Market Data for AI Agents: MCP or API?

Use MCP to discover and prototype a market-data workflow, then use REST when the workflow becomes a scheduled production job.

For an AI agent market data project, MCP and REST are two interfaces to the same Alphanume observations. MCP is usually better while the researcher is discovering tools, inspecting fields, and revising a question conversationally. REST is usually better after the request, pagination, validation, and output schema are fixed. The practical handoff is not model versus code. It is an exploratory tool call becoming a versioned request that another process can replay without interpretation.

The hosted server exposes the catalog as 25 tools and returns the same point-in-time data contracts as the REST API. OAuth clients use the remote endpoint; clients without a browser can use the key-auth endpoint. The hosted MCP reference documents transports, authentication, limits, and response conventions. The access tiers apply equally to MCP and REST because every call resolves to the same account key and entitlement.

Choose the interface from the job

MCP gives the model tool descriptions and parameter schemas. That is valuable when a person asks a broad question such as which point-in-time fields can build a historical size universe. The agent can discover get_historical_market_cap, learn that a ticker or date filter is required, and inspect the response. REST becomes preferable when a scheduler already knows the endpoint, filters, cursor rules, retry policy, and destination table.

Neither interface repairs a vague research design. If the agent uses a current ticker list for an old rebalance, the result has survivorship leakage whether the data arrived through a tool or a URL. The Historical Market Cap dataset is useful precisely because its date and shares fields let the caller build a dated universe, but the caller must still define when information became eligible.

Prototype the exact screen through MCP

Start with one observation date. Ask the agent to identify the tool, call it with a narrow filter, preserve pagination metadata, and explain no more than the returned fields support. A useful prototype ends with a machine-readable request specification. It does not end with a persuasive paragraph about the market.

Research date: 2025-06-30.
Use Alphanume MCP to retrieve historical market cap for that date.
Keep date, ticker, market_cap, and shares_outstanding.
Report count, has_more, next_cursor, truncated_to_max_rows, and the exact tool arguments.
If truncated_to_max_rows is present, stop and label the market-wide result incomplete even when has_more is false.
Do not attach future returns or replace missing values.
After inspecting the result, write the equivalent REST endpoint and query parameters for the complete extraction without making a second market claim.

The MCP layer can truncate the data array to 500 rows after the REST response is formed. Its inherited has_more and next_cursor therefore cannot certify completeness when truncated_to_max_rows appears. The equivalent REST request uses GET /v1/historical-market-cap with the same date. Production code must paginate that REST route until has_more=false, passing both values from next_cursor. The existing stock-data REST API guide in Python shows the general request pattern once the prototype is stable.

Make the production handoff explicit

A handoff document should fix the endpoint, all query parameters, expected columns and types, pagination loop, permitted nulls, entitlement errors, rate-limit behavior, and observation timestamp. Store that document beside the job. Then have the scheduled process write raw responses before transformations, so an unexpected research result can be traced to its input rather than reconstructed from a model summary.

GET /v1/historical-market-cap?date=2025-06-30
Required columns: date, ticker, market_cap, shares_outstanding
Pagination: cursor_date plus cursor_ticker from next_cursor
Stop condition: has_more is false
MCP guard: if truncated_to_max_rows is present, do not treat that tool result as complete; run this REST extraction
Null policy: retain null; do not backfill from later dates
Failure policy: distinguish 403 access, 429 rate limit, and empty data
Audit record: request time, parameters, response count, cursor sequence

This specification is intentionally boring. That is the advantage of REST in production. A model can help generate the client and tests, but the scheduled job should not renegotiate the meaning of a missing field every morning. Keep MCP available for investigation when a schema or research question changes, then repeat the handoff.

Watch the interface-specific failure modes

With MCP, the model may omit a filter, join dates loosely, summarize away nulls, or mistake REST cursor metadata for proof that a capped tool result is complete. Require the argument log and fail whenever truncated_to_max_rows appears. With REST, application code may silently accept a schema change, retry a non-retryable entitlement response, or write partial pages as complete. Validate response fields and counts before promoting data. In both cases, a 403 date-range restriction describes access.

Run one two-interface acceptance test

Pick a single date and dataset, run the MCP prototype, then execute the generated REST request in a controlled script. Compare row count, fields, values, nulls, and pagination. Save both raw outputs and the comparison result. If they agree, schedule only the REST version and keep the MCP transcript as the research record. If they differ, stop at the first differing request parameter instead of asking the model to explain the market.