"Analyzing Python Modules, Docker Files, and Generating Mock Data or Outputs" matching MCP tools:

• convert_html_to_pdf

  Sats4AI - Bitcoin-Powered AI Tools

  Convert HTML or Markdown to a pixel-perfect PDF. Returns JSON: { url } — a temporary download URL (valid ~1 hour). Great for generating invoices, reports, receipts, or formatted documents programmatically. Supports full HTML/CSS including tables, images (base64 or URL), and inline styles. For Markdown input, set format='markdown'. 50 sats per conversion. Use convert_file instead for converting existing files between formats (e.g., DOCX→PDF). Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='convert_html_to_pdf'.

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• delete_app

  WebSlop

  DESTRUCTIVE: Permanently delete an app, its Docker service, volume, and all data including version history. This cannot be undone. You MUST confirm with the user before calling this tool.

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• get_job_status

  Weftly

  Check the status of a transcribe or summarize job. Returns the current state and, when completed, an `outputs` array. Each output has either `content` (returned inline) or a presigned, time-limited (1 hour) `download_url`. Small text outputs (e.g. `transcript` SRT, `clip-candidates`, `summary`) come inline as `content`; larger outputs — `transcript-words` JSON for any non-trivial recording, plus video outputs like `clip-video` / `clip-vertical-video` — come as a `download_url` to fetch when needed. Optionally pass `format` (srt, txt, vtt, json, words) to get the transcript content inline in the top-level `transcript` field — `txt` and `vtt` are derived from the stored SRT; `json` is v1 (segments only); `words` is v2 (segments + per-word timestamps matching /.well-known/weftly-transcript-v2.schema.json). Poll this periodically after calling complete_upload — wait at least 60 seconds between checks. For files under 10 minutes, jobs usually complete within 1-2 minutes. For long files (1hr+), expect 10-30 minutes. Also use this to recover from lost state: if the original challenge was lost, call get_job_status(job_id) to retrieve a fresh challenge (status "awaiting_payment") or the upload URL (status "awaiting_upload").

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• analyze_record

  Senzing

  Get the Senzing JSON analyzer script to validate mapped data files client-side. REQUIRED: `workspace_dir` (writable directory, e.g. ~/sz-workspace) — the call WILL FAIL without it. The analyzer validates records against the Entity Specification, examines feature distribution, attribute coverage, and data quality. Returns a Python script (no dependencies) with instructions. No source data is sent to the server. Typical workspace_dir values: Linux `/tmp` or `~/sz-workspace`; macOS `~/sz-workspace`; sandboxed envs: explicit path under home (do NOT assume /tmp exists).

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• get_plan_status

  FavCRM

  Show the current organisation plan, subscription/payment state, enabled modules, and quota usage. Use before deciding whether an agentic operation is allowed.

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• find_examples

  Senzing

  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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• code_get_file

  searchcode

  Get code from a remote public git repository — either a specific function/class by name, a line range, or a full file. PREFERRED WORKFLOW: When search results or findings have already identified a specific function, method, or class, use symbol_name to extract just that declaration. This avoids fetching entire files and keeps context focused. Only fetch full files when you need a broad understanding of a file you haven't seen before. For supported languages (Go, Python, TypeScript, JavaScript, Java, C, C++, C#, Kotlin, Swift, Rust) the response includes a symbols list of declarations with line ranges. This is not a first-call tool — use code_analyze or code_search first to identify targets, then extract precisely what you need.

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• axint.validate

  Axint

  Validate a TypeScript intent definition without generating Swift. Runs the full Axint validation pipeline (134 diagnostic rules) and returns a JSON array of diagnostics: { severity: 'error'|'warning', code: 'AXnnn', line: number, column: number,... Use: use for TypeScript DSL diagnostics before Swift output; use swift.validate for existing Swift. Effects: read-only diagnostics; writes no files and uses no network.

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• tfoutputs

  InsideOut (Riley)

  INSPECTION: Retrieve Terraform outputs from a completed deployment Returns structured output values (VPC IDs, endpoints, cluster names, etc.) after a successful deploy. Sensitive outputs are redacted (shown as '(sensitive)'). By default returns outputs for the latest successful deploy. Optionally specify job_id to get outputs for a specific deployment. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id (specific deployment), lifecycle (filter by step e.g. 'cloud-provision').

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• x711_code_sandbox

  x711 — Universal Agent Gas Station

  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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• estimate_cost

  AiDOOS Virtual Delivery Center

  Use this tool when a user wants cost or sizing for specific deliverables they've already listed. Trigger phrases: 'how much would it cost to build X, Y, and Z', 'estimate the price for these features', 'how many Delivery Units / weeks would these modules take', 'budget for this work', 'price out this scope', 'I need a ballpark for the following'. Use this INSTEAD OF plan_vdc when the user has already decomposed the work into specific modules — don't make them go through pod/role generation again. If the user only describes a goal without modules, prefer plan_vdc. What this tool does: takes 1-30 module descriptions, returns Delivery Units per module, total Delivery Units, project-rate USD cost, and the recommended Delivery Pack (Starter 10 DUs/$2K, Small 60 DUs/$10K, Scale 250 DUs/$40K, or Enterprise).

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• mock_from_schema

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, date-time, uri, uuid), enum, const, and nested schemas. Perfect for testing MCP tools with realistic data.

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• executive_comp_peer_benchmark

  gapup-mcp

  As a Chief Human Resources Officer (CHRO), benchmark executive compensation packages against peer companies using public SEC filings and private compensation data from Equilar and Bloomberg. Inputs include executive name, title, company ticker, and peer group criteria. Outputs structured compensation metrics (base salary, bonus, equity, total compensation) with source attribution and confidence scores.

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• sabbatical_policy_comparator

  gapup-mcp

  Enables CHROs to benchmark their company's sabbatical policies against peer organizations using data from SHRM, Payscale, and Mercer. Inputs include company size, industry, and current policy details. Outputs structured comparison with cost impact analysis, eligibility criteria, and duration benchmarks. Ideal for strategic HR planning and policy optimization.

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• syndicated_loan_pricing_benchmark

  mcp-knowledge

  Provides CFOs with peer benchmarking for syndicated loan pricing by comparing current loan terms against market data from Tradeweb and FRED. Inputs include loan amount, tenor, credit rating, and currency. Outputs structured pricing benchmarks with spread, yield, and fee comparisons. Ideal for quick validation of loan competitiveness or negotiation preparation.

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• check_plan_operation

  FavCRM

  Preflight whether an intended operation is allowed by token scopes, enabled modules, subscription state, and quota. Provide toolName, quotaCode, or moduleCode.

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• list_campaigns

  LinkedIn MCP Server (Salesbot)

  List your campaigns with ID, name, status (draft/running/paused), description and lead counts. Use this to obtain campaign_id when adding leads, generating messages or approving drafts.

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• create_mock_for_function

  3TG Test Generation

  Generate a Jest manual mock file for a specific exported function. 3TG writes the mock to `<srcDir>/__mocks__/<basename>.<ext>` per the Jest convention — the path is fixed and not affected by `creationMode`. Use this when isolating a downstream test from a known dependency. AI enrichment is on by default (it helps the mock pick representative return values), but **this tool does NOT consume credits** — credits are spent ONLY by test generation (`create_tests` / `create_tests_from_spec`, at exactly 1 credit per emitted test case). Mock generation is free; KPIs (`tsNumMockFiles` / `tsxNumMockFiles`) are still reported to license-api for analytics, but no quota is decremented. CRITICAL POST-CALL ACTION — write returned files to disk: The MCP server does NOT touch the user's filesystem. It returns the generated file CONTENTS in the response's `files` array. After this tool returns, you MUST iterate over `files` and write each entry's `content` verbatim to its `path` using your native file-write capability (e.g. Write / edit_file / create_file — whatever your client exposes). Create parent directories as needed. Returned paths are project-root-relative and already translated to the `.3tg/` mirror convention where applicable (e.g. specs land under `.3tg/<source-path>.3tg.md`; tests / mocks travel through unchanged). Write each path verbatim. Do NOT claim "Generated test file: <path>" unless you have actually written the file. The user will assume the MCP wrote it and waste time looking for a non-existent file. If you can't write for some reason (permission denied, no write capability in this client), return the contents inline in your message so the user can copy-paste them. Never report success silently when the write didn't happen.

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• purl_audit

  ZEN SecDB

  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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• get_site_status

  BorealHost

  Get detailed status of a hosted site including resources, domains, and modules. Requires: API key with read scope. Args: slug: Site identifier (the slug chosen during checkout) Returns: {"slug": "my-site", "plan": "site_starter", "status": "active", "domains": ["my-site.borealhost.ai"], "modules": {...}, "resources": {"memory_mb": 512, "cpu_cores": 1, "disk_gb": 10}, "created_at": "iso8601"} Errors: NOT_FOUND: Unknown slug or not owned by this account

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