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521,397 tools. Updated 2026-09-06 11:20

"Python" matching MCP tools:

  • Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.
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  • An outside check on code, executed in a sealed sandbox. Call it before code crosses a consequence boundary: before you merge it, deploy it, publish it, settle a payout on it, or report it done. A self-audit verifies consistency, never completeness: a check written inside the frame that produced the code passes on the code's own assumptions. This is the check that is not you. Also call it when a fix passes your own check but the target still fails; that means your check shares the code's assumption and cannot see the error. INPUT: code (JavaScript/Node or Python 3 source, deterministic only) plus ONE of: contract {fn, examples:[{call,expected}]} (copy call and expected from the test or spec the consequence depends on), or assumption (plain-language claim, weaker read). It checks the code against the contract exactly as given. VERDICTS (synchronous): BROKE: the code violates your contract, with the exact input and a rerunnable proof; do not proceed. HELD: the code meets the contract you gave; proceed on that contract, and nothing more. FINDINGS: a stated property strains under a generated input; check it before proceeding. DROP: not deterministically checkable. PAYMENT: 0.10 USDC per call, x402 v2 on Base, no account. Every delivered verdict is charged, HELD and DROP included. If no verdict is produced, the payment authorization is cancelled and you are not charged.
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  • Deploy a GitHub repository as a live web app on Dockhold. Call this when the user wants to put an app online, get a shareable HTTPS URL, or host a demo. Returns the new app id. Two paths: a PUBLIC repo needs only repo_url; a PRIVATE repo needs repo_url plus github_installation_id (call list_github_repos first, each repo comes with the installation_id to pass here). Deploying a private repo turns on auto-deploy: future pushes to that repo redeploy the app automatically. The app builds and comes online automatically; poll get_app_status to watch it. Set memory_mb to size the app's compute, one of the values get_resource_usage reports under compute.steps_mb: 256 MB fits a static site or a small API, 512 MB fits a typical Node or Python web app, and anything that holds data in memory needs more. Omit it and the app gets the minimum slice (256 MB) so it doesn't take your whole compute pool; resize_app changes the size later with no rebuild, applied as a rolling update that replaces the app's instances. The response reports memory_mb (what this app got) and compute_available_mb (what's left in your pool), so size the next app off that. This tool needs a GitHub repo URL: if the code only exists locally (no repo), it cannot be used here, and the user should run `npx dockhold login` then `npx dockhold deploy` in the project folder instead. Requires a token with the deploy scope.
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  • Perform a software package vulnerability audit using SecDB. ## What this tool does Analyzes a list of software packages identified by PURL (Package URL) and returns vulnerability information plus a Markdown summary. The audit results are based exclusively on the package list provided. ## When to use this tool Use this tool when the user wants to determine: - whether application dependencies contain known vulnerabilities - whether a project is affected by security advisories - which packages require patching or upgrading ## Supported ecosystems - **npm** - Node.js packages (e.g. pkg:npm/lodash@4.17.21) - **maven** - Java/JVM packages (e.g. pkg:maven/org.apache.logging.log4j/log4j-core@2.14.1) - **pypi** - Python packages (e.g. pkg:pypi/django@4.2.0) - **gem** - Ruby gems (e.g. pkg:gem/rails@7.0.0) - **cargo** - Rust crates (e.g. pkg:cargo/openssl-src@111.10) - **nuget** - .NET packages (e.g. pkg:nuget/Newtonsoft.Json@13.0.1) - **golang** - Go modules (e.g. pkg:golang/github.com/gin-gonic/gin@1.9.1) - **composer** - PHP packages (e.g. pkg:composer/symfony/symfony@6.4.0) ## Inputs - **purls**: list of Package URLs, one per entry. Generate them from your project manifest files: - Node.js: package.json / package-lock.json - Python: requirements.txt / Pipfile.lock / pyproject.toml - Ruby: Gemfile.lock - Go: go.mod / go.sum - Rust: Cargo.lock - PHP: composer.lock - Java: pom.xml / build.gradle - .NET: *.csproj / packages.lock.json ## Outputs - **report**: structured JSON objects describing the advisories affecting the audited packages. - **summary**: Markdown summary including total vulnerabilities, severity breakdown, and key findings. ## LLM usage guidelines - Never guess whether a package is vulnerable — always call this tool. - Only submit PURLs from the supported ecosystems listed above; others will be ignored. - The `summary` is already Markdown and can be shown directly. - Use `report` when deeper technical analysis is required.
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  • Search the RoxyAPI knowledge base and get back ranked documentation snippets, each with a source URL. It covers API endpoints with their request and response fields, SDK usage for TypeScript, Python, PHP, C#, and the WordPress plugin, authentication and API keys, UI components, and step by step integration guides. Call this first whenever you need to integrate RoxyAPI into an app: to find which endpoint or SDK method to use, what parameters a call takes, how to authenticate, or how to wire a feature end to end. Pass the user question verbatim as `query`. If the first results miss, rephrase once and retry.
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  • Submit an uploaded PDF for faxing. Step 1 (before this tool): upload the PDF over plain HTTP multipart, using any HTTP client you have — shell, JavaScript fetch with FormData, Python, etc.: curl -F "file=@document.pdf" https://www.sendthisfax.com/api/upload fetch("https://www.sendthisfax.com/api/upload", {method: "POST", body: formDataWithFile}) The response contains fax_public_id and page_count. PDFs must be unencrypted, at most 50 MB and 1000 pages. Step 2: call this tool with the fax_public_id and the recipient fax number. Two modes: - With an API key (Authorization: Bearer stf_live_... on this MCP connection): the fax price is debited from the prepaid credit balance and sending starts immediately — no checkout, no browser. sender_email and billing_country are optional (they default to the key's records). Buy credits at https://www.sendthisfax.com/en/credits. - Without an API key: sender_email and billing_country are REQUIRED and the tool returns a checkout_url the USER must pay in a browser; the fax is sent automatically once paid. In both modes, poll get_fax_status until status reaches "delivered" or "failed" (failures after payment are auto-refunded). For integration testing, +19898989898 is the designated test recipient number.
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Matching MCP Servers

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    This MCP server provides direct access to ruff linting, formatting checks, and ty type-checking for Python projects, with token-efficient, structured output.
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Matching MCP Connectors

  • Proves AI-generated Python does what you asked: lint, types, security, sandbox run, exact fixes.

  • Security + bug + perf + refactor audit for Python. Returns 0-10 score + MD report.

  • Everything validation does, plus deterministic fixes: the corrected source comes back in fixed_code, and the original is kept whenever the fix cannot be proven safe. The code is still never run. Use it when validation failed and you want the fix rather than the diagnosis. Alternatives: validate_python when the diagnosis is enough; execute_python when the fix has to be proven to run. Auth: a key is required. This call needs a paid key and answers HTTP 402 without one. Credits are bought without an account, 3 per call: GET /v1/pricing says where to send the xDAI. Or pay for this one call with no key at all: call it without one and the result carries x402 payment requirements ($0.03 in USD Coin on eip155:8453); sign them and repeat the call with the payment in _meta['x402/payment']. Arguments: code: the whole file, 1..200000 bytes of UTF-8 measured after encoding (empty is refused with 400, larger with 413); a fragment is fine, but line and column numbers in the answer count from 1 in what you sent. language: must be 'python'; anything else is 400, and the field may be omitted. options.max_iterations (1..10, default 3) caps the fix/verify rounds: raise it for a file with several independent faults, leave it for a snippet. options.optimize (default false) additionally folds constants and drops dead code, and is only worth setting when you asked for a rewrite anyway. options.transpile_to (e.g. 'javascript') returns a translation of the *repaired* source in transpiled, not of what you sent. fixed_code is null when nothing could be proven safe to change, so treat null as 'no fix', not as an error. options.timeout_s, options.examples and options.expected_output do nothing here: nothing is run, so there is no clock, no stdout, and no way to check an example. Returns valid, score 0..1, diagnostics (rule, message, line, column), security findings, fixes, fixed_code and runtime; see outputSchema. The code and its verdict are retained to improve the service.
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  • Monte Carlo Schedule Risk Analysis — P10/P50/P80/P90 completion-date forecast for a Primavera P6 schedule. Implements an AACE-style quantitative SRA (the same math as CPP's browser Tool_11 Portfolio Risk Engine, scripted Python counterpart). For each iteration, every activity duration is sampled from the chosen distribution (Triangular, BetaPERT, Uniform, Lognormal, etc.) parameterized by % of baseline duration; CPM re-runs and the project finish date is recorded. After all iterations, P10/P50/P80/P90 completion dates and a sensitivity tornado (per-activity correlation to project finish) are reported. Use this tool when you need probabilistic completion forecasts or a tornado/sensitivity ranking. For the QRAMM-aligned five-level maturity badge (AACE 122R-22) on the result, pipe the response into ``qramm_maturity``. Args: xer_path: server-side path to the schedule XER. xer_content: full text of the schedule XER (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. iterations: number of MC iterations (default 5000). distribution: 'Triangular', 'BetaPERT', 'Uniform', 'Lognormal' (case-insensitive — passed through). optimistic_pct, most_likely_pct, pessimistic_pct: % of baseline duration for the distribution params (defaults: 85 / 100 / 120). seed: optional fixed seed for reproducibility (0 = system entropy = non-reproducible). output_dir: optional output dir; tempdir if "". Returns: Full SRA result dict, key paths: - 'baseline.percentiles': lowercase p-keys {'p10','p25','p50','p75','p80','p85','p90','p95'}, each {'day', 'date'}. NOTE: keys are lowercase — read result['baseline']['percentiles']['p80'], not 'P80'. - 'baseline.config': sim params used - 'baseline.sensitivity': per-activity tornado rows - 'risk_register_simulation.percentiles' (only when a risk_register is supplied): SAME lowercase convention, {'p10','p50','p80','p90'} each {'day', 'date'}. - 'project_name', 'data_date', ... - HTML / DOCX paths if outputs emitted
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  • Draws N unique random cards from the 78-card deck using cryptographic randomness (Python secrets.SystemRandom). Every call is independent — there is no session state. WORKFLOW: BEFORE: None — standalone. AFTER: None — interpret drawn cards using their active_meaning and active_keywords fields. INPUT CONTRACT: count (int 1–78, default 1) — Number of unique cards to draw. Example: 1 (daily pull), 3 (simple reading), 10 (Celtic Cross), 78 (full deck shuffle). Values outside 1–78 are rejected locally with MCP INVALID_PARAMS. allow_reversed (bool, default false) — When true, each drawn card independently has a 50% chance of reversal (cryptographically random, not seeded). DO NOT CONFUSE WITH: asterwise_get_tarot_card_of_the_day — deterministic daily card, same for all callers. asterwise_get_tarot_three_card_spread — positional read with named positions and meanings. asterwise_get_tarot_celtic_cross — 10-card positional spread. Full output and error contract: https://docs.asterwise.com/mcp/tools/draw-tarot-cards/
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  • Exact name lookup — returns the first thought matching the name exactly. Requires npub for credit billing. ⚠️ NOT AUTHORITATIVE. Backed by the vendor's name index, which is known to be incomplete on large brains (upstream: TheBrainTech/thebrain-api-quickstart-python#1): a hit is real, but a MISS is NOT proof the thought is absent. Never conclude a thought does not exist from a null result here — verify by ID with get_thought, or by graph traversal from a known neighbour, before creating a duplicate.
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  • Full-text search across thought names and content. Requires npub for credit billing. ⚠️ NOT AUTHORITATIVE. Backed by the vendor's search index, which is incomplete on large brains (upstream: TheBrainTech/thebrain-api-quickstart-python#1) — it returns empty for the majority of thoughts that provably exist. A hit is real; an empty result is NOT proof of absence. Use for discovery of older/established thoughts, not as an existence check — verify by ID with get_thought before acting on "not found".
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  • Bulk-export a buyer's licensed catalog via GET /enterprise-license?format=ndjson (Phase 11 M3). Returns up to 1000 articles per call (collected from line-delimited JSON wire format). Same per-scope content contract as list_feed: METERED (filtered-scope) keys export metadata only (content_body null, content_access 'metered_per_call') — use get_content for article text. Each article emits one usage_records row (analytics-only sentinel 'bulk-export:<request_id>:<article_id>' — not metered-billable per the revenue-model bifurcation invariant). Use `since` (ISO 8601) for delta-feed. Use `cursor` to paginate beyond 1000. Backend supports 5000 articles per call; the MCP cap is 1000 for transport reasonability. Real bulk-ingest pipelines should use the Python SDK (pip install opedd) directly — not via MCP. Requires OPEDD_ACCESS_KEY (ent_*).
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  • Read-only queries on the open spreadsheet. No data is modified. Safe to auto-approve. Call as {"action": "<name>", "params": {...}} — per-action params are listed in the Action Reference below. Special actions (not shown in the action enum): • batch — {"action": "batch", "params": {"actions": [{"action": "<name>", "params": {...}}, ...]}}. Runs reads in parallel; individual failures are reported per-entry without short-circuiting. • context — {"action": "context", "params": {"topic": "<name>"}} or {"action": "context", "params": {"action": "<name>"}}. Returns deeper docs for a topic or a single action's signature. Plural "topics" / "actions" arrays are also accepted and may be combined. Topics: python, javascript, formula, connection, validation, a1, quadratic, chart, pivot_table. Action Reference • get_cell_data(selection, page?, sheet_name?) — Returns cell values for a selection in A1 notation. Supports comma-separated ranges to fetch multiple areas in ONE call, including across different sheets. Examples: "A1:B10, D1:E10", "TableName, OtherTable", "'Sheet1'!A1:B10, 'Sheet2'!C1:D10". Table names are globally unique so they work without sheet prefixes. For cell ranges on other sheets use 'SheetName'!Range. Only use when you need the full dataset (aggregations, lookups, analysis). The file summary already includes sample rows. Results may be paginated — use page (0-based) for additional pages. • has_cell_data(selection, sheet_name?) — Check if any cells in a selection have data. Returns true if ANY cell contains data. Use before creating/moving tables or code to avoid spill errors. All ranges MUST be on the same sheet. • get_code_cell_value(code_cell_position?, code_cell_name?, sheet_name?) — Get full code from an existing Python, JavaScript, or connection code cell. Do NOT use for formula cells — formulas are already in get_cell_data results and the file summary. • get_text_formats(selection, page?, sheet_name?) — Get text formatting info. Use table column references for tables ("Table_Name[Column Name]"). Results may be paginated. • get_validations(sheet_name?) — Get all validations in a sheet. • get_conditional_formats(sheet_name) — Get all conditional formatting rules. Use to check existing rules before creating/updating/deleting. • text_search(query, case_sensitive?, whole_cell?, search_code?, regex?, sheet_name?) — Search for text in cell outputs. Supports regex when enabled (e.g., "\d+", "^hello", "foo|bar"). Searches cell outputs only, not code. Booleans default false. • get_sheet_info() — List all sheets and names. • get_spreadsheet_context(sheet_name?, include_errors?) — Full context snapshot of the file. • read_data(selection, sheet_name?, max_rows?) — Read cell data as compact CSV. Auto-tiers: returns all rows for small/medium data (<5000 rows), head+tail preview for large data. Preferred over get_cell_data for most reads. • outline(sheet_name?) — Structural map of the file: sheets, bounds, tables, code cells, charts, connections, errors. Use to understand file layout before reading data. • dependencies(position, sheet_name?, direction?) — Trace cell dependencies. direction: "forward" (what this cell reads), "reverse" (what depends on this cell), or "both" (default). • export_pdf(options?) — Export the file as a PDF with Excel-parity print semantics. Returns {mime_type, size_bytes, data_base64}. options is a camelCase object: {sheetIds?: [id], fileName?, pageSetup?: {paperSize ("letter"|"legal"|"tabloid"|"a3"|"a4"|"a5"|...), orientation ("portrait"|"landscape"), margins {left,right,top,bottom,header,footer} (inches), scaling ({type:"zoom",percent} or {type:"fitTo",width?,height?}), pageOrder ("downThenOver"|"overThenDown"), centerHorizontally?, centerVertically?, printGridlines?, printHeadings?, header/footer {odd:{left,center,right}, even?, first?} with Excel codes (&P page, &N total, &D date, &T time, &F file, &A sheet, &B bold)}, sheetOptions?: {"<sheetId>": {pageSetup?, printArea? ("A1:F20"), repeatRows? ([1,2]), repeatCols?, rowBreaks?, colBreaks?}}}. Omit options for sensible defaults (letter portrait, 100% zoom, all sheets). • list_connections(team_uuid?) — List all database connections in a team (PostgreSQL, MySQL, MS SQL, Snowflake, BigQuery, Mixpanel, Google Analytics, Plaid, etc.). Returns each connection's uuid, name, and type. team_uuid is optional — if omitted, the user's only team is used; multi-team users must pass it. Call this BEFORE get_database_schemas or set_sql_code_cell_value to discover the connection_ids and connection types you need. • get_database_schemas(connection_ids, connection_type, team_uuid) — Get table/column schemas for database connections. Always call before writing SQL. Get connection_ids from list_connections. connection_type: POSTGRES, MYSQL, MSSQL, SNOWFLAKE, BIGQUERY, COCKROACHDB, etc. • list_agent_connections(team_uuid?) — List the team's ready Agent Connections (third-party REST API bindings). Returns each connection's uuid, name, service, base URL, auth pattern, and `{{SECRET_NAME}}` references to use in fetch code. team_uuid is optional — if omitted, the user's only team is used; multi-team users must pass it. Reference secrets via `{{SECRET_NAME}}` in Python/JavaScript fetch code; the connection proxy substitutes team secret values at request time. • inspect_agent_connection(connection_id, team_uuid?) — Get the full schema (resources, endpoints, fields, docs URLs) and plan for one ready Agent Connection by uuid (from list_agent_connections). Call BEFORE writing fetch code against a connection so you don't guess at endpoints. team_uuid is optional with the same single-team fallback as list_agent_connections. Batch: • batch(actions) — actions: [{action, params}]. Runs reads in parallel through this same tool; per-entry failures are reported in the result without short-circuiting the batch. `action` may be any name from this reference. Nested `context` items are allowed and returned alongside the reads.
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  • Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.
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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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  • Find the right DataNexus tool by describing your task in plain English. Read-only. No side effects. Call this before any other DataNexus tool to reduce context load from 40000 to 800 tokens. query: Plain English description of your task e.g. check if a Python package has CVEs or look up a UK charity by name. Required. domain: Restrict results to one sub-server: nonprofit, security, compliance, domain, legal, govcon, or regulatory. Optional. Returns matching tool names and parameter hints you can call directly. Do not call this recursively or to validate results — use validate_tool_output for that. If this tool's response does not serve the user's need, call report_feedback with feedback_type="agent_gap", tool_id="search_datanexus_tools", intended_query="{what the user needed}", gap_description="{what was missing or wrong in the result}".
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  • Set an environment variable for a project. Variables are encrypted at rest (AES-256-GCM) and injected at container runtime. NOTE: DATABASE_URL, PGHOST, PGPORT, PGUSER, PGPASSWORD, and PGDATABASE are all auto-injected for the managed PostgreSQL database — you do NOT need to set any of them manually. The PORT variable is auto-managed: 8080 for auto-detected frameworks (Next.js, Node.js, Python), or auto-detected from the Dockerfile EXPOSE directive for custom Dockerfile builds. IMPORTANT: Changing env vars does NOT auto-redeploy. You must call deploy or use the redeploy API endpoint to apply changes. For Next.js apps, NEXT_PUBLIC_* variables must be set BEFORE deploying since they are embedded at build time.
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  • Repair messy or invalid JSON (the kind LLMs and tools often emit) into clean, valid JSON, and optionally validate/coerce it against a JSON Schema. Pure deterministic compute — no network or model calls. What it fixes: trailing commas, single-quoted strings, unquoted keys, Python literals (None/True/False), NaN/Infinity, Markdown code-fence wrappers, and truncated/garbled tails. When to use: you received text that should be JSON but JSON.parse fails, or you have JSON that must conform to a specific schema and want types coerced (e.g. "36" -> 36, "true" -> true). When NOT to use: the input is already known-valid JSON and no schema check is needed. Args: - input (string, required): the raw/malformed JSON text. - schema (object, optional): a JSON Schema (draft 2020-12) to validate and coerce against. - coerce (boolean, optional, default true): coerce primitive types to satisfy the schema before validating. Returns structuredContent: { "ok": boolean, // true if valid JSON (and schema-valid when a schema was given) "data": any, // the repaired/validated JSON value; null if unfixable "changed": boolean, // true if any repair or coercion modified the input "errors": string[], // actionable messages when ok is false "repairs": string[] // description of each fix applied }
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  • 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.
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  • Get a one-time uploader script to push a local file into a shared file. For large local HTML/Markdown files that don't fit inline in ``file_create``/``file_update``. Returns ``upload_url``, ``upload_token``, an ``expires_in_seconds`` TTL, and a self-deleting Python ``script``. Save the script to disk and run ``python3 upload.py /path/to/file``; it reads the file, POSTs it to the server with the one-time token, prints the resulting file id (and public URL if ``publish=true``), and deletes itself on success. The token is single-use and expires in ~10 min. **Update mode:** pass ``file_id`` to append the uploaded body as a new version to an existing shared file (the title is ignored; the existing file's title/slug/share_token are preserved, and the bucket mirror is re-published to the new version when the file is already published). This is the supported way to push a new version of a large file.
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  • [RETRIEVAL / READ] Search MisakaNet's public failure-lesson index by error text, keyword, or topic. This is the primary read path — run it first when you hit an error, before deciding to submit anything. For a known lesson ID or path, prefer misakanet_get_lesson — it skips ranking and returns the full content. detail controls progressive disclosure: compact (default, ~80 tok/lesson) for broad scans, summary (~200 tok) adds domain/tags/fix, full returns complete lesson data. FAQ: results may also include answered questions (type="faq", issue_url + answer) — if a maintainer already answered the same question, the answer surfaces here. Returns: object {results: [{id, title, domain, tags, path, description, score}], source, detail, query}; on no match: {no_match: true, suggestion, intake}. Example: misakanet_search(query='pip install timeout', domain='python', top=3)
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