Agishub MCP vs Pageindex MCP
A side-by-side look at the Agishub MCP and Pageindex MCP 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 Pageindex MCP 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 Pageindex MCP when
- You need dedicated capabilities in the Knowledge & Memory domain.
- You prefer a local stdio subprocess transport.
- You want tools like
process_document, browse_documents, search_documents.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Knowledge & Memory | Knowledge & Memory |
| Quality signal | 53/100 · Good | 63/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 403 |
| Directory activity | 0 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 34 | 9 |
| Full listing | View Agishub MCP | View Pageindex MCP |
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
Pageindex MCP tools 9
process_documentUpload and process PDF documents from URLs or local files. Supports OCR processing, hierarchical content extraction, and intelligent document analysis. Returns a unique doc_id for subsequent operations. Processing typically takes 0-3 minutes depending on document size (estimate: 2 seconds per page). Supports files up to 100MB.browse_documentsPrimary document retrieval tool. After orienting with get_folder_structure() (when available), use this for all document-related questions. The bare call returns root-level sub-folders and documents; pass folder_id to drill into a sub-folder level by level. Use sort="relevance" + query for semantic ranking. Do NOT jump to search_documents() first — it is an escalation path, only after browse_documents(sort="relevance") has failed.search_documentsESCALATION tool — never the first step. Use only after `browse_documents(sort="relevance", query=...)` missed a document you strongly believe exists by precise term, acronym, or file-name fragment. Query must be keywords only — see the `query` schema describe. Each result has a `score` (6-10, higher is more relevant). On result: single best match → read it; several equally good → ask user; none → fall back to `browse_documents(sort="relevance")`.get_folder_structureOrientation step: show the folder hierarchy as a tree (like `tree -d`). **Call this before browse_documents()** to plan targeted retrieval — folder names reveal content domains (e.g. "Research", "Finance") and return folder IDs you pass to browse_documents(folder_id=…). Wait for this result before issuing browse_documents() or search_documents(). For folder_id="root" (default), returns the entire folder tree; for a specific folder_id, returns that subtree only.get_documentCheck a document's processing status and metadata. `status` is one of "pending", "queued", "processing", "completed", or "failed" — call this before `get_document_structure()` or `get_page_content()` to confirm the document is ready.get_document_structureExtract a document's hierarchical outline (headers, sections, page references). REQUIRED for documents over 20 pages — call this first to locate relevant sections, then pass their page numbers to `get_page_content()`. Use the `part` parameter to iterate large outlines until `pagination.has_more` is false.get_page_contentExtract page content from a processed document. Use tight, targeted page ranges — never the whole document at once. For documents over 20 pages, call `get_document_structure()` first to pick relevant sections. Embedded image paths in the response feed into `get_document_image()`.get_document_imageRetrieve an image from a document — pass an `image_path` from a `get_page_content()` response: either an embedded image (from a content block) or a full rendered page (from the `page_images` array, useful when a page is scanned or its meaning depends on visual layout). Returns the image as an MCP image content block (base64 data + MIME type). Do NOT repeat the raw base64 value in your text response.remove_documentPermanently delete documents and all associated data. Only invoke when the user explicitly names the documents AND confirms deletion. Returns `results` — one entry per requested document: `{ doc_name, status: "deleted" | "not_found" | "failed", error? }`. Inspect each entry for per-document failures. This action is irreversible.
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"
]
}
}
}
Pageindex MCP
{
"mcpServers": {
"pageindex-mcp": {
"command": "npx",
"args": [
"-y",
"@pageindex/mcp"
]
}
}
}
Frequently asked questions
Agishub MCP is categorized under Knowledge & Memory and uses a local stdio subprocess. Pageindex MCP belongs to Knowledge & Memory and uses a local stdio subprocess. Pick Agishub MCP for knowledge & memory work and Pageindex MCP 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. Pageindex MCP has no declared pricing — check the project's own site. 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 pageindex-mcp 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. Pageindex MCP has 63/100 (Good) with 3 views, 0 installs and 403 GitHub stars.