MCP Everything Server vs MCP Memory Server

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	MCP Everything Server MCP Servers	MCP Memory Server MCP Servers
Tagline	The kitchen-sink reference MCP server that exercises every corner of the Model Context Protocol	Persistent knowledge-graph memory for Claude and other MCP clients
Category	MCP Servers	MCP Servers
Pricing	Free· Free and open source (MIT License). No cloud service or paid tier — you run it locally via npx, Docker, or your MCP client of choice.	Free· Free and open source (MIT). Self-hosted; no vendor charges. Runs locally via npx or Docker.
Model	—	—
Editorial score	—	—
Use cases	MCP client conformance testingRegression testing of stdio and Streamable HTTP transportsVerifying sampling round-trip behavior in a new agent hostDebugging elicitation UI in an IDE integrationReference reading for authoring a new MCP serverDemoing MCP primitives in workshops and talksSmoke-testing cancellation and progress-notification handlingValidating resource-subscription update delivery	Persistent Claude Desktop memory across chatsLong-lived coding-agent project memoryLightweight personal CRM of people and companiesResearch-agent scratch knowledge graphCross-session preference and style memoryTeam convention and past-bug recall for coding assistantsStructured note-taking backend for MCP clientsLocal-first alternative to hosted memory APIs
Pros	Only server that exercises the full MCP feature matrix in one place — tools, resources, prompts, sampling, elicitation, roots, logging, subscriptions, and Tasks Maintained by the Model Context Protocol project itself, so behavior tracks the spec as it evolves (SEP-1686 Tasks, Streamable HTTP, etc.) Runs anywhere an MCP client runs — npx, Docker, Claude Desktop, VS Code, Cursor, Windsurf — with stdio or HTTP transports TypeScript source is short and readable, making it a de-facto reference for how each handler should be shaped MIT-licensed, no telemetry, no signup, no cloud dependency Includes progress notifications and cancellation flows that most tutorial servers skip, so client cancel/timeout logic can be exercised properly	Official, Anthropic-maintained reference implementation - the canonical way to add persistent memory to an MCP client Zero-config install via npx or a one-line Docker command; works out of the box with Claude Desktop's claude_desktop_config.json Simple, inspectable JSONL storage on disk that you can grep, diff, back up and edit by hand Structured entity/relation/observation model is more queryable than a raw text scratchpad and cheaper than a vector DB Nine well-scoped tools plus a live-updating knowledge-graph Resource, so agents can both read and mutate memory Fully open source (MIT) and vendor-neutral - runs against any MCP-speaking model, not just Claude Trivial to fork or wrap for team-specific schemas since the codebase is a single small TypeScript file
Cons	Explicitly not useful for end users — it does not do anything a human would actually want done Feature drift means some primitives (SEP-1686 Tasks, elicitation) may not yet be implemented in every client, producing red herrings during testing Documentation is a single features.md; there is no guided tour that maps each tool to the spec section it exercises TypeScript-only reference — Python or Rust client authors have to translate patterns themselves Sampling and elicitation flows depend on the client honoring them, so a silent client makes it hard to tell whether the server or the client is at fault	Reference-quality, not production-grade: single-file JSONL storage with no concurrency control, indexing or replication search_nodes is a plain substring match with no embeddings or semantic ranking - large graphs degrade quickly No built-in multi-user, auth or per-project isolation; a shared install mixes memories from every session The model still has to be prompted to actually call the memory tools - forgetful assistants forget to remember No web UI, visualisation or admin surface; you inspect and clean the graph by editing the JSONL yourself Only a local filesystem backend - no Postgres, SQLite or cloud sync option is shipped
Website	github.com	github.com

Pick MCP Everything Server if

✅ Only server that exercises the full MCP feature matrix in one place — tools, resources, prompts, sampling, elicitation, roots, logging, subscriptions, and Tasks
✅ Maintained by the Model Context Protocol project itself, so behavior tracks the spec as it evolves (SEP-1686 Tasks, Streamable HTTP, etc.)
✅ Runs anywhere an MCP client runs — npx, Docker, Claude Desktop, VS Code, Cursor, Windsurf — with stdio or HTTP transports
✅ TypeScript source is short and readable, making it a de-facto reference for how each handler should be shaped

Pick MCP Memory Server if

✅ Official, Anthropic-maintained reference implementation - the canonical way to add persistent memory to an MCP client
✅ Zero-config install via npx or a one-line Docker command; works out of the box with Claude Desktop's claude_desktop_config.json
✅ Simple, inspectable JSONL storage on disk that you can grep, diff, back up and edit by hand
✅ Structured entity/relation/observation model is more queryable than a raw text scratchpad and cheaper than a vector DB

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