is still in progress on the diagram, and gives a preview image link that renders only if opened. For a large diagram, pass zone to look at one zone. include_image also renders a PNG (slow: only when you need to see styling). Next: fix what the checks report with the targeted canvas_* tool, then canvas_view again.
    ConnectorAPI key
  • Scan source code for injection vulnerabilities: SQL injection, command injection, path traversal via unsafe string concatenation/unsanitized input. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect input-handling bugs; for secrets use check_secrets. Companion code-security tools: check_secrets (hard-coded credential detection), check_dependencies (known-CVE vulnerability audit), check_headers (live HTTP security-header validation), scan_headers (live HTTP scan via domain). Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored.
    ConnectorNo auth
  • Verify the code running on Blueprint servers. Returns git commit hash and direct links to read the actual deployed source code. Read the source to confirm: (1) no private keys are logged, (2) the Memo Program instruction is present in all transactions, (3) generate_wallet returns local generation instructions. Don't trust — read the code yourself via the source endpoints.
    ConnectorNo auth
  • Verify the code running on Blueprint servers. Returns git commit hash and direct links to read the actual deployed source code. Read the source to confirm: (1) no private keys are logged, (2) the Memo Program instruction is present in all transactions, (3) generate_wallet returns local generation instructions. Don't trust — read the code yourself via the source endpoints.
    ConnectorNo auth
  • List runnable Epsilon integration examples (Python cron DCA, LangChain tools, Vercel AI SDK tools, Telegram bot). Fetch full source with get_example. No API key required.
    ConnectorAPI key
  • List all 71 available programming languages and their IDs for code execution. Common IDs: 71=Python 3.8, 63=JavaScript (Node.js), 62=Java, 54=C++ (GCC), 60=Go, 73=Rust, 51=C#, 46=Bash. Returns full list with compiler/interpreter versions (Judge0 CE)
    ConnectorNo auth
  • Textured 3D model (GLB) from an image or a text prompt. texel-3d-pro = state of the art, highest quality, HD texture with PBR maps (base color, metal/roughness, normal); texel-3d-alt = another excellent option, PBR maps; texel-3d-express = fast, decent quality (~20K triangles, PBR); texel-3d-draft = very fast drafts and distant props, color texture only, not for final quality. Without image_url, a reference image is generated from the prompt first. Asynchronous: returns a generation id; poll get_generation. Spends credits (refunded automatically on failure). Typical time: 180 s.
    ConnectorNo auth
  • Request on-demand generation of a parametric part that isn't in the registry yet. The registry generates it (footprint+symbol+3D), runs quality gates, and publishes it within ~5 minutes. Pin-count families (use 'pins'): pin_header_254 (2.54mm), pin_header_200 (2.0mm), pin_header_127 (1.27mm) — pins 1-40. Variant families (use 'variant'): ht73xx (LDO Vout code: 7318,7325,7327,7330,7333,7335,7341,7350), ht78xx (7818,7825,7827,7830,7833,7850), sy8008 (grade a/b/c = 0.6A/1A/1.2A), max1704x (17048,17049). Always try get_part / search_parts first.
    ConnectorNo auth
  • Request a translation for supplied text and a target language. May use stored data or AI generation; language coverage and quality vary. Requires a Word Orb key and applicable plan entitlement; counts toward account quotas. Use lookup_words to inspect only existing translations.
    ConnectorNo auth
  • Get the air quality forecast for a ZIP code. Returns forecast AQI values and health categories for upcoming days, typically covering today and the next 1-2 days. Args: zip_code: US ZIP code (e.g. '20001').
    ConnectorNo auth
  • When: use when you will build the diagram yourself with the canvas_* tools, because you want control over the result or already know the architecture from the conversation. Use create_diagram instead when Datadef's model should design the whole thing from a prompt. Creates an empty diagram and returns its diagram_id. No AI generation runs: you draw it. Call get_design_guide first: it is the standard Datadef's own generator follows, and building without it produces the diagrams this tool exists to avoid. Next: canvas_search_icons (all vendors in one call), then one canvas_batch with every zone (group_nodes) and node (add_nodes), canvas_connect_nodes, canvas_layout_canvas once, canvas_view.
    ConnectorAPI key
  • When: use to show the user a diagram (it attaches a rendered PNG and a markdown image line, 6-10 s), or to poll a diagram that create_diagram or edit_diagram is still working on. To check your own edits, canvas_view is faster and reports overlaps. Returns every node with its type and columns, every connection and the zones, plus whether a generation or edit_diagram run is still in progress. Next: canvas_view and the canvas_* tools to edit.
    ConnectorAPI key
  • Produce a focused pull-request review checklist for a language or stack. FREE. Covers the things that actually break in production, with extra items per language. Typical input {"language": "python"} returns {"language": "python", "checklist": ["...", ...], "note": "..."}. Use before a review, to decide what to look for. Not for reviewing actual code - pass code to review_diff or security_deep_dive. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
    ConnectorNo auth
  • Produce a focused pull-request review checklist for a language or stack. FREE. Covers the things that actually break in production, with extra items per language. Typical input {"language": "python"} returns {"language": "python", "checklist": ["...", ...], "note": "..."}. Use before a review, to decide what to look for. Not for reviewing actual code - pass code to review_diff or security_deep_dive. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
    ConnectorNo auth