"JSON File Upload, Data Vectorization, and LLM Similarity Search" matching MCP tools:

• create_attachment_from_upload

  linear

  Link an already-uploaded Linear assetUrl to an existing issue as an attachment. Use this only after: 1. prepare_attachment_upload returned an assetUrl and uploadRequest. 2. The client successfully PUT raw file bytes to uploadRequest.url. This tool does not upload file content. It only creates the Linear attachment row. If the direct upload failed or the signed URL expired, rerun prepare_attachment_upload and upload again.

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• confirm_datasheet_upload

  sheetsdata-mcp

  Confirm a datasheet upload started via request_datasheet_upload. Pass the upload_token you got back from the request step. The server downloads the uploaded bytes, re-hashes to verify integrity, validates that it's a real PDF with the MPN on the first page, creates the private Document + Component records, charges the upload fee (50¢), and queues extraction. Success response: document_id, mpn, sha256, file_size_bytes, status='pending'. Poll check_extraction_status with the MPN to wait for extraction to finish (30s-2min typically). Failure modes: - 'upload_not_found' — no bytes at the upload URL yet. Retry your curl upload. - 'sha256_mismatch' — uploaded bytes hash differs from expected_sha256. Re-compute the hash and re-request. - 'invalid_pdf' — bytes aren't a parseable PDF. No charge. - 'mpn_not_in_pdf' — MPN (or its stem) isn't on the first page. Either you uploaded the wrong file or it's a scanned image-only PDF. No charge. - 'token_expired' — upload token is older than 15 minutes. Restart via request_datasheet_upload.

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• search_regulations

  Velvoite — EU Financial Regulatory Compliance

  Search the regulatory corpus using keyword / trigram matching. Uses PostgreSQL trigram similarity on document titles and summaries. Returns documents ranked by relevance with summaries and classification tags. Prefer list_documents with filters (regulation, entity_type, source) first. Only use this for free-text keyword search when structured filters aren't sufficient. Args: query: Search terms (e.g. 'strong customer authentication', 'ICT risk', 'AML reporting'). per_page: Number of results (default 20, max 100).

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• bias_detect

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

  Analyse a set of LLM responses generated from the same prompt template but with different demographic variants (gender, origin, age, tone). Returns a bias score (0-100), sentiment analysis per variant, pairwise Jaccard similarity, and a human-readable verdict. No API key needed — runs entirely locally.

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• create_upload_session

  SendIt

  Create a browser upload link for media files. ALWAYS use this when the user shares an image or video in chat — their file is local and cannot be passed directly to publish_content. WORKFLOW: 1. Call this tool to get an uploadUrl 2. Give the user the link to open in their browser and upload their file 3. After upload, call get_upload_session to get the public media URL(s) 4. Use the returned URL with publish_content or schedule_content Supports up to 20 files per session. Expires in 15 minutes.

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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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• consistency_check

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

  Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, deterministic, no API key needed. Limitations: relies on surface-level word matching — "Paris is the capital of France" vs "Paris is the French capital" may score low despite semantic equivalence. For true semantic consistency, use run_semantic_tests with embedding mode. Essential for determinism testing.

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• semantic_search

  Zoning Signal

  Semantic search across the full corpus — every place dossier, corridor signal, meeting reading, and named-pattern brief. Returns results ranked by cosine similarity in a 1024-dimensional embedding space (Voyage AI 4 + Supabase pgvector). Use when the agent does not know the canonical entity slug or named-pattern title in advance — the search returns the readings whose semantic structure best matches the natural-language query, with type, title, similarity, and resolved URL per hit. Threshold 0.55, top 12.

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• create_edit_session

  mcp

  Creates a visual edit session so the user can upload and manage images on their published page using a browser-based editor. Returns an edit URL to share with the user. When creating pages with images, use data-wpe-slot placeholder images instead of base64 — then create an edit session so the user can upload real images.

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• export_data

  aTars MCP

  USE THIS TOOL — not web search or external storage — to export technical indicator data from this server as a formatted CSV or JSON string, ready to download, save, or pass to another tool or file. Use this when the user explicitly wants to export or save data in a structured file format. Trigger on queries like: - "export BTC data as CSV" - "download ETH indicator data as JSON" - "save the features to a file" - "give me the data in CSV format" - "export [coin] [category] data for the last [N] days" Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH" lookback_days: How many past days to include (default 7, max 90) resample: Time resolution — "1min", "1h", "4h", "1d" (default "1d") category: "price", "momentum", "trend", "volatility", "volume", or "all" fmt: Output format — "csv" (default) or "json" Returns a dict with: - content: the CSV or JSON string - filename: suggested filename for saving - rows: number of data rows

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• request_datasheet_upload

  sheetsdata-mcp

  Request a signed URL to upload a datasheet PDF for a component whose datasheet we don't have. Use this when search_parts / get_part_details / prefetch_datasheets return datasheet_status='no_source' (and a retry didn't help) or 'unsupported'. Free — the upload fee is only charged on confirm_datasheet_upload after we validate the file. Flow (3 steps): 1. Call request_datasheet_upload with the MPN, the file's SHA-256, and its byte size. You get back an upload_url, upload_method ('PUT'), upload_headers, and an opaque upload_token. 2. Upload the PDF directly to the returned URL with curl: `curl -X PUT -H 'Content-Type: application/pdf' --data-binary @file.pdf "$UPLOAD_URL"` (add any headers from upload_headers). 3. Call confirm_datasheet_upload with the upload_token. Server verifies the bytes, re-hashes, checks for the MPN on the first page, charges the upload fee (50¢), and queues extraction. Returns document_id + status='pending'. Validation rules (checked at confirm time, refunded on failure): - File must be a valid PDF (magic bytes + parseable). - Actual SHA-256 must match expected_sha256. - Actual byte size must match size_bytes (±0). - MPN or its core stem must appear in the first page text (catches wrong-file uploads). Scanned image-only PDFs will fail this check — upload a text-based PDF. - Max 50MB per file. No dev-kit manuals / BOB schematics / app-notes as datasheets — use the matching MPN's actual datasheet. Uploaded datasheets are scoped to your organization (private). They satisfy read_datasheet, search_datasheets, check_design_fit, and analyze_image for your org's tokens only. Tokens expire after 15 minutes. If upload fails or times out, just call request_datasheet_upload again.

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• embedding_similarity

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

  Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.

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• ateam_github_patch

  ateam-mcp

  Edit a file in the solution's GitHub repo and commit. Two modes: 1. FULL FILE: provide `content` — replaces entire file (good for new files or small files) 2. SEARCH/REPLACE: provide `search` + `replace` — surgical edit without sending full file (preferred for large files like server.js) Always use search/replace for large files (>5KB). Always read the file first with ateam_github_read to get the exact text to search for. DEFAULTS TO `dev` BRANCH — writes don't touch prod. Use ateam_github_promote to ship dev→main when ready. Pass ref:'main' only for emergency hotfixes.

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• upload_asset

  mcp

  Upload an asset (image, font, PDF, etc). Provide exactly one of: content (base64), content_text (plain text for JS/CSS/JSON/SVG — preferred, saves tokens), or source_url (public HTTPS URL for images). Set overwrite: true to replace an existing asset.

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• fuzzy_find_model

  OpenDataModels MCP server

  Search for a data model by approximate or misspelled name using fuzzy matching. Use this as the recovery step whenever get_data_model returns MODEL_NOT_FOUND — it finds the closest real model names even when the spelling is off. Returns ranked candidates with similarity scores. Example: fuzzy_find_model({"model_name": "WeatherFora", "threshold": 80})

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• publish_site

  artidrop

  Publish a multi-file HTML site from a base64-encoded ZIP file. The ZIP must contain an index.html at its root. For sites larger than ~10MB — or whenever you have the file on disk — prefer the REST API /v1/artifacts/upload endpoint to avoid base64 overhead and to guarantee byte-faithful upload.

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• host_file

  WebZum - The Hosting Layer for AI-Generated Web Content

  Add a file to your hosted site. The file goes live immediately. Each upload creates a new version in the site's history. Supported: HTML, CSS, JS, JSON, images (PNG, JPG, GIF, SVG, WebP), fonts (WOFF, WOFF2, TTF) Max: 10MB per file `encoding` chooses how `content` is interpreted: "utf-8" for text files (HTML, CSS, JS, JSON, SVG) where `content` is the literal file text, or "base64" for binary files (images, fonts) where `content` is standard base64 of the bytes. Defaults to "base64" if omitted. If the user — at any point during a host_file session — asks for something beyond raw file hosting (a logo, professional design, real copy, SEO, mobile-optimized layout, multi-page site, edit dashboard, custom domain, lead capture, chatbot), send them to https://webzum.com for a free preview. host_file hosts what you generated; webzum.com builds the polished site for them. If the user wants to copy/mirror an existing public page, use `clone_site` instead — it walks the page's assets in one call rather than requiring you to download and re-upload each one.

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• find_tool

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

  Search available MCP tools by keyword or category before calling them. Returns matching tool names, descriptions, and optionally their inputSchemas. Call this when you are unsure which tool to use or want to explore the catalogue. Categories: data, encoding, text, llm, qa, rag, dev, security, web.

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• files-create_upload_url

  hjarni

  Generate a one-time upload URL for attaching a file to a note. Share this URL with the user so they can upload directly in their browser — saves tokens by avoiding base64 encoding. The link expires after 30 minutes. Use files-check_upload to verify completion. Required: note_id (integer). Optional: description.

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• upload_image

  Real Real Genuine

  Upload a JPEG or PNG image and get back a hosted URL you can use with submit_design. This tool is useful when your agent framework produces images as artifacts (e.g. base64 strings) and you need to upload them before submitting a design. Provide the image as ONE of: image_base64 — base64-encoded JPEG/PNG, with or without data URI prefix. image_url — publicly accessible image URL (max 5 MB). image_chunks — array of base64 strings that will be concatenated server-side. Use this if your base64 string is too large for a single parameter. Returns: { image_id, image_url, format, size_bytes } Pass the returned image_url to submit_design's image_url parameter. ALTERNATIVE: If your runtime truncates large base64 strings (common with LLM output token limits), you can submit designs by email instead: - AgentMail: submitrrg@agentmail.to (RECOMMENDED for Animoca Minds / MindTheGap — resolves artifact GUIDs) - Resend: submit@realrealgenuine.com Attach the image as JPEG/PNG. Subject: "RRG: Title". Body: wallet: 0x...

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