AI Boost
Summary
AI Boost's MCP server converts developer expertise into structured, reusable context for compatible LLM agents.
AI Boost runs an MCP server that records conventions, rules, and domain knowledge, then supplies them to agents as targeted context on demand. It targets the common failure mode where agents depend on scattered chat history instead of consistent guidance. Data remains private and is never used for training. The service is freemium, yet specific paid-tier pricing is not published. Performance requires both MCP support in the agent and ongoing human curation of the stored boosters.
Bottom line: *Use when teams need consistent rules across agent sessions; skip if agents lack MCP support or curation effort is low.*
Pricing Plans
Usage-Based- Free Tier
- Private boosters are free. Paid tiers inferred but not explicitly documented in vendor content.
Personal (Private Boosters)
Private boosters for personal use at no cost; never indexed or shared publicly.
- Private booster creation and storage
- Semantic and keyword indexing
- Auto-suggest on relevant tasks
- Instant deletion of boosters or account
View full pricing on ai-boost.io →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Solves real gap: agents get curated, structured expertise instead of noisy memory replays
- Private by default with strong commitments: never indexed, sold, or used for training
- Seamless integration: one MCP config, works across all agent clients and projects
- Semantic + keyword indexing ensures correct boosters surface at the right moments
Cons
Sign in to edit- Pricing model and tiers not clearly communicated on the vendor site
- Requires agents to support MCP protocol (limits compatibility to newer tools)
- Booster quality and relevance depend on human curation; poor capture = poor suggestions
About
- Platforms
- Web (MCP server), integrates with Cursor, Claude Code, and generic MCP clients
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-06-08T08:41:56.286Z
Best For
Who it's for
- Development teams using agentic coding assistants (Cursor, Claude Code)
- Organizations standardizing on conventions and governance rules for AI agents
- Domain experts wanting to codify and distribute specialized knowledge
- Teams managing multi-session, stateful AI-assisted development workflows
What it does well
- Maintaining consistent auth patterns and security conventions across multi-repo agent workflows
- Capturing domain expertise (business rules, API gotchas, deployment procedures) for team reuse
- Reducing repetition by auto-suggesting saved workflows when agents detect similar contexts
- Building shared knowledge libraries of coding standards and best practices for teams
Integrations
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Sign Up to ContributeFrequently Asked Questions
- Is AI Boost free?
- AI Boost has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is AI Boost open source?
- No — AI Boost is a closed-source tool. Source code is not publicly available.
- Does AI Boost have an API?
- Yes. AI Boost exposes a developer API. See the official documentation at https://ai-boost.io for details.
- What platforms does AI Boost support?
- AI Boost is available on: Web (MCP server), integrates with Cursor, Claude Code, and generic MCP clients.
Curated lists that include this category
AI Boost's MCP server converts developer expertise into structured, reusable context for compatible LLM agents.
AI Boost runs an MCP server that records conventions, rules, and domain knowledge, then supplies them to agents as targeted context on demand. It targets the common failure mode where agents depend on scattered chat history instead of consistent guidance. Data remains private and is never used for training. The service is freemium, yet specific paid-tier pricing is not published. Performance requires both MCP support in the agent and ongoing human curation of the stored boosters.
*Use when teams need consistent rules across agent sessions; skip if agents lack MCP support or curation effort is low.*