Load Indian Mutual Fund data to DuckDB

Indian Mutual Fund REST APIs provide access to scheme information, NAV history, and portfolio data for mutual funds registered in India. Everything needed to build a working Indian Mutual Fund → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Indian Mutual Fund to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Indian Mutual Fund to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Indian Mutual Fund API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Indian Mutual Fund API at a glance

These values come from the Indian Mutual Fund API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Indian Mutual Fund API?

Public APIs like MFapi.in and Tigzig MF NAV API are completely free and do not require any authentication or headers. Commercial platforms like BSE StAR MF or NSE NMF II require registered Mutual Fund Distributors to obtain API keys and use Bearer tokens in an Authorization header.

1. Get your credentials

Many popular Indian Mutual Fund REST APIs (such as mfapi.in) do not require authentication or API keys. For platforms that do require credentials, such as professional or exchange-based services (e.g., MintByte or BSE StAR MF), you typically obtain them by signing up for an account on the provider's developer dashboard. Once logged in, navigate to the API or 'Developer' settings section to generate an API Key or client credentials. Keep these securely stored as they are usually displayed only once.

2. Add them to .dlt/secrets.toml

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[sources.indian_mutual_fund_source]
api_key = "your_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Indian Mutual Fund data can I load into DuckDB?

These are the Indian Mutual Fund endpoints dlt can load into DuckDB:

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How do I load only new Indian Mutual Fund records?

The Indian Mutual Fund API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "schemes", "endpoint": {
    "path": "mf",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Indian Mutual Fund pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /mf/search and /v1/data/amfi/scheme/{code} from the Indian Mutual Fund API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def indian_mutual_fund_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mfapi.in (for MFapi.in) or https://api.tigzig.com/mf/v1 (for Tigzig)",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "schemes", "endpoint": {"path": "mf"}},
            {"name": "scheme_details", "endpoint": {"path": "mf/{scheme_code}"}}
        ],
    }
    yield from rest_api_resources(config)


def load_indian_mutual_fund_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="indian_mutual_fund_pipeline",
        destination="duckdb",
        dataset_name="indian_mutual_fund_data",
    )
    load_info = pipeline.run(indian_mutual_fund_source())
    print(load_info)


if __name__ == "__main__":
    load_indian_mutual_fund_to_duckdb()

Run it with python indian_mutual_fund_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Indian Mutual Fund data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("indian_mutual_fund_pipeline").dataset()
df = data.schemes.df()
print(df.head())

SQL:

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SELECT * FROM indian_mutual_fund_data.schemes LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Indian Mutual Fund to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Indian Mutual Fund loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Indian Mutual Fund data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack