Agishub MCP vs Screenpipe
A side-by-side look at the Agishub MCP and Screenpipe MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Agishub MCP for knowledge & memory work as a local process; choose Screenpipe for knowledge & memory as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Agishub MCP when
- You need dedicated capabilities in the Knowledge & Memory domain.
- You prefer a local stdio subprocess transport.
- You want tools like
ai.chat, ai.classify, ai.embed.
Choose Screenpipe when
- You need dedicated capabilities in the Knowledge & Memory domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
search-content, synced-devices, search-synced-content.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Knowledge & Memory | Knowledge & Memory |
| Quality signal | 53/100 · Good | 72/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | No auth required |
| Pricing | Not declared | Freemium |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 22,033 |
| Directory activity | 0 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 34 | 30 |
| Full listing | View Agishub MCP | View Screenpipe |
Tools & capabilities
Agishub MCP tools 34
ai.chatAsk a general-purpose LLM a question or give it an instruction, with an optional system prompt. No external API key required.ai.classifyClassify a text into exactly one of the candidate labels you provide (e.g. sentiment, topic, intent).ai.embedTurn text into a numeric embedding vector for semantic search, RAG and similarity. Multilingual.ai.extractExtract named entities from text — people, organizations, locations, dates and miscellaneous — returned as structured JSON.ai.summarizeSummarize a block of text into a short abstract, with an optional target length. No external API key required.web.browserDrive a headless browser: open a URL and run an ordered list of steps — click, type, press keys, wait, extract text and screenshot. For flows the plain scraper can't reach (logins, forms, multi-step pages).web.crawlCrawl multiple pages of a site, respecting link depth and domain limits. Returns async job_id; results include markdown or HTML per page. Use it to fetch and process many pages of content at once.web.extractFetch any public web page and return its main content as clean, token-efficient Markdown (title, description, headings, links, lists). Set render:true to execute JavaScript first for single-page apps or JS-heavy pages that would otherwise come back empty. Built for RAG and for agents that need to r…web.extract_structuredAI-powered structured extraction: give a URL plus a natural-language prompt and/or a JSON Schema, and get back clean structured JSON (e.g. product name, price, rating). Renders the page in a headless browser first, so it works on SPAs.web.linksReturn every hyperlink on a JavaScript-rendered page as a list of absolute URLs, with options to keep only visible links or only same-site links. Backed by a headless browser. Use it to map a site or seed a crawler.web.mapDiscover all URLs reachable from a domain within a link depth limit. Returns a flat list of absolute URLs, respects robots.txt crawl delays. Use it to map a site's structure before crawling.web.scrapeExtract specific elements from a JavaScript-rendered page by CSS selector. Give a list of selectors (e.g. 'h1', '.price', 'a.product') and get back the text and attributes of every match. Backed by a headless browser, so it works on SPAs and JS-heavy pages.- +22 more on the listing page
Screenpipe tools 30
search-contentSearch screen text, audio transcriptions, input events, memories, and parsed app data. Returns timestamped results with app context. USE WHEN: you need the actual text/content of a moment — quotes, screen text, transcript lines, or compact parsed messages, emails, tasks, documents, and code review — or want to filter by speaker/window. DO NOT USE for: broad questions like 'what was I doing?' (use activity-summary, it pre-summarizes apps + windows + transcripts). Also DO NOT USE for: targeted UI controls (use search-elements). Start with limit=5, increase only if needed. Per-result text is auto-truncated to 1000 chars; pass max_content_length=0 to opt out, or a custom integer to override.synced-devicesList this signed-in user's Screenpipe devices that have uploaded Data Sync records, including each device name and last sync time. USE WHEN: the user asks what devices are available, names another device, or asks a cross-device question and you need the exact device_name filter. This never accepts an account or bucket identifier; the local app forwards the signed-in user's identity.search-synced-contentSearch Data Sync records from this signed-in user's devices. Results include device name, device ID, and timestamp for attribution. USE WHEN: the user asks about another/named device, asks across devices, or local search does not cover the requested machine. For the current machine only, use search-content. Start with a narrow time range and limit=10.list-meetingsList detected meetings (Zoom, Teams, Meet, etc.) with id, duration, app, attendees, and note status. Pass `q` to substring-match title, attendee names/emails, and notes — `q` searches ALL meeting history, so when looking for a meeting with a person or on a topic ('when did I last talk to Noah?'), pass `q` and OMIT start_time. Only constrain the time range when the question itself is time-bound. Results are newest-first; without `q`, old meetings only surface via time range or offset pagination. Follow up with get-meeting (id from results) for the full note and transcript.activity-summaryRich activity overview: authoritative active minutes, app/window time, edited document paths, key text, and audio transcriptions, with optional parsed task context when available. USE WHEN: any broad question about what the user did — 'what was I doing?', 'how long on X?', 'which apps?', 'recap my morning'. This is almost always the right first call for time-range questions — usually sufficient without follow-up searches. Use parsed/path evidence to identify tasks, but only active-minute fields for duration; frame and row counts are never time. DO NOT USE for: finding a specific keyword (use keyword-search) or a specific UI control (use search-elements).search-elementsSearch UI elements (buttons, links, text fields) from the accessibility tree, filterable by role. USE WHEN: you want a specific UI control or page-structure question — 'find every Submit button I saw', 'list the links in that page'. DO NOT USE for: general text/content (use search-content) or fast keyword lookup (use keyword-search).frame-contextGet full accessibility text, parsed tree nodes, and URLs for a specific frame ID. Use after search-content to get detailed context for a specific moment.export-videoExport an MP4 of screen recordings for a time range, with synced microphone audio. Frames are placed at their real timestamps, so the clip's duration matches the wall-clock span you requested (not a sped-up timelapse). Returns the file path. Can take a few minutes for long ranges.update-memoryCreate, update, or delete a persistent memory (facts, preferences, decisions the user wants to remember). To retrieve memories, use search-content with content_type='memory'. To create: provide content + tags. To update: provide id + fields to change. To delete: provide id + delete=true.get-feedbackSearch local user ratings and written comments attached to AI-produced notifications, chats, memories, blocks, artifacts, and other targets. Use before generating related work so you preserve what earned up ratings and correct what earned down ratings.send-notificationSend a notification to the screenpipe desktop UI. Use high priority only for time-sensitive failures or decisions needing human attention; routine findings and completed tasks should be normal or low.health-checkCheck if screenpipe is running and healthy. Returns recording status, frame/audio stats, timestamps.- +18 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Agishub MCP
{
"mcpServers": {
"agent-memory": {
"command": "npx",
"args": [
"-y",
"@agishub/mcp"
]
}
}
}
Screenpipe
{
"mcpServers": {
"screenpipe": {
"command": "npx",
"args": [
"-y",
"screenpipe-mcp@latest"
]
}
}
}
Frequently asked questions
Agishub MCP is categorized under Knowledge & Memory and uses a local stdio subprocess. Screenpipe belongs to Knowledge & Memory and uses a local stdio subprocess. Pick Agishub MCP for knowledge & memory work and Screenpipe for knowledge & memory.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Agishub MCP has no declared pricing — check the project's own site. Screenpipe is listed as freemium — a free tier with paid plans. Auth: no auth required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add agent-memory and screenpipe as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Agishub MCP has a quality signal of 53/100 (Good) with 0 views, 0 installs and 0 GitHub stars. Screenpipe has 72/100 (Great) with 3 views, 0 installs and 22,033 GitHub stars.