Agishub MCP vs MCP Local Rag
A side-by-side look at the Agishub MCP and MCP Local Rag 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 MCP Local Rag 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 MCP Local Rag 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 have the keys it needs:
BASE_DIR, BASE_DIRS, IGNORE_PATHS, DB_PATH, CACHE_DIR, HF_ENDPOINT. - You want tools like
sync_start, sync_status, ingest_file.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Knowledge & Memory | Knowledge & Memory |
| Quality signal | 53/100 · Good | 69/100 · Great |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | No auth required |
| Pricing | Not declared | Free |
| Required env vars | None listed | BASE_DIRBASE_DIRSIGNORE_PATHSDB_PATHCACHE_DIRHF_ENDPOINT |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 416 |
| Directory activity | 0 views 0 installs 0 upvotes | 12 views 0 installs 0 upvotes |
| Tools | 34 | 9 |
| Full listing | View Agishub MCP | View MCP Local Rag |
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
MCP Local Rag tools 9
sync_startReconcile the index with all configured roots or one pathsync_statusPoll a running sync jobingest_fileIngest or replace one fileingest_dataIngest text, Markdown, or HTML already held by the clientquery_documentsSearch with semantic matching and keyword boostread_chunk_neighborsRead surrounding chunks from a search resultlist_filesShow supported files and their ingestion statedelete_fileDelete an indexed file or an `ingest_data` itemstatusShow index and search status
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"
]
}
}
}
MCP Local Rag
{
"mcpServers": {
"shinpr-mcp-local-rag": {
"command": "npx",
"args": [
"-y",
"mcp-local-rag"
],
"env": {
"BASE_DIR": "YOUR_BASE_DIR_HERE",
"BASE_DIRS": "YOUR_BASE_DIRS_HERE",
"IGNORE_PATHS": "YOUR_IGNORE_PATHS_HERE",
"DB_PATH": "YOUR_DB_PATH_HERE",
"CACHE_DIR": "YOUR_CACHE_DIR_HERE",
"HF_ENDPOINT": "YOUR_HF_ENDPOINT_HERE"
}
}
}
}
Frequently asked questions
Agishub MCP is categorized under Knowledge & Memory and uses a local stdio subprocess. MCP Local Rag belongs to Knowledge & Memory and uses a local stdio subprocess. Pick Agishub MCP for knowledge & memory work and MCP Local Rag 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. MCP Local Rag is listed as free. 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 shinpr-mcp-local-rag 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. MCP Local Rag has 69/100 (Great) with 12 views, 0 installs and 416 GitHub stars.