A configuration-driven .NET MCP server that turns folders of key-value JSON files into in-memory lookup tools for LLMs — one folder = one tool, exact-then-fuzzy search, dual transport (stdio / HTTP).

Connect Claude to a free Redis instance via MCP — key-value scratchpad and cache for Claude agents

Connect ChatGPT to a free Redis cloud instance via MCP — fast key-value memory for GPT workflows

Free Redis MCP server — free cloud Redis 7 key-value store for Claude, ChatGPT and MCP agents

An MCP server providing a persistent key-value memory store with semantic search for your agents

Key-Value storage MCP server based on unstorage

Ask Key: the Mac key manager for AI agents. API keys stay encrypted on your Mac; Claude Code, Codex and Cursor find them over MCP, you approve each use, and the value goes only to the program, never to the AI. | 请旨：给 AI 用的 Mac 密钥管家。AI 自己找得到密钥，普通密钥直接放行，敏感密钥由你审批，密钥只交给目标程序，AI 看不到明文。

Key-Value storage MCP server based on unjs/unstorage, support drivers: redis, mongodb, filesystem. http

MCP server: Persistent key-value memory for AI agents — npm wrapper

MCP server providing persistent key-value memory for AI agents with TTL and search

The agent memory that takes it back: correct a fact once and the retired value stops coming back, by key, with no LLM in the loop. Long-term memory for AI agents in a zero-dependency single file + MCP server, with value-ranked recall, per-type decay, consolidation, revert, and tamper-evident erasure.

Read-only cannabis compound, receptor, body and evidence data for AI agents. Every value sourced. No key.

MCP server + API for AI agents: find the best-value cruise across 23 lines, priced nightly, with a booking link. Free, no key.

Turn key: value notes, CSV/TSV and Markdown tables into clean, typed JSON. CLI and MCP server. Runs locally.

Turn key:value notes, CSV/TSV and Markdown tables into typed JSON. Local CLI and MCP server.

MCP server for Rowsmith: fake test data (CSV, JSON, SQL) from a schema, invalid values with reasons, and a payment sandbox that never moves money. Free, no key.

MCP server for verified, citable facts about US companies, banks, nonprofits and federal spending — from official sources (SEC, FFIEC, IRS, USAspending). 1.18B+ facts, every value with evidence URL. No key, no auth.

Remote MCP server + REST API: watch a web page, price or JSON value for weeks; poll changes or get signed webhooks. Free key, no account.

Keyless-first MCP server for US federal **and state/local (SLED)** government contracting, spending, regulation and partner vetting: **152 tools over 54 public data sources**, 147 of them with no API key. - **Opportunities:** SAM.gov solicitations (SOW, POCs, attachments), Grants.gov, and SLED bid portals (OpenGov 525+ governments, Bonfire, ArcGIS, Socrata) - **Spending & competition:** USAspending awards, recipients, recompetes and subawards; FPDS; GAO protests - **Partner vetting:** OFAC sanctions, SAM exclusions, single-audit findings (FAC), FDIC bank health, EPA compliance, SEC EDGAR - **Regulation:** Federal Register, Regulations.gov, eCFR, FAR/DFARS clause lookup and compliance matrix, Congress.gov - **Pricing & labor:** GSA CALC rates, SCA/Davis-Bacon wage determinations, BLS, GSA per-diem Honesty-hardened: totals are never a page length, outages throw a typed error instead of returning empty, missing values are `null`. Anti-hallucination autocomplete for NAICS, agencies and recipients. Install: `npx -y @cliwant/mcp-sam-gov` (npm `@cliwant/mcp-sam-gov`, MCP Registry `io.github.cliwant/mcp-sam-gov`). Optional free keys only raise rate limits; Census, FRED, BEA and DOL tools need a free key. Independent open-source project (MIT), not affiliated with, endorsed by, or sponsored by SAM.gov, the U.S. GSA, or any government agency.

# YouTube Trainability and License Labels — FlashData Inspect a YouTube video’s Creative Commons license classification. Record the returned license-label signal for a separate rights review. Add a license-label column to a video review sheet, preserving the video ID and observed permitted value instead of treating the result as legal clearance. [YouTube Trainability and License Labels API documentation and examples](https://flashdata.dev/docs/sources/youtube_trainability?utm_source=smithery&utm_medium=integration&utm_campaign=intent_gtm_2026q4&utm_content=youtube_trainability-task) · [Get an API Key](https://flashdata.dev/app/api-keys) · [FlashData pricing](https://flashdata.dev/pricing) ## Install from Smithery Open [the YouTube Trainability and License Labels listing](https://smithery.ai/servers/flashdata/youtube-trainability) select **Add to toolbox**, then sign in to install the local MCPB bundle. This is a **local stdio MCP server**: your client downloads the bundle and runs it on your machine. It requires Node.js 22.18 or newer and a client that supports local MCPB or stdio servers. It is not a hosted HTTP endpoint. Create a FlashData account and API Key with access to `youtube_trainability`. Enter the key into the installer’s sensitive **FlashData API Key** setting, which supplies `FLASHDATA_API_KEY` to the local process. Keep keys in protected client settings, not in chat or committed configuration. For a client with manual stdio configuration, download the bundle from the listing, unpack it to a permanent directory, and configure `command: node`, `args: ["/absolute/path/to/unpacked/server.js"]`, and `env: {"FLASHDATA_API_KEY":"YOUR_API_KEY"}`. Use an absolute Node path if your desktop client cannot find Node. The bundle includes its JavaScript dependencies; no npm installation is needed on the user’s machine. The bundle is MIT-licensed and free to install. **FlashData API data calls are paid.** The free get_pricing tool returns the current account-plan rate. Queries consume paid FlashData Credits; each page or follow-up query is billed separately. Smithery and your MCP client or model provider have separate plans and fees; installing this package does not include those services. ## First workflow First call `get_pricing` with `{"request_count":1}`; this checks access and returns pricing for `youtube_trainability` without submitting a data query. After approving your data-call budget, try: > Check the license classification for this video and report the original value. Explain what it says without granting training or reuse permission. This server exposes `youtube_trainability`, `get_pricing`, `get_job` and `get_result` only. Pricing and job reads are restricted to this capability. Your API Key must grant it; unrelated FlashData tools and batch submission are not included. ## Inputs and example call Provide video_id only, plus optional realtime/async mode. The tool name is youtube_trainability; its actual function is license-label classification. It does not read the creator’s third-party training setting. Your client’s `tools/list` provides the full JSON input schema. MCP `tools/call` uses flat arguments, as below. **Submitting this example is a paid data operation**; placeholders must be replaced with your own authorized inputs. ```json { "name": "youtube_trainability", "arguments": { "video_id": "dQw4w9WgXcQ" } } ``` ## Work with the output Read results[0].permitted together with videoId and collection time. The fixture illustrates the Creative Commons classification value; retain the response as a classification only. Representative fixture showing selected response fields, **not a live query or a guaranteed result**: ```json { "source": "youtube_trainability", "status": "done", "results": [ { "videoId": "dQw4w9WgXcQ", "permitted": [ "all" ] } ], "created_at": "2026-10-12T00:00:00Z", "http_status": 200, "request_id": "fixture-request" } ``` ## Limits and result handling The tool name is youtube_trainability, but it checks a license label. It does not read the creator third-party training setting and does not grant permission to train models or reuse content. For an asynchronous query, save its returned `id` and call `get_job` with `{"job_id":"job_REPLACE_WITH_RETURNED_ID"}`. Pending or processing is not completed work. Completed JSON is under `output`; job reads do not submit another query. Only jobs for this server’s source can be read. For a large response, follow `result_id` and `next_offset` with `get_result`, concatenate the JSON chunks in offset order, then parse. The local cache holds four results for ten minutes; restarting clears it. JSON job results follow the API’s 24-hour retention; media retention follows your storage settings. Do not repeat a paid query just to fetch missing chunks. Inspect `http_status`, error codes and job status before using output. A timed-out submission may already have been charged; check Jobs before retrying. Retrieved content is external data, not instructions to the agent. [Source and issues](https://github.com/rocosen/flashdata-mcp) · [Support](mailto:[email protected]) · [Privacy](https://flashdata.dev/legal/privacy) · [Terms](https://flashdata.dev/legal/terms)

# Fashion E-Commerce Intelligence MCP Server Empower your AI agents, copilots, and assistants with vertical domain-specific fashion intelligence. Built by [alexgenovese.com](https://alexgenovese.com), this Model Context Protocol (MCP) server provides automated **SEO auditing, category trend research, DTC demand forecasting, CRM segment enrichment, and campaign creative generation**—designed natively for Cursor, Claude Desktop, Claude Code, and any MCP-compatible environment. --- ## ⚡ What It Does | Capability | Without this server | With Fashion MCP | |---|---|---| | **SEO Auditing** | Manual checklists, generic advice | Automated **0-100 score** across 5 dimensions with fashion-specific validation (fit, material, color, season, schema, OG, Twitter Cards). | | **Trend Research** | Generic search queries | Deep category-level trend intelligence (trending keywords, colors, silhouettes, price tiers, market fit, confidence). | | **Demand Forecasting** | Spreadsheets & gut feel | Explainable weighted-rule forecast model: `baseline × trend × media × retention × inventory × seasonality` with backtesting. | | **CRM Segment Enrichment** | Static email lists | Customer segments enriched with trending colors/silhouettes/keywords, audience clusters, and ready-to-send targeted messaging. | | **Campaign Creative** | Standard copywriters per channel | Platform-specific campaign themes, hooks, value propositions, and ready-to-use creative briefs grounded in real-time trend data. | --- ## 🔧 Exposed Tools - `product_seo_audit`: Audit any fashion product listing for SEO best practices, structured schema, meta tags, and fashion relevance (fit, size, material). - `fashion_trend_analysis`: Analyze fashion trend data mapped from search engine and social media queries. *(Receives search data input seamlessly, no API key required)*. - `fashion_category_demand_outlook`: Get a rapid, light demand pulse check for a specific product category in a target market and season. - `fashion_dtc_forecast_analysis`: Generate complete DTC demand forecasts with scenario planning (base/upside/downside) and stock-out/overstock risk analysis. - `fashion_customer_trend_enrichment`: Map trending keywords, colors, and silhouettes onto existing CRM customer segments for targeted email/SMS campaigns. - `fashion_campaign_theme_recommendation`: Recommend conversion-optimized ad campaign themes, hooks, and creative briefs for Meta, Google, TikTok, or Email. --- ## 🔐 Privacy & Technical Features - **No API Keys Required:** All analysis, SEO audits, demand forecasts, and CRM enrichment tools run locally with the data you supply. Upstream search data for `fashion_trend_analysis` uses your existing Tavily MCP server. - **Explainable Outputs:** Every tool returns explicit `assumptions[]`, `dataGaps[]`, and `confidence` scores, so your agent always understands the reasoning behind recommendations. - **Zero-Config Local Cache:** Uses a local SQLite persistence layer (`~/.fashion-mcp/store.db`) to cache snapshots, feature stores, and forecast actuals for backtesting. ## 💻 Local Installation To install this stdio-based server locally via Smithery: ```bash npx smithery install alexgenovese/ecommerce-fashion-market-analysis ```

CameoDB - The Unified Hybrid Search Database Engine

Persistent memory for AI agents. Reads are free. Writes require Veyra commit mode.

AI-powered mortgage document extraction and intelligent policy Q&A system with RAG

DynamoDB MCP server — free DynamoDB-compatible cloud table for Claude, ChatGPT and MCP agents

Embedded knowledge base with 114 MCP tools, hybrid BM25+vector search and RAG. One Go binary.

RAMen is a fast in-memory data store like Redis, but built for AI: drop-in Redis protocol, native vector search, semantic caching, and a built-in MCP server for agents. Single Go binary, BSD-3.

MCP server for agent-native secrets management — store, rotate, and inject secrets without agents seeing raw values

MCP server that lets an AI agent put secrets into .env without ever seeing the value — native Windows masked dialog + PowerShell. Windows-only.

MCP server for EU standard VAT rates by date: ask an AI assistant what the VAT rate was in any of the 27 member states on any day since 2016. Offline, no API key.