Shared semantic cache for AI agents to reuse verified research answers and reduce redundant token usage.
Quick Install
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Manual Client & Custom JSON ConfigExpand JSON ▾
Client Config & Setup
Choose your client or environment
Target File:
💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Overview
Use cases
Reuse prior AI agent research to save tokens
Improve answer accuracy with verified public sources
Share research results across developer sessions
Reduce redundant web searches in long conversations
Maintain up-to-date cached knowledge with freshness checks
Key features
Semantic caching of AI research queries
Instant answers from verified sources on cache hits
Partial research to fill gaps on partial hits
Automatic saving of new research summaries
Freshness policies by topic volatility
Multiple privacy layers to sanitize and reject sensitive data
Capabilities & Tool Schemas
Inspect callable tools, capabilities, and parameters exposed to AI agents by Wellread.
Extracted Tool Capabilities
Semantic caching of AI research queries
Instant answers from verified sources on cache hits
Partial research to fill gaps on partial hits
Automatic saving of new research summaries
Freshness policies by topic volatility
Multiple privacy layers to sanitize and reject sensitive data
Documentation Overview
wellread - Another dev already searched that.
Your agent's next research task was probably already solved. Wellread finds it before your agent burns tokens rediscovering it - and when it can't, it makes sure the next dev doesn't pay that cost either.
Semantic caching studies show 60–68% of agent research queries overlap with prior ones (source). And AI-driven live web searches grew 15x in 2025 (Cloudflare). Wellread is the cache that layer has been missing.
The compounding effect
| Without wellread | With wellread | |
|---|---|---|
| Turn 1 (fresh session) | 200K tokens · 10 turns · 67s | 647 tokens · 1 turn · 28s |
| Turn 30 (~40K context) | 1.2M tokens | 647 tokens |
| Turn 100 (~150K context) | 3.5M tokens | 647 tokens |
| Turn 250 (~480K context) | 11M tokens | 647 tokens |
The deeper your session, the more expensive research gets - and the more wellread saves.
The problem
- Your agent researches every technical question from scratch. When it doesn't, it hallucinates - outdated APIs, wrong examples, broken code.
- Every turn re-sends the whole conversation. By turn 100, you've paid for the same context a hundred times.
The fix
Before your agent hits the web, wellread checks what other devs already found.
- Hit → instant answer from verified sources. Zero web searches. One turn.
- Partial → starts from what exists, only researches the gaps.
- Miss → normal research, then saves the summary for whoever comes next.
Your agent doesn't just spend fewer tokens. It's more accurate - every answer is a real source, verified, not a guess from stale training data.
Install
Restart your editor. That's it.
Update: npx wellread@latest - Uninstall: npx wellread uninstall
Singleplayer from day one
You don't need a crowd for wellread to pay off.
Singleplayer - your own research comes back to you. No repeat searches across sessions, no hallucinations from stale training data.
Multiplayer - when another dev has already cracked that Auth.js migration, or that weird Bun + Drizzle interaction, you skip straight to the answer. One person researches, everyone benefits.
Early users build the network. Their contributions get credited - and permanent.
Freshness
Each entry knows how fast its topic changes:
| Type | Fresh | Re-check | Re-research |
|---|---|---|---|
| Timeless (TCP, SQL basics) | 1 year | - | after |
| Stable (React, PostgreSQL) | 6 months | 1 year | after |
| Evolving (Next.js, Bun) | 30 days | 90 days | after |
| Volatile (betas, pre-release) | 7 days | 30 days | after |
When an agent re-verifies, the clock resets for everyone.
Privacy
Six layers between your private context and the shared network:
- Hook instruction - before anything leaves your machine, the hook tells your agent to sanitize the query: strip project names, API keys, file paths, credentials. Only the generic technical concept is sent.
- Search schema - the search tool's parameter description reinforces: "Remove project names, API keys, file paths, credentials."
- Save schema - the save tool explicitly says: "NEVER include project/repo/company names, internal URLs, file paths, credentials, business logic. Content is PUBLIC."
- URL gate (server, hard reject) - every source must start with
https://orhttp://. File paths, library identifiers, internal URLs → rejected. The contribution is not saved. - Path detection (server, hard reject) - the server scans content and search surface for local paths (
/Users/...,/home/...,file://,C:\...). If found → rejected. - By design - your agent doesn't forward your input. It synthesizes from public sources. What gets saved is a distilled summary of public docs, not your code or conversation.
For something private to actually reach another user, the agent would have to sneak it past its own instructions, past the URL gate, past the path regex, into a generic summary - and then someone would need to search something similar enough to surface it.
Stats
Ask your agent:
"show me my wellread stats"
See your token savings, your top contributions, and how many devs used research you saved.
Supported tools
Works with any MCP client. Best experience with Claude Code. Also supports Cursor, Windsurf, Gemini CLI, VS Code, OpenCode.
Links
License
Related MCP Servers
Adoption & maintenance
Factual signals from GitHub, npm, and our automated checks — not a rating.
GitHub stars
9
Stargazers on the source repository.
Last commit
4mo ago
Most recent push to the default branch.
Reviews
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Frequently Asked Questions about Wellread
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CategoryKnowledge & Memory
47Quality signal: Fair · 47/100How this signal is calculated ▾
Server availabilityNot measured
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Verified ownership10/20
Documentation & tools22/30
Adoption & activity3/15
Community engagement0/10
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Scanned 14d ago via OSV.dev · wellread (npm)
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