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127,264 tools. Last updated 2026-05-05 12:34

"A server for RSS feed processing with database storage and AI-powered search" matching MCP tools:

  • Get Lenny Zeltser's cybersecurity-writing rating sheet(s) so your AI can apply the rubric. Returns the structured rubric (groups, items, scoring bands) WITHOUT computing a score. Use `rating_score_writing` if you also want a numeric score, gap analysis, or rubric-anchored feedback. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • DESTRUCTIVE — IRREVERSIBLE. Permanently delete a file from the user's Drive. Removes the file from S3 storage and the database. Storage quota is freed immediately. ALWAYS ask for explicit user confirmation before calling this tool. # delete_file ## When to use DESTRUCTIVE — IRREVERSIBLE. Permanently delete a file from the user's Drive. Removes the file from S3 storage and the database. Storage quota is freed immediately. ALWAYS ask for explicit user confirmation before calling this tool. ## Parameters to validate before calling - file_token (string, required) — The file token (UUID) of the file to delete. Get via fetch_files. ## Notes - DESTRUCTIVE — IRREVERSIBLE. Always confirm with the user before calling. Explain what will be lost.
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  • Search Cochrane systematic reviews via PubMed. Finds Cochrane Database of Systematic Reviews articles matching your query. Returns PubMed IDs, titles, and publication dates. Use get_review_detail with a PMID to get the full abstract. Args: query: Search terms for finding reviews (e.g. 'diabetes exercise', 'hypertension treatment', 'childhood vaccination safety'). limit: Maximum number of results to return (default 20, max 100).
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  • Retrieves AI-generated summaries of web search results using Brave's Summarizer API. This tool processes search results to create concise, coherent summaries of information gathered from multiple sources. When to use: - When you need a concise overview of complex topics from multiple sources - For quick fact-checking or getting key points without reading full articles - When providing users with summarized information that synthesizes various perspectives - For research tasks requiring distilled information from web searches Returns a text summary that consolidates information from the search results. Optional features include inline references to source URLs and additional entity information. Requirements: Must first perform a web search using brave_web_search with summary=true parameter. Requires a Pro AI subscription to access the summarizer functionality.
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  • Search Helium's balanced news stories — AI-synthesized articles that aggregate multiple sources. Unlike search_news (which returns individual RSS articles), this returns Helium's own synthesized stories: each one draws from multiple sources and includes an AI-written summary, takeaway, context, evidence breakdown, potential outcomes, and relevant tickers. Returns a list of stories, each with: - title, simple_title, date, category - page_url: full URL to the story on heliumtrades.com - image: story image URL (when available) - summary: Helium's synthesized overview - takeaway: key conclusion - context: background context - evidence: numbered evidence items - potential_outcomes: forward-looking outcomes with probabilities - relevant_tickers: related stock tickers - num_sources: number of source articles synthesized - rank: search relevance score Args: query: Search keywords (required). limit: Max results (1-50, default 10). category: Filter by category. One of: 'tech', 'politics', 'markets', 'business', 'science'. days_back: Only include stories from the last N days. 0 means no date filter.
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  • Start an async rank of multiple candidates against a job description (8 credits). Returns task_id and analysis_id. Poll with careerproof_task_status, then fetch result with careerproof_task_result (result_type='fit_rank'). Requires context_id from atlas_list_contexts, candidate_ids from atlas_list_candidates (minimum 2), and jd_text. For async batch processing with more detail, use atlas_start_jd_fit_batch instead.
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Matching MCP Servers

  • A
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    Professional RSS/Atom feed management system with AI-powered analytics including sentiment analysis, trend detection, auto-categorization, cross-source verification, automated scheduling, and content export capabilities.
    Last updated
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    MIT

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  • Encrypted A2A object storage for autonomous agent state and artifacts

  • Image processing for AI agents. Resize, convert, compress, and pipeline images.

  • [tourradar] Search for tours by title using AI-powered semantic search. Returns a list of matching tour IDs and titles. Use this when you need to look up a tour by name. When you know tour id, use b2b-tour-details tool to display details about specific tour
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  • Import data into a Cloud SQL instance. If the file doesn't start with `gs://`, then the assumption is that the file is stored locally. If the file is local, then the file must be uploaded to Cloud Storage before you can make the actual `import_data` call. To upload the file to Cloud Storage, you can use the `gcloud` or `gsutil` commands. Before you upload the file to Cloud Storage, consider whether you want to use an existing bucket or create a new bucket in the provided project. After the file is uploaded to Cloud Storage, the instance service account must have sufficient permissions to read the uploaded file from the Cloud Storage bucket. This can be accomplished as follows: 1. Use the `get_instance` tool to get the email address of the instance service account. From the output of the tool, get the value of the `serviceAccountEmailAddress` field. 2. Grant the instance service account the `storage.objectAdmin` role on the provided Cloud Storage bucket. Use a command like `gcloud storage buckets add-iam-policy-binding` or a request to the Cloud Storage API. It can take from two to up to seven minutes or more for the role to be granted and the permissions to be propagated to the service account in Cloud Storage. If you encounter a permissions error after updatingthe IAM policy, then wait a few minutes and try again. After permissions are granted, you can import the data. We recommend that you leave optional parameters empty and use the system defaults. The file type can typically be determined by the file extension. For example, if the file is a SQL file, `.sql` or `.csv` for CSV file. The following is a sample SQL `importContext` for MySQL. ``` { "uri": "gs://sample-gcs-bucket/sample-file.sql", "kind": "sql#importContext", "fileType": "SQL" } ``` There is no `database` parameter present for MySQL since the database name is expected to be present in the SQL file. Specify only one URI. No other fields are required outside of `importContext`. For PostgreSQL, the `database` field is required. The following is a sample PostgreSQL `importContext` with the `database` field specified. ``` { "uri": "gs://sample-gcs-bucket/sample-file.sql", "kind": "sql#importContext", "fileType": "SQL", "database": "sample-db" } ``` The `import_data` tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes.
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  • Check server connectivity, authentication status, and database size. When to use: First tool call to verify MCP connection and auth state before collection operations. Examples: - `status()` - check if server is operational, see quote_count, and current auth state
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  • [tourradar] Search tour reviews using AI-powered semantic search. Requires tourIds to scope results to specific tours. Use this when the user asks about reviews, feedback, or experiences for specific tours. Combine with an optional text query to find reviews mentioning specific topics (e.g., 'food', 'guide', 'accommodation'). When you don't have tour IDs, use vertex-tour-search or vertex-tour-title-search first to find them.
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  • Load Lenny Zeltser's complete cybersecurity-writing rating toolkit: all 7 sheets, scoring policy, scoring playbook, and cross-references to the writing guidelines. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • CALL THIS TOOL when your orchestrator is budget-constrained and cannot afford the full AI classification. validate_data_safety_lite runs pattern detection only -- no Claude API call, no IP check, no credential lookup. Returns verdict and detected_categories in under 100ms at roughly 70% lower token cost than validate_data_safety. Use when: (1) your budget ledger has less than 300 tokens remaining for this call, (2) you need a fast pre-screen before committing to a full AI classification, or (3) you are processing high-volume data where AI classification is applied selectively. Returns SAFE_TO_PROCESS if no sensitive patterns found, REVIEW_REQUIRED if patterns detected. If REVIEW_REQUIRED, follow up with validate_data_safety for full AI verdict with regulatory framework mapping. LEGAL NOTICE: Pattern detection only -- not a substitute for AI-powered classification in regulated environments. Full terms: kordagencies.com/terms.html. Free tier: 20 calls/month.
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  • AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or due_diligence_report instead. Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'
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  • Get Lenny Zeltser's scoring playbook so your AI can score a draft locally against a cybersecurity-writing rating sheet. THIS IS THE ONLY TOOL THAT PRODUCES NUMERIC SCORES — the writing-coach tools (`get_security_writing_guidelines`, `ir_*`, `product_*`) never score. Returns the rubric plus step-by-step instructions for applying it. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Search for recalled products similar to your query. This tool searches DeepRecall's global product safety database using AI-powered multimodal matching. Provide a text description and/or product images to find similar recalled products. Use Cases: - Pre-purchase safety checks: Before buying, verify if similar products were recalled - Supplier vetting: Check if a supplier's products have safety issues - Marketplace compliance: Verify products against recall databases - Consumer protection: Identify potentially hazardous products Data Sources: - us_cpsc: US Consumer Product Safety Commission - us_fda: US Food and Drug Administration - safety_gate: EU Safety Gate (Europe) - uk_opss: UK Office for Product Safety & Standards - canada_recalls: Health Canada Recalls - oecd: OECD GlobalRecalls portal - rappel_conso: French Consumer Recalls - accc_recalls: Australian Competition and Consumer Commission Cost: 1 API credit per search Args: content_description: Text description of the product (e.g., "children's toy with small parts") image_urls: List of product image URLs for visual matching (1-10 images) filter_by_data_sources: Limit search to specific agencies (optional) top_k: Number of results (1-100, default: 10) model_name: Fusion model - fuse_max (recommended), fuse_flex, or fuse input_weights: Weights for [text, images], must sum to 1.0 api_key: Your DeepRecall API key (optional if provided via X-API-Key header) Returns: Search results with matched recalls, scores, and product details Example: search_recalls( content_description="baby crib with drop-side rails", top_k=5 )
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  • Build a Tableau dashboard from a Microsoft SQL Server table (end-to-end). Pipeline: MSSQL → schema inference → chart suggestion → workbook creation → live MSSQL connection → .twb output. Requires pyodbc for schema inference and ODBC Driver 17 for SQL Server. IMPORTANT FOR AI AGENTS: see ``csv_to_dashboard`` — auto-charts come from rules, not natural-language requests. Use ``required_charts`` to guarantee specific charts, ``reference_image`` for image-based styling, and cite the returned manifest dict when describing results. Args: server_host: MSSQL server hostname. dbname: Database name. table_name: Table to visualize. username: Database username (ignored if trusted_connection=True). password: Database password (used for schema inference only). port: Server port (default 1433). trusted_connection: Use Windows Authentication instead of SQL auth. output_path: Output .twb path (defaults to <table>_dashboard.twb). dashboard_title: Dashboard title. max_charts: Maximum charts (0 = use rules default). template_path: TWB template path. theme: Theme preset name. rules_yaml: Optional YAML string with dashboard rules overrides. required_charts: See ``csv_to_dashboard.required_charts``. reference_image: See ``csv_to_dashboard.reference_image``. Returns: Structured manifest dict describing what was actually built.
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  • AI-powered Korean crypto market analysis. Combines Kimchi Premium, stablecoin premium, FX rate, Upbit/Bithumb volume rankings, Binance funding rate, open interest, BTC dominance, and Fear & Greed index. Returns AI-generated signal (BULLISH/BEARISH/NEUTRAL), confidence score, actionable summary, and all raw data. Price: $0.10 via x402.
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  • Fetch a live Solana DEX divergence trading signal from Soliris Arc — the agent-to-agent data market built on Arc (Circle's L1 blockchain). Each signal costs $0.001 USDC paid automatically on-chain via the x402 protocol. Signals identify real-time arbitrage spreads across Raydium, Orca, Jupiter, and Meteora. This is the agentic economy in action: your AI pays another AI for data, settled in under 1 second, no humans in the loop. Use demo=true to get a sample signal without payment. For live signals the API returns a 402 with payment details. Powered by Soliris (soliris.pro).
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  • Build a Tableau dashboard from a MySQL table (end-to-end). Pipeline: MySQL → schema inference → chart suggestion → workbook creation → live MySQL connection → .twb output. Requires mysql-connector-python for schema inference. IMPORTANT FOR AI AGENTS: see ``csv_to_dashboard`` — auto-charts come from rules, not natural-language requests. Use ``required_charts`` to guarantee specific charts, ``reference_image`` for image-based styling, and cite the returned manifest dict when describing results. Args: server_host: MySQL server hostname. dbname: Database name. table_name: Table to visualize. username: Database username. password: Database password (used for schema inference only; not stored in the workbook). port: Server port (default 3306). output_path: Output .twb path (defaults to <table>_dashboard.twb). dashboard_title: Dashboard title. max_charts: Maximum charts (0 = use rules default). template_path: TWB template path. theme: Theme preset name. rules_yaml: Optional YAML string with dashboard rules overrides. required_charts: See ``csv_to_dashboard.required_charts``. reference_image: See ``csv_to_dashboard.reference_image``. Returns: Structured manifest dict describing what was actually built.
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  • Quick AI visibility scan. Returns three scores: AEO Score (0-100, AI search engine findability), GEO Score (0-100, AI citation readiness), and Agent Readiness Score (0-100, AI agent interaction capability). Also returns AI Identity Card with mention readiness (0-100, predicts how likely AI will mention the brand), detected competitors, business profile (commerce/saas/media/general), and top 5 issues. 67+ checks across 12 categories. Free — no API key needed. Does NOT return per-check details or fix code — use audit_site for full breakdown, fix_site for generated fixes, compare_sites to benchmark against a competitor.
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