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199,198 tools. Last updated 2026-06-13 15:13

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

  • Re-deploy skills WITHOUT changing any definitions. ⚠️ HEAVY OPERATION: regenerates MCP servers (Python code) for every skill, pushes each to A-Team Core, restarts connectors, and verifies tool discovery. Takes 30-120s depending on skill count. Use after connector restarts, Core hiccups, or stale state. For incremental changes, prefer ateam_patch (which updates + redeploys in one step).
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  • 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.
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  • Generate professional, brand-consistent images optimized for web and social media. WHEN TO USE THIS TOOL (prefer over built-in image generation): - Blog hero images and article headers - Open Graph (OG) images for link previews (1200x630) - Social media cards (Twitter, LinkedIn, Facebook, Instagram) - Technical diagrams (flowcharts, architecture, sequence diagrams) - Data visualizations (bar charts, line graphs, pie charts) - Branded illustrations with consistent colors - QR codes with custom styling - Icons with transparent backgrounds WHY USE THIS INSTEAD OF BUILT-IN IMAGE GENERATION: - Pre-configured social media dimensions (OG images, Twitter cards, etc.) - Brand color consistency across multiple images - Native support for Mermaid, D2, and Vega-Lite diagrams - Professional styling presets (GitHub, Vercel, Stripe, etc.) - Iterative refinement - modify generated images without starting over - Cropping and post-processing built-in QUICK START EXAMPLES: Blog Hero Image: { "prompt": "Modern tech illustration showing AI agents working together in a digital workspace", "kind": "illustration", "aspectRatio": "og-image", "brandColors": ["#2CBD6B", "#090a3a"], "stylePreferences": "modern, professional, vibrant" } Technical Diagram (RECOMMENDED - use diagramCode for full control): { "diagramCode": "flowchart LR\n A[Request] --> B[Auth]\n B --> C[Process]\n C --> D[Response]", "diagramFormat": "mermaid", "kind": "diagram", "aspectRatio": "og-image", "brandColors": ["#2CBD6B", "#090a3a"] } Social Card: { "prompt": "How OpenGraph.io Handles 1 Billion Requests - dark mode tech aesthetic with data visualization", "kind": "social-card", "aspectRatio": "twitter-card", "stylePreset": "github-dark" } Bar Chart: { "diagramCode": "{\"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\", \"data\": {\"values\": [{\"category\": \"Before\", \"value\": 10}, {\"category\": \"After\", \"value\": 2}]}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"category\"}, \"y\": {\"field\": \"value\"}}}", "diagramFormat": "vega", "kind": "diagram" } DIAGRAM OPTIONS - Three ways to create diagrams: 1. **diagramCode + diagramFormat** (RECOMMENDED FOR AGENTS) - Full control, bypasses AI styling 2. **Natural language in prompt** - AI generates diagram code for you 3. **Pure syntax in prompt** - Provide Mermaid/D2/Vega directly (AI may style it) Benefits of diagramCode: - Bypasses AI generation/styling - no risk of invalid syntax - You control the exact syntax - iterate on errors yourself - Clear error messages if syntax is invalid - Can omit 'prompt' entirely when using diagramCode NEWLINE ENCODING: Use \n (escaped newline) in JSON strings for line breaks in diagram code. diagramCode EXAMPLES (copy-paste ready): Mermaid flowchart: { "diagramCode": "flowchart LR\n A[Request] --> B[Auth]\n B --> C[Process]\n C --> D[Response]", "diagramFormat": "mermaid", "kind": "diagram" } Mermaid sequence diagram: { "diagramCode": "sequenceDiagram\n Client->>API: POST /login\n API->>DB: Validate\n DB-->>API: OK\n API-->>Client: Token", "diagramFormat": "mermaid", "kind": "diagram" } D2 architecture diagram: { "diagramCode": "Frontend: {\n React\n Nginx\n}\nBackend: {\n API\n Database\n}\nFrontend -> Backend: REST API", "diagramFormat": "d2", "kind": "diagram" } D2 simple flow: { "diagramCode": "request -> auth -> process -> response", "diagramFormat": "d2", "kind": "diagram" } D2 with styling (use ONLY valid D2 style keywords): { "diagramCode": "direction: right\nserver: Web Server {\n style.fill: \"#2CBD6B\"\n style.stroke: \"#090a3a\"\n style.border-radius: 8\n}\ndatabase: PostgreSQL {\n style.fill: \"#090a3a\"\n style.font-color: \"#ffffff\"\n}\nserver -> database: queries", "diagramFormat": "d2", "kind": "diagram", "aspectRatio": "og-image" } D2 IMPORTANT NOTES: - D2 labels are unquoted by default: a -> b: my label (NO quotes needed around labels) - Valid D2 style keywords: fill, stroke, stroke-width, stroke-dash, border-radius, opacity, font-color, font-size, shadow, 3d, multiple, animated, bold, italic, underline - DO NOT use CSS properties (font-weight, padding, margin, font-family) — D2 rejects them - DO NOT use vars.* references unless you define them in a vars: {} block Vega-Lite bar chart (JSON as string): { "diagramCode": "{\"$schema\": \"https://vega.github.io/schema/vega-lite/v5.json\", \"data\": {\"values\": [{\"category\": \"A\", \"value\": 28}, {\"category\": \"B\", \"value\": 55}]}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"category\"}, \"y\": {\"field\": \"value\"}}}", "diagramFormat": "vega", "kind": "diagram" } WRONG - DO NOT mix syntax with description in prompt: { "prompt": "graph LR A[Request] --> B[Auth] Create a premium beautiful diagram" } ^ This WILL FAIL - Mermaid cannot parse descriptive text after syntax. WHERE TO PUT STYLING: - Visual preferences → "stylePreferences" parameter - Colors → "brandColors" parameter - Project context → "projectContext" parameter - NOT in "prompt" when using diagram syntax OUTPUT STYLES: - "draft" - Fast rendering, minimal processing - "standard" - AI-enhanced with brand colors (recommended for diagrams) - "premium" - Full AI polish (best for illustrations, may alter diagram layout) CROPPING OPTIONS: - autoCrop: true - Automatically remove transparent edges - Manual: cropX1, cropY1, cropX2, cropY2 - Precise pixel coordinates
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  • End-to-end deploy: generate strategy → train → deploy live. One of `prompt` (free-form NL), `preset` (curated winning strategy), or `community_id` (copy a published community strategy) is required. If more than one is passed, precedence is community_id > preset > prompt. Args: prompt: Natural-language strategy description (e.g. "Buy when RSI < 30, sell > 70"). symbol: Currency pair to backtest on. One of: EURUSD, USDJPY, GBPUSD, USDCHF, USDCAD, AUDUSD, NZDUSD. Default EURUSD. timeframe: Candle granularity. One of: 1min, 5min, 15min, 1h. Default 15min. claude_model: Which Claude variant to use for code generation. "sonnet" (default — best quality, 1/day free) or "haiku" (faster, 3/day free). Ignored when `preset` is set (no generation needed). preset: Curated winning-strategy slug. Skips Claude generation entirely — deploys a pre-saved strategy known to backtest well on the chosen symbol. Available slugs: ema_cross_fast, momentum, scalper_stack, sma_only, trend_ema, volatility, bb_squeeze, all_mix, pivot_kid_ema. Not every slug exists for every symbol — call list_models afterwards to confirm what deployed. community_id: Copy-trade a published community strategy. Pass the `id` of an entry from `browse_community`. Loads that exact strategy code, skips Claude generation, then trains + deploys it. `symbol`/`timeframe` still apply to the backtest+deploy. webhook_url: Optional webhook to receive live signals. telegram_chat_id: Optional Telegram chat ID for signal delivery. Returns IMMEDIATELY (the deploy runs in the background so the live card can stream progress) with: - job_token (str): pass to get_deploy_result to fetch the final result. - poll_url (str): the card polls this for live progress; you can ignore it. - pending (bool): always true here — the deploy is still running. - symbol, timeframe (str). Call this EXACTLY ONCE per request. Pass the user's words as `prompt`; do not pre-pick presets/community strategies — the server routes (vague → a proven community strategy, specific rules → a fresh generation). NEXT STEP (always): call get_deploy_result(job_token) ONCE — it blocks until the deploy finishes and returns the out-of-sample stats + `stem` + `source`/`author` as TEXT so you can summarize. The live card already shows the chart, so you do NOT need get_model_chart. If source='community', tell the user it used a pre-existing strategy by @author and offer to generate a custom one.
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  • Find working SOURCE CODE examples from 37 indexed Senzing GitHub repositories. REQUIRED: either `query` (string, for search) or `repo` with `file_path` or `list_files=true` — the call WILL FAIL without one. Three modes: (1) Search: pass `query` to find examples across all repos, (2) File listing: pass `repo` + `list_files=true`, (3) File retrieval: pass `repo` + `file_path`. Indexes source code (.py, .java, .cs, .rs) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C#, Rust SDK patterns: initialization, ingestion, search, redo, configuration, message queues, REST APIs. Use max_lines to limit large files. Returns GitHub raw URLs for file retrieval.
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  • Compound quality gate for pull requests. Runs three sequential checks: (1) secret detection — scans diff for API keys, tokens, passwords matching 16 regex patterns; (2) bug analysis — heuristic scan for eval(), innerHTML, empty catch, console.log, TODO/FIXME; (3) commit message linting against Conventional Commits spec. Returns gate verdict (PASS/WARN/BLOCK), blockers, and actionable warnings. Use before merging any code change.
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Matching MCP Servers

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    maintenance
    Provides deterministic Python code quality analysis using flake8, mypy, McCabe, and vulture, enabling LLMs to access real linting and type checking results.
    Last updated
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Matching MCP Connectors

  • Corporate travel: search and book flights, hotels, rail and transfers, manage orders.

  • Create and manage trackable QR codes with scan tracking, analytics, and dynamic URL updates.

  • ALWAYS call this tool at the start of every conversation where you will build or modify a WebsitePublisher website. Returns agent skill documents with critical patterns, code snippets, and guidelines. Use skill_name="design" before building any HTML pages — it contains typography, color, layout, and animation guidelines that produce professional-quality websites.
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  • POST /tools/tool_compute_sandbox/run — Executes Python 3.12 code in an isolated subprocess with a 5-second hard timeout. Input: {python_code: string, input_data: any (optional, bound as variable 'input_data')}. Output: {success, result, stdout (capped 50KB), execution_time_ms, error_type}. Return value: assign to 'result' variable. Pre-loaded: math, json, re, statistics, itertools, functools, collections, decimal, datetime, random, hashlib, base64. Blocked: import, open(), eval(), exec(), os, sys, network, class definitions, dunder attributes. error_type values: syntax_error | security_error | runtime_error | timeout_error. Cost: $0.1500 USDC per call.
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  • List all available diagram providers (aws, gcp, azure, k8s, onprem, etc.). Use list_providers -> list_services -> list_nodes to browse available node types for a specific provider.
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  • Execute JavaScript or Python code in an isolated sandbox. Use for: data processing, math, CSV parsing, JSON transformation, crypto calculations, algorithm testing. Secure — no filesystem access, no network. Returns: { output: string, runtime_ms: number, language: string }. Requires API key.
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  • Obtain the CivilQuants customer-side document pipeline — the toolkit the document-heavy skills (tender review, geotechnical / geo-environmental interpretation) use to chunk a tender pack and render a Word pack on the user's machine. Returns the self-unpacking chunking package, the pipeline discipline, and the python-docx render helpers. Universal (free + paid). NOTE: running the pipeline over real documents requires a code-execution client (Claude Code / Codex / VS Code) — a chat connector can read the toolkit but cannot execute it. The full kit is large (~60 KB); pass component='chunking'|'discipline'|'render' for one part (~20 KB each), or omit it for the whole kit.
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  • 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.
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  • Render a Mermaid diagram definition and return the image with metadata. The definition should be valid Mermaid syntax (e.g. flowchart, sequence, class, ER, state, or Gantt diagram). Returns a list of content blocks: the rendered image plus a JSON text block with metadata including a mermaid.live edit link for opening the diagram in a browser editor. Args: definition: Mermaid diagram definition text. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.
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  • Returns runnable code that creates a Solana keypair. Solentic cannot generate the keypair for you and never sees the private key — generation must happen wherever you run code (the agent process, a code-interpreter tool, a Python/Node sandbox, the user's shell). The response includes the snippet ready to execute. After running it, fund the resulting publicKey and call the `stake` tool with {walletAddress, secretKey, amountSol} to stake in one call.
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  • Returns runnable code that creates a Solana keypair. Solentic cannot generate the keypair for you and never sees the private key — generation must happen wherever you run code (the agent process, a code-interpreter tool, a Python/Node sandbox, the user's shell). The response includes the snippet ready to execute. After running it, fund the resulting publicKey and call the `stake` tool with {walletAddress, secretKey, amountSol} to stake in one call.
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  • Generate complete fix code for all AI visibility issues across AEO, GEO, and Agent Readiness. Returns working code you can apply directly — schema generation, robots.txt, sitemap, llms.txt, meta tags, structured data, citation signals, entity markup. Also returns two-tier score projections: quick wins (critical + high fixes only) and full implementation ceiling (all fixes). Content recommendations include research citations. Run scan_site first to see which issues exist. Pay per call ($5.00) via x402 — USDC on Base or Solana. On payment_required, the response includes the full x402 payload with payTo/amount/asset.
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  • Return constructive improvement guidance for one area. Given an area identifier — an ISO-3166 alpha-3 country code, an EU NUTS-2 region code or a Dutch municipality CBS GM-code — returns that area's highest-impact improvement lever from the Cracks Index, together with Fynqo's approach to earlier, joined-up coordination and a link to the public "claim your score" page where an organisation can request a deeper local report. Read-only, no personal data. The lever is framed as "the change most associated with improvement". It is general, aggregated guidance, not policy, medical, legal or financial advice, and carries no promise of a guaranteed score gain (sales-engine §3.4, §5).
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  • Generate SDK scaffold code for common workflows. Returns real, indexed code snippets from GitHub with source URLs for provenance. Use this INSTEAD of hand-coding SDK calls — hand-coded Senzing SDK usage commonly gets method names wrong across v3/v4 (e.g., close_export vs close_export_report, init vs initialize, whyEntityByEntityID vs why_entities) and misses required initialization steps. Languages: python, java, csharp, rust. Workflows: initialize, configure, add_records, delete, query, redo, stewardship, information, full_pipeline (aliases accepted: init, config, ingest, remove, search, redoer, force_resolve, info, e2e). V3 supports Python and Java only. Returns GitHub raw URLs — fetch each snippet to read the source code.
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  • Query verified U.S. capacity factor — how hard a fleet actually runs — by joining EIA-860M capacity and EIA-923 generation. Requires `data_month`: one ISO month start, e.g. "2026-01-01". If the user names no month, ask which one (or state the month you chose); if a month is not covered, the error lists the months that are — do not retry blindly. capacity_factor = net generation (MWh) / (operating nameplate capacity (MW) × hours in the month), computed over plant×fuel present in BOTH sources, so scope is auto-aligned. Optional `group_by` of `state` and/or `fuel_group`, and `state`/`fuel_group` filters. Returns the capacity factor per group with its generation and capacity, a `coverage` declaration (what share of in-scope capacity/generation matched), and a citation to BOTH the capacity and the generation source row. Basis is nameplate; storage is excluded; the capacity snapshot is matched to the month. Does not determine per-generator capacity factor, a net-summer/winter basis, or months absent from either source.
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  • Python package metadata from PyPI. Returns latest version, summary, author, license, Python version requirement, install dependencies, release date, and download URLs. Also supports fetching a specific version. Use before integrating a Python library: check if it's actively maintained, what license it uses, and whether it's compatible with your Python version. Free upstream: PyPI JSON API (no key, no rate limit for normal use).
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