| 0 |
| 0 |
| Ecosystem | 1 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Exposes LinkedIn person profiles as MCP tools callable by Claude and other MCP-compatible AI assistants. Uses Patchright (a hardened Playwright fork) to maintain persistent browser profiles stored locally (~/.linkedin-mcp/profile) with cookie-based authentication, eliminating repeated login flows. Implements a 'one-section-one-navigation' architecture where each profile section (work history, education, skills, certifications, posts) maps to a discrete URL, allowing the AI to request only needed data and minimize page loads.
Unique: Uses Patchright (hardened Playwright fork) instead of standard Playwright/Selenium to evade LinkedIn's bot detection, combined with persistent local browser profiles that maintain authentication state across sessions without re-login. The 'one-section-one-navigation' design allows granular data fetching mapped to discrete URLs, reducing page loads and rate-limit exposure compared to monolithic profile scraping.
vs alternatives: Avoids repeated login flows and detection triggers that plague generic LinkedIn scrapers by leveraging persistent authenticated sessions and Patchright's anti-detection hardening, making it more reliable for long-running AI agent workflows than REST API wrappers or basic Selenium-based scrapers.
Retrieves comprehensive company data from LinkedIn including overview, employees, recent feed posts, and company metadata through MCP tools. Implements the same 'one-section-one-navigation' pattern as person profiles, where each company section (overview, employees, feed) maps to a specific URL. Uses Patchright browser automation to parse company pages and extract structured data without triggering rate limits or detection.
Unique: Applies the same 'one-section-one-navigation' architecture to company pages, allowing Claude to request only specific company sections (overview, employees, feed) rather than loading entire company profiles. This minimizes page loads and detection risk while enabling granular data extraction tailored to the AI's actual information needs.
vs alternatives: More efficient than monolithic company scraping tools because it maps each data type to a discrete navigation action, reducing unnecessary page loads and rate-limit exposure. Patchright-based automation is more resilient to LinkedIn's anti-bot mechanisms than generic web scraping libraries.
Provides Docker and docker-compose configurations for containerized deployment of the LinkedIn MCP server. Enables users to run the server in isolated containers with predefined dependencies, environment variables, and volume mounts for profile persistence. Supports both standalone Docker runs and multi-container orchestration via docker-compose, simplifying deployment across different environments (local, cloud, CI/CD).
Unique: Provides production-ready Dockerfile and docker-compose configurations that abstract away Python dependency management and enable containerized deployment. Includes volume mount configurations for persistent profile storage, allowing authentication state to survive container restarts.
vs alternatives: More portable than native Python deployment because it eliminates Python version and dependency conflicts. More scalable than local deployment because it enables horizontal scaling via container orchestration platforms.
Integrates with Claude Desktop through a manifest.json file that registers the LinkedIn MCP server as a tool provider. The manifest defines tool schemas (input parameters, output types) and server connection details, enabling Claude Desktop to discover and invoke LinkedIn tools. Uses Claude Desktop's native MCP client to communicate with the server via stdio or network sockets.
Unique: Integrates with Claude Desktop through a manifest.json file that declares tool schemas and server connection details, enabling Claude Desktop's native MCP client to discover and invoke LinkedIn tools without custom integration code. Manifest-based registration is the standard MCP pattern for tool discovery.
vs alternatives: More integrated than manual tool configuration because Claude Desktop automatically discovers tools from the manifest. More maintainable than hardcoded tool lists because schema changes are centralized in manifest.json.
Implements a 'one-section-one-navigation' design pattern where each data section (person work history, company overview, job details) maps to exactly one URL. This allows Claude to request only the specific data it needs without loading entire profiles or pages. Reduces page loads, minimizes rate-limit exposure, and improves reliability by limiting the DOM parsing surface area. Each tool corresponds to a discrete navigation action, enabling granular data fetching.
Unique: Implements a deliberate architectural pattern where each data section maps to exactly one URL/navigation action, allowing Claude to request only needed data without loading entire profiles. This design minimizes page loads, reduces DOM parsing overhead, and limits the attack surface for LinkedIn's bot detection, making it more efficient and reliable than monolithic profile scraping.
vs alternatives: More efficient than monolithic scraping because it avoids loading unnecessary data. More reliable than full-page scraping because it limits DOM parsing to specific sections, reducing the risk of selector breakage when LinkedIn updates page layouts.
Enables Claude to search LinkedIn job listings with filters (keywords, location, experience level, job type, salary range) and retrieve detailed job information by ID. Implements structured search parameters that map to LinkedIn's search API query format, allowing the AI to construct filtered job searches without manual URL manipulation. Returns job metadata including title, company, location, salary, description, and application requirements.
Unique: Exposes LinkedIn job search as structured MCP tools with filter parameters (location, experience level, job type, salary) that map directly to LinkedIn's search query format, allowing Claude to construct filtered searches programmatically. Separates search (list results) from detail retrieval (fetch full job posting by ID) to optimize for both discovery and deep analysis workflows.
vs alternatives: More flexible than static job board integrations because it allows Claude to dynamically construct searches with multiple filters. More reliable than REST API wrappers because it uses authenticated browser automation, avoiding LinkedIn API rate limits and authentication barriers.
Retrieves LinkedIn inbox conversations and enables message search across threads. Implements conversation listing (fetching recent inbox threads) and message search (finding specific messages within conversations). Uses Patchright to navigate LinkedIn's messaging interface and extract conversation metadata (participants, timestamps, message content). Maintains conversation threading context for multi-turn message analysis.
Unique: Exposes LinkedIn's messaging interface as MCP tools with both conversation listing and message search capabilities, maintaining thread context for multi-turn analysis. Uses Patchright to navigate the JavaScript-heavy messaging UI, which is more reliable than attempting to reverse-engineer LinkedIn's internal messaging API.
vs alternatives: Provides conversation threading and search that generic email-to-LinkedIn bridges cannot offer. More reliable than REST API approaches because it uses authenticated browser automation, avoiding LinkedIn's strict API restrictions on messaging access.
Enables Claude to send LinkedIn connection requests programmatically, optionally including personalized messages. Implements form submission via Patchright to navigate LinkedIn's connection request flow, including message composition and submission. Handles LinkedIn's rate limiting and connection request validation (e.g., preventing duplicate requests to the same person).
Unique: Automates LinkedIn connection requests with optional personalized messages through MCP, allowing Claude to integrate networking into multi-step workflows. Uses Patchright to handle LinkedIn's form submission and validation, respecting rate limits and preventing duplicate requests through client-side state tracking.
vs alternatives: More integrated than manual LinkedIn outreach because it's callable from Claude workflows. More reliable than LinkedIn API approaches because LinkedIn's official API does not support connection requests; Patchright-based automation is the only viable approach.
+5 more capabilities
Crawls 11+ heterogeneous platforms (Zhihu, Weibo, Bilibili, Twitter, Reddit, HackerNews, etc.) and RSS feeds using platform-specific scrapers, normalizes disparate data schemas into a unified NewsItem model, and deduplicates content across sources using fuzzy title matching and URL canonicalization. The system maintains platform-specific metadata (rank, heat value, engagement metrics) while presenting a single normalized feed, enabling cross-platform trend detection that would be invisible within individual platform silos.
Unique: Implements platform-specific crawler modules with unified NewsItem schema and fuzzy deduplication across 11+ heterogeneous sources (Chinese + international), rather than relying on single-platform APIs or generic RSS parsing. Maintains platform-specific metadata (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) for weighted hotspot scoring.
vs alternatives: Covers more platforms (especially Chinese social media) with deeper metadata extraction than generic RSS aggregators, and provides unified deduplication across sources unlike single-platform monitoring tools.
Implements a multi-stage filtering pipeline that matches news items against user-defined keywords using regex patterns, required word lists, and excluded word lists. The system applies frequency-based scoring (keyword occurrence count) combined with platform hotspot weights to rank filtered results. Configuration is stored in frequency_words.txt with support for regex patterns, AND/OR/NOT boolean operators, and per-keyword weighting. Filtering occurs at collection time (reducing storage) and again at report generation time (enabling dynamic reconfiguration without re-crawling).
Unique: Combines regex pattern matching with frequency-based scoring and platform hotspot weighting (rank × 0.6 + frequency × 0.3 + platform hot value × 0.1) in a two-stage pipeline (collection-time and report-time filtering). Supports dynamic reconfiguration without re-crawling by applying filters at report generation.
TrendRadar scores higher at 58/100 vs linkedin-mcp-server at 38/100. linkedin-mcp-server leads on quality, while TrendRadar is stronger on adoption and ecosystem.
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vs alternatives: More flexible than simple keyword matching (supports regex and boolean logic) and more efficient than semantic filtering (no LLM overhead), making it suitable for real-time filtering at scale.
Detects newly emerged topics by comparing current crawl results against historical data stored in the system. Topics are marked as 🆕 (new) if they appear for the first time in the current crawl or if their hotspot rank increased significantly compared to previous crawls. The system tracks topic emergence velocity (how quickly a topic rises in rankings) and flags topics with unusual acceleration. New topic detection is performed at report generation time, enabling dynamic detection without re-crawling. The system maintains a historical hotspot index for comparison.
Unique: Detects new topics by comparing current hotspot rankings against historical data, marking topics with significant rank increases as 🆕. Tracks emergence velocity to distinguish breaking news from sustained trends.
vs alternatives: More efficient than semantic similarity detection (no LLM overhead) and more accurate than simple first-appearance detection (accounts for re-emerging topics), but requires historical baseline data.
Provides a web-based UI for editing TrendRadar configuration files (config.yaml, frequency_words.txt, timeline.yaml) with real-time validation and preview. The editor supports: (1) syntax highlighting for YAML and regex, (2) validation of keyword patterns (regex compilation check), (3) preview of filtered results based on current keyword configuration, (4) drag-and-drop channel configuration, (5) schedule preview (shows next 10 execution times). Changes are validated before saving, preventing configuration errors. The editor is optional; users can edit config files directly.
Unique: Provides web-based configuration editor with real-time validation, regex preview, and schedule visualization. Enables non-technical users to configure TrendRadar without editing YAML files.
vs alternatives: More user-friendly than manual YAML editing and provides validation feedback, but adds operational complexity compared to file-based configuration.
Integrates LiteLLM to provide vendor-agnostic AI analysis and summarization of filtered news items. Users configure their preferred LLM provider (OpenAI, Anthropic, Ollama, local models, etc.) once in config.yaml, and the system automatically routes analysis requests to that provider. The AI analysis capability includes: (1) automated summarization of long articles into key points, (2) sentiment analysis (positive/negative/neutral), (3) trend prediction based on historical patterns, and (4) custom analysis prompts. Analysis results are cached to avoid redundant API calls and can be pushed directly to notification channels.
Unique: Uses LiteLLM abstraction layer to support any LLM provider (OpenAI, Anthropic, Ollama, local models) with single configuration, enabling provider switching without code changes. Caches analysis results to reduce redundant API calls and costs.
vs alternatives: More flexible than hardcoded OpenAI integration (supports any LiteLLM provider) and cheaper than dedicated sentiment analysis APIs (can use local models), but slower than rule-based sentiment analysis.
Leverages LiteLLM to translate news content from source languages (primarily Chinese) to target languages (English, etc.) on-demand. The system detects source language automatically (via langdetect or similar), caches translations to avoid re-translating identical content, and batches translation requests to reduce API calls. Translations are stored alongside original content, enabling bilingual reports and multi-language notification delivery. Translation can be triggered at collection time (all news) or report time (only filtered news).
Unique: Implements provider-agnostic translation via LiteLLM with automatic language detection, content-based caching, and batch request optimization. Stores translations alongside originals for bilingual report generation.
vs alternatives: More flexible than dedicated translation APIs (supports any LiteLLM provider) and cheaper than commercial translation services when using local models, but slower than specialized translation APIs.
Implements a notification abstraction layer supporting 9+ delivery channels (WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc.). Each channel has a provider-specific formatter that converts normalized news items into channel-appropriate messages (e.g., WeChat card format, Telegram markdown, email HTML). The system batches notifications atomically—all news items for a report are sent as a single batch to each channel, ensuring consistency and reducing API calls. Message formatting respects channel constraints (character limits, attachment limits, etc.) and supports templating for customization.
Unique: Implements atomic message batching across 9+ heterogeneous channels with provider-specific formatters and constraint-aware truncation. Single configuration enables simultaneous delivery to WeChat, WeWork, Feishu, Telegram, Email, ntfy, Bark, Slack, etc. without code changes.
vs alternatives: Supports more channels (especially Chinese platforms like WeWork, Feishu) than generic notification services, and batching reduces API calls and spam compared to per-item notifications.
Exposes TrendRadar's data and analysis capabilities as an MCP server, enabling AI agents and LLM applications to query trends, perform analysis, and generate insights through natural language. The MCP server implements tools for: (1) querying filtered news by keyword/date/platform, (2) retrieving trend statistics and hotspot rankings, (3) running custom analysis on news subsets, (4) generating reports in various formats. Clients (Claude, other LLM agents) can invoke these tools via MCP protocol, enabling conversational exploration of trends without direct database access. The server maintains state across multiple requests, allowing multi-turn conversations about trends.
Unique: Implements full MCP server exposing trend data and analysis tools to LLM agents, enabling conversational queries and multi-turn analysis workflows. Maintains state across requests and supports complex tool invocations (filtering, analysis, report generation).
vs alternatives: Enables conversational access to trends (vs. API-only access) and integrates with LLM agent workflows (vs. standalone tools), but adds operational complexity compared to simple REST APIs.
+4 more capabilities