@shahid.la/cds-db-nlquery-mcp

MCP server for natural language queries against CDS db-layer entities (db/schema.cds).

Ask questions about your database in plain English. The server discovers your schema automatically, generates real SQL JOINs using CDS, and returns rows. No hardcoded queries, no SQL, no schema configuration. Bring your own LLM: Anthropic or OpenAI, or any OpenAI-compatible endpoint (Azure OpenAI, Groq, Ollama, local models, etc.).

Targets the db/ layer, not OData services. If your entities are exposed as OData services, use an MCP package that targets the service layer instead.

Read-only. This package only executes SELECT queries. No INSERT, UPDATE, or DELETE.

Prerequisites

• A CAP project with @sap/cds >= 7
• A configured CDS database (HANA Cloud, SQLite, PostgreSQL)
• An MCP client (Claude Code, Claude Desktop, or any MCP-compatible host)
• An API key for an LLM provider (see LLM provider below), used to translate your question into a query, not to answer it

Quick start

1. Install in your CAP project

npm install @shahid.la/cds-db-nlquery-mcp

2. Create .mcp.json in your CAP project root (same folder as package.json)

{
  "mcpServers": {
    "cds-db-nlquery": {
      "command": "npx",
      "args": ["-y", "@shahid.la/cds-db-nlquery-mcp"],
      "cwd": "/absolute/path/to/your/cap/project",
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-..."
      }
    }
  }
}

Set cwd to the absolute path of your CAP project root, where db/schema.cds lives. See LLM provider for using OpenAI, Gemini, or another provider instead.

npx -y re-resolves the package on every server start (from npm's cache once downloaded, not a fresh network fetch each time, but still an extra resolution step). If you've run npm install @shahid.la/cds-db-nlquery-mcp already, you can point "command" at the installed binary directly instead, node_modules/.bin/cds-db-nlquery-mcp, for a slightly more predictable startup, especially in production.

3. Open your project in Claude Code and ask a question

Which customers have a DTI ratio above 5?
Show me all open payments overdue by more than 30 days, include the borrower name.
List all loans in the MINING sector with the customer's current DTI ratio.

Example response: the server tells the client to render rows as a vertical field/value list, not a markdown table (real output, verified against a live deployment):

Found 3 customers with DTI above 5:

PARTNER    : 30100003
DTI_RATIO  : 7.20
BU_SORT1   : Domestic Customer AU 3

PARTNER    : 30100001
DTI_RATIO  : 5.80
BU_SORT1   : Domestic Customer AU 1

PARTNER    : 30100002
DTI_RATIO  : 5.40
BU_SORT1   : Domestic Customer AU 2

How it works

When you ask a question:

1. Your MCP client (e.g. Claude Code) calls the natural_language_query tool with your question.
2. The MCP server has already loaded your CDS schema at startup: entity names, columns, and associations. It sends this schema plus your question to the LLM provider you configured (Anthropic, OpenAI, or any OpenAI-compatible endpoint), which translates it into a structured query descriptor.
3. The server executes a single CDS query. CDS association paths (customer.BU_SORT1) generate real SQL JOINs, executed by your database, not by JavaScript. Scales to production data volumes.
4. Results come back to your MCP client, which formats and presents the answer.

The LLM call in step 2 is a small, cheap planning step (translating your question into JSON). It does not need a large or expensive model. A fast/cheap tier model is recommended.

Configuration

All configuration is via environment variables in the .mcp.json env block.

{
  "mcpServers": {
    "cds-db-nlquery": {
      "command": "npx",
      "args": ["-y", "@shahid.la/cds-db-nlquery-mcp"],
      "cwd": "/path/to/your/cap/project",
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "MCP_ALLOWED_ENTITIES": "Customers,Orders,Products",
        "MCP_BLOCKED_COLUMNS": "PASSWORD,EMBEDDING,SSN",
        "MCP_MAX_ROWS": "100",
        "MCP_MODEL_PATH": "db"
      }
    }
  }
}

| Variable | Default | Description | |---|---|---| | MCP_ALLOWED_ENTITIES | (all entities) | Comma-separated list of entity short names, the name after the last dot in the FQN (e.g. Customers, not my.app.Customers). Leave unset during development; always set for production. | | MCP_BLOCKED_COLUMNS | (none) | Comma-separated column names to exclude from all results. Stripped before the query runs. Useful for columns like EMBEDDING, PASSWORD, SSN. | | MCP_MAX_ROWS | 500 | Maximum rows per query. Enforced as a SQL LIMIT, not a post-fetch filter. | | MCP_MODEL_PATH | db | Path to your CDS model folder or file, relative to cwd. Change if your schema is at model/, srv/, etc. | | MCP_DB_USER / MCP_DB_PASSWORD | (consumer app's own DB user) | Connect with a different HANA user than your app's runtime user. Host/port/schema are reused, only credentials are overridden. See Security → Production for why this matters. |

LLM provider

A small LLM call translates your question into a query descriptor. Bring your own provider, set ONE of the following in the env block:

Anthropic (Claude):

"env": {
  "ANTHROPIC_API_KEY": "sk-ant-...",
  "ANTHROPIC_MODEL": "claude-haiku-4-5-20251001"
}

OpenAI, or any OpenAI-compatible provider:

"env": {
  "OPENAI_API_KEY": "sk-...",
  "OPENAI_MODEL": "gpt-4o-mini"
}

OPENAI_MODEL accepts any model name. Use whatever your provider expects. Set OPENAI_BASE_URL to point at a different OpenAI-compatible endpoint:

| Provider | OPENAI_BASE_URL | Example OPENAI_MODEL | |---|---|---| | OpenAI | (omit, uses default) | gpt-4o-mini | | Google Gemini | https://generativelanguage.googleapis.com/v1beta/openai/ | gemini-2.0-flash | | Groq | https://api.groq.com/openai/v1 | llama-3.1-8b-instant | | Mistral | https://api.mistral.ai/v1 | mistral-small-latest | | DeepSeek | https://api.deepseek.com | deepseek-chat | | xAI (Grok) | https://api.x.ai/v1 | grok-2-latest | | Azure OpenAI | your Azure endpoint | your deployment name | | Ollama (local) | http://localhost:11434/v1 | llama3.1 (API key can be any non-empty string) |

| Variable | Description | |---|---| | LLM_PROVIDER | "anthropic" or "openai". Auto-detected from whichever API key is set, only needed if both are set and you want to force one. | | ANTHROPIC_API_KEY / ANTHROPIC_MODEL | Anthropic native API | | OPENAI_API_KEY / OPENAI_MODEL / OPENAI_BASE_URL | OpenAI or any OpenAI-compatible endpoint |

This is a planning-only call (question to JSON descriptor). A fast/cheap tier model is sufficient and recommended.

Security

Development

The server uses your project's existing database connection, whatever CDS has configured in default-env.json or your service binding. No extra setup needed.

Production

Important: this package queries the database directly via cds.run(). It does not go through the CAP service layer, so CAP @requires and @restrict annotations are not enforced. Access control is entirely your responsibility at the database and configuration level.

Step 1: Create a dedicated read-only database user

-- Run as DBADMIN in SAP HANA Cloud Central
CREATE USER MCP_READER PASSWORD 'your-password';
GRANT SELECT ON SCHEMA YOUR_HDI_SCHEMA TO MCP_READER;

-- Revoke tables you do not want queryable
REVOKE SELECT ON "YOUR_HDI_SCHEMA"."your.AuditLog" FROM MCP_READER;
REVOKE SELECT ON "YOUR_HDI_SCHEMA"."your.RegulatoryDocuments" FROM MCP_READER;

This user can only read. No write access. HANA itself enforces it, independently of this package.

Step 2: Point the server at it with MCP_DB_USER/MCP_DB_PASSWORD

The server connects with these credentials instead of inheriting your app's own database connection. Host, port, and schema are reused automatically, only the user/password are overridden:

"env": {
  "MCP_DB_USER": "MCP_READER",
  "MCP_DB_PASSWORD": "your-password"
}

If you keep these in a separate gitignored file instead of directly in .mcp.json, that's fine too. The server just reads process.env, same as any other variable here.

Step 3: Set MCP_ALLOWED_ENTITIES

"env": {
  "MCP_ALLOWED_ENTITIES": "Customers,Orders,Products"
}

This is enforced on the entity you query directly and on any entity reached via an association join in select/where. For example, if Customers isn't in the allowlist, a query against Orders can't read Customers data through a customer.NAME join path either. The database user restricts access at the HANA level; MCP_ALLOWED_ENTITIES adds a second layer at the application level. Use both.

Joins

The server reads CDS associations from your schema. When the LLM references customer.BU_SORT1 in a query, CDS generates a real SQL JOIN, executed by the database.

Multiple associations in one query work:

"Show loans in the MINING sector with borrower name and current DTI"

Generates a single SQL statement joining Loans → BusinessPartners → BCA_DTI in one database round-trip.

One constraint: do not select the same column name from two different entities in the same query (e.g. LOAN_ID from both the main entity and a joined entity). The database rejects duplicate column names. Claude is instructed to avoid this, but worth being aware of.

Comparing two columns to each other

Most filters compare a column to a fixed value (DTI_RATIO > 5). For questions like "which loans have collateral worth less than the loan amount", the comparison is between two columns instead. Use valCol in place of val:

{ "col": "collateral.VALUE", "op": "<", "valCol": "AMOUNT" }

Either side can be an association path. This is handled by the same join mechanism as everything else. No separate query, no JavaScript-side comparison.

Filtering by a coded value's human meaning

If a column has a @Common.Text value-help association (see Coded values), filter via the text field directly rather than guessing the underlying code:

{ "col": "status.TEXT", "op": "like", "val": "Active" }

like is case-insensitive (enforced via UPPER() on both sides, not relying on database collation), so the exact case of the value doesn't need to match.

Column and entity labels

The LLM only sees what's in your CDS model: column names, types, and any labels you've annotated. Plain code comments (// customer name) are invisible to it. Without a label, an ambiguous column name can cause the LLM to guess wrong, regardless of how capable the model is.

@title: reused automatically, zero extra work

If your schema already has SAP's standard @title annotation (common in projects with a Fiori UI or OData service), the server picks it up automatically. You don't need to do anything:

entity Customers {
  key PARTNER  : String(10);
  @title: 'Customer Name'
  BU_SORT1     : String(40);
}

@NLP.label: for disambiguation @title isn't meant for

Use this when you need to tell the LLM something a UI-facing label shouldn't say, e.g. "don't use this column for X." This was a real bug we hit: a BU_TYPE code column (1=person, 2=organisation) was being picked by the LLM whenever a question asked for a customer's "name," because nothing told it otherwise. The fix:

entity Customers {
  key PARTNER  : String(10);
  @NLP.label: 'Partner type code: 1=person, 2=organisation. NOT a name, never use for name lookups'
  BU_TYPE      : String(2);
  @NLP.label: 'Customer / business partner full name, use this whenever a question asks for a name'
  BU_SORT1     : String(40);
}

After adding these two labels, even the cheapest tier model (claude-haiku-4-5) picked the right column every time. Fix ambiguity at the schema level, not by tweaking prompts per-bug. It's permanent and works regardless of which LLM provider or model you use.

Precedence: @NLP.label is checked first, falls back to @title, falls back to the raw column/entity name if neither is set.

Other @NLP annotations

entity Customers @(NLP.label: 'Active borrowers, loan customers with income and sector data') {
  dti : Association to BCA_DTI on dti.PARTNER = PARTNER
        @NLP.joinType: 'LEFT';
}

| Annotation | Where | Effect | |---|---|---| | @NLP.label | Entity or column | Description shown to the LLM. Falls back to @title, then the name. | | @NLP.joinType | Association | Override join type: 'LEFT' or 'INNER'. Auto-detected from cardinality if not set. | | @NLP.alias | Association | Override the association name used in queries |

All optional. The package works without any of them. But for any column whose name alone could be misread (codes, abbreviations, anything that looks like one thing but means another), a label is the difference between the LLM guessing and the LLM knowing.

Coded values: what does STATUS = 'C' mean?

Labels solve "what does this column mean." A separate problem: what does a coded value in that column mean? STATUS = 'C'. Closed? Cancelled? Confirmed?

There are two ways to tell the LLM, and which one to use depends on whether the value list can change without a code deploy.

@Common.Text: for business-configurable codes (the common case)

Most status/type codes are business data. Someone in operations might introduce a new value next quarter, and that should never require touching the schema file. The SAP-standard mechanism for this is @Common.Text, pointing through an association to a small lookup/check table, the same pattern Fiori elements uses for value-help dropdowns:

entity LoanStatusCodes {
  key CODE : String(1);
  TEXT     : String(20);
}

entity Loans {
  @Common.Text: status.TEXT
  STATUS : String(1);
  status : Association to LoanStatusCodes on status.CODE = STATUS;
}

Seed it with {CODE: 'A', TEXT: 'Active'}, {CODE: 'C', TEXT: 'Closed'}. Adding a new status later is an INSERT into LoanStatusCodes, no schema change, no redeploy. The server detects @Common.Text, tells the LLM a readable value is available via the association path, and the LLM includes it in select using the normal join mechanism. No extra code on our side, it's the same association-path JOIN used everywhere else in this package.

CDS enum: only for sets that are genuinely fixed forever

STATUS : String(1) enum { active = 'A'; closed = 'C'; };

This is compile-time: adding a value means editing schema.cds and redeploying. Appropriate only for values that are tied to actual program logic anyway (so a code change would be required regardless), not for business classifications. We initially used enum for our own demo's status fields and walked it back to @Common.Text for exactly this reason. It's documented here as a contrast, not a recommendation.

Rule of thumb: reach for @Common.Text by default. Use enum only when you're sure the list can never grow without code changing anyway.

When a column has @Common.Text, query results get the raw code back as normal. Presentation (showing "Closed" instead of "C") is up to whichever LLM renders the final answer, using the readable value it fetched via the join.

Startup log

When the server starts, check the output panel in Claude Code:

[cds-db-nlquery-mcp] Schema loaded — 12 entities
[cds-db-nlquery-mcp] WARNING: MCP_ALLOWED_ENTITIES not set — all entities are queryable. Set in .mcp.json for production.
[cds-db-nlquery-mcp] WARNING: MCP_DB_USER not set — using the project's default DB connection (likely the main app's full-access user). Set MCP_DB_USER/MCP_DB_PASSWORD to a restricted read-only user for production use.
[cds-db-nlquery-mcp] LLM provider: anthropic
[cds-db-nlquery-mcp] Ready (stdio)

0 entities at startup? Check that cwd in .mcp.json points to your CAP project root and that MCP_MODEL_PATH matches your schema folder name.

WARNING: No LLM provider configured? Set ANTHROPIC_API_KEY or OPENAI_API_KEY in the .mcp.json env block, see LLM provider.

WARNING: MCP_DB_USER not set? Expected during development, the server is using the same database connection as your CAP app. Before production use, see Security → Production for creating a dedicated read-only user.

Testing this against your own deployment

npm install gives you src/, the MCP server, and nothing else. That's the entire published package. The GitHub repository also has an examples/capability-demo/ folder with a real CDS schema, seed data, ~35 example queries with verified expected results, and four scripts to actually exercise them against your own database (not just read about it): one runs the example queries directly against the internal functions, one lets you ask your own question end-to-end through a real LLM, one runs every example question's natural-language text through a real LLM and checks the result, and one spawns src/mcp-server.js itself as a real child process and drives it through the actual MCP stdio protocol, the same way Claude Code or Claude Desktop would, rather than calling internal functions directly. None of this ships with npm install. Clone the repo if you want it. See that folder's own README for details.

How releases are tested

npm test (126 unit tests) runs against a mocked database. Fast, but it can't catch backend-specific behavior (a real HANA deployment has rejected things the mocks happily accepted, more than once). Before tagging a release, npm run test:deployment is run against a real CAP project connected to live HANA Cloud. It requires real credentials and a deployed examples/capability-demo/ schema, so it isn't part of npm test or CI, but it is a required manual step, not an optional one. examples/capability-demo/smoke-test-server.js is also run. It spawns src/mcp-server.js itself and drives it over the real MCP stdio protocol with a real LLM call, not just the internal functions directly.

RELEASE_VERIFICATION.md is the actual, append-only record of this: real output from a real run against live BTP HANA, captured and committed before each release, not just claimed.

License

MIT