Financial Data MCP Server: Connect Stock Research to AI Agents
How to expose prices, filings, earnings, fundamentals, transcripts, and news as safe, source-aware tools an agent can call.
A financial data MCP server publishes typed tools that let an AI client discover and query market and company evidence during a task. A production server should be read-only by default, use narrow schemas, return source and coverage metadata, enforce plan and key limits, protect secrets, and distinguish unavailable data from an empty result.
Design tools around research decisions
Useful tools resolve a security, fetch historical bars, list SEC filings, retrieve filing facts, query earnings events, locate transcript availability, and return company news references. Each tool should do one job with explicit symbol, time, limit, cursor, and as-of inputs.
Do not make the model guess whether a source is complete. Return coverage status, source, known-at time, rights state, and a stable record identifier beside the data.
Keep the server safe and predictable
Start with read-only methods. Validate arguments, cap result sizes, paginate deterministically, meter keys, log request IDs, and redact secrets. Prompt text must never be allowed to broaden backend authorization.
For outcomes or monitored events, integrations can notify the user through their own connected channel, but external sends should require explicit opt-in and idempotency. The MCP data layer should not silently act on a market observation.
- Use server-side API keys only.
- Expose narrow typed JSON schemas.
- Return citations and source URLs.
- Fail closed on unknown rights or coverage.
What DataCedar can expose
DataCedar’s independent serving layer is suited to MCP because the tools do not need to know which acquisition adapter collected a record. They query one versioned API for company identity, permitted history, SEC evidence, events, documents, and coverage.
The same contract can serve Claude, ChatGPT, Cursor, Codex, or another compatible client. Model choice remains separate from data rights, authentication, and the evidence returned.
How DataCedar preserves the evidence
DataCedar separates acquisition from serving. Permitted source responses are retained with retrieval time and identifiers, normalized into DataCedar-owned tables, checked against expected coverage, and exposed through a stable versioned API. A collector can be replaced without changing the customer contract or making an upstream provider a runtime dependency.
Every research stream carries effective and known-at time where the distinction matters. Rights-restricted, unavailable, partial, stale, and genuinely empty states remain visible, so a backtest can fail closed and a buyer can see the product boundary before committing engineering time.
Key takeaways
- 01Build small read-only tools with typed inputs.
- 02Return source, known-at time, rights, and coverage.
- 03Keep authorization outside the model prompt.
- 04Require opt-in before any external notification action.
Query public company evidence through one stable API. Free Explorer tier, no card.
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