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ClariLayer

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Individual-analyst context layer over MCP: bootstrap, reconcile, and remember your data context.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "clarilayer": {
      "command": "npx",
      "args": [
        "-y",
        "clarilayer"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ’» More in Developer Tools

Documentation Overview

MseeP.ai Security Assessment Badge

ClariLayer

ClariLayer

Stop re-explaining your data to your AI every session.

The individual-analyst context layer, delivered over MCP.
Connect it to Claude Code, Cursor, Codex, or claude.ai β€” your agent stops making the same data mistakes.

Website Β· Docs Β· Get started (free) Β· Use cases

MCP server Clients Free for individuals


ClariLayer is an individual-analyst context layer, delivered over MCP. Connect it to Claude Code, Cursor, Codex, or claude.ai and it bootstraps your real working context from the SQL and dbt you already have, reconciles your definitions against your warehouse, and remembers your corrections β€” so your agent stops re-explaining your data and stops making the same mistakes every session.

ClariLayer in Claude Code: the agent recalls a saved net-revenue definition, reconciles it against the warehouse, finds refunds the definition excludes, and flags the entry with a caveat that's waiting in the next session

A seeded demo warehouse: your agent recalls a saved definition, reconciles it against real results, and the mismatch is flagged as a caveat that's waiting next session. Statuses are asserted / caveat β€” never "verified".

The problem

Every new session, your AI coding agent starts from zero about your data. So it makes the same mistakes β€” queries the wrong table, picks the wrong join, counts refunds in revenue, uses a churn definition you deprecated months ago. You correct it. Next session, it forgets, and you correct it again.

A hand-written CLAUDE.md of definitions helps a little β€” but it has the same trust problem as the original numbers: it's just asserted text. Nobody checked it against your warehouse.

What ClariLayer does

It gives your agent a durable, reconciled memory of your data context β€” and it lives inside the agent you already use, over MCP. Four verbs, all live:

VerbWhat it does
recallBefore writing SQL, defining a metric, making an engineering decision, or answering a question about your data or codebase, your agent pulls the most relevant saved context β€” each with its provenance and status. Read-only, in-flow.
rememberSaves one durable fact β€” a definition, schema note, reusable query, assumption, caveat, or decision β€” so it survives across sessions. Engineering context β€” a decision, constraint, or incident lesson β€” saves the same way, via remember's strict engineering object with scope paths and a repo/revision source pointer.
bootstrapBulk-imports context from artifacts you already have, across five source kinds: a SQL SELECT (deterministically structured), a data dictionary / codebook (structured into one schema-note per variable), dbt models, CLAUDE.md / freeform notes, and a governed semantic-layer model (a Databricks Metric View, dbt semantic model, … imported as canonical metric definitions). No cold empty store.
reconcileGrounds a saved definition against your real warehouse result β€” or, for a CRM definition, bounded row-free HubSpot evidence. Your agent runs the SQL (or reads the CRM metadata) with its own access and reports back, so a declared-vs-actual mismatch surfaces as a caveat.

The context you build compounds across sessions and is portable across Claude Code, Cursor, Codex, and claude.ai.

These four verbs are the in-flow core loop. The full contract today is 19 MCP tools at capability v51 (the four above plus propose / propose_batch, the entry and reasoning lifecycle, supersede, the read-only suggest_links, get_context_entry for an entry's full stored content, get_project_stanza, the completion-receipt context_checkpoint, capabilities, and a health check). The canonical, live list is always discoverable by your client at connect β€” via the initialize response or a capabilities call β€” so you never have to trust a doc over the wire. See CAPABILITIES.md for what each recent capability bump added.

Install

Fastest β€” one command. Auto-detects Claude Code, Cursor, and Codex, writes the right config, and offers to add the standing-orders block to your CLAUDE.md:

Terminal
npx clarilayer init

You'll need a free context key β€” sign up at clarilayer.com, then open Connect your AI to mint one. The CLI prompts for it and validates it. Full options: CLI.md.

Prefer to wire it up by hand?

Replace cl_YOUR_CONTEXT_KEY with your key.

Claude Code β€” run in your terminal:

Terminal
claude mcp add --transport http clarilayer https://clarilayer.com/api/mcp/mcp --header "Authorization: Bearer cl_YOUR_CONTEXT_KEY"

Cursor β€” add to ~/.cursor/mcp.json:

config.json
{
  "mcpServers": {
    "clarilayer": {
      "url": "https://clarilayer.com/api/mcp/mcp",
      "headers": { "Authorization": "Bearer cl_YOUR_CONTEXT_KEY" }
    }
  }
}

Codex β€” add to ~/.codex/config.toml. Recent Codex connects to the URL directly, no Node/npx needed (same as Claude Code and Cursor):

toml
[mcp_servers.clarilayer]
url = "https://clarilayer.com/api/mcp/mcp"
http_headers = { "Authorization" = "Bearer cl_YOUR_CONTEXT_KEY" }

Only on older Codex without direct-HTTP support, bridge it via mcp-remote instead β€” this route requires Node.js (npx):

toml
[mcp_servers.clarilayer]
command = "npx"
args = ["-y", "mcp-remote", "https://clarilayer.com/api/mcp/mcp", "--header", "Authorization: Bearer cl_YOUR_CONTEXT_KEY"]

On claude.ai? No context key needed β€” claude.ai connects over OAuth instead. Open Settings β†’ Connectors β†’ Add custom connector, paste https://clarilayer.com/api/mcp/mcp, then sign in and approve when prompted. There is no cl_… key on this path.

See QUICKSTART.md for the full walkthrough and troubleshooting.

Then tell your agent to actually use it

Paste this into your project's CLAUDE.md (or AGENTS.md) so your agent reaches for ClariLayer proactively instead of waiting to be asked. The same stanza, as a ready-to-paste file, is examples/CLAUDE.md:

markdown
## ClariLayer β€” your data context layer (use it proactively)

ClariLayer is connected over MCP and holds this project's durable data and engineering context: definitions, schema notes, reusable SQL, assumptions, caveats, decisions, and the engineering decisions, constraints, and incident lessons this repository relies on. Use it WITHOUT waiting to be asked.

Read the full README β†’View source on GitHub β†’

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Frequently Asked Questions about ClariLayer

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "clarilayer": { "command": "npx", "args": ["-y", "ClariLayer"] } }

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Technical Specs & Signals

CategoryπŸ’»Developer Tools
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
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27Quality signal: Emerging Β· 27/100How this signal is calculated β–Ύ
Server availabilityNot measured

Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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