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Fmp Research

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A remote MCP server for ChatGPT that exposes read-only Financial Modeling Prep tools to build strict buy-side earnings-call evidence packs and score catalyst re

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Описание

A remote MCP server for ChatGPT that exposes read-only Financial Modeling Prep tools to build strict buy-side earnings-call evidence packs and score catalyst reports.

README

Read-only MCP server for FMP and SEC research context. The server behaves as an information and suggestion layer: it returns source context, selected periods, tables, filings where requested, and recommended_next_actions without enforcing gates, restrictions, or blocking logic.

Tools

Tool Purpose
fmp_get_company_profile Return company profile and metadata.
fmp_list_transcript_dates Discover available earnings-call periods and suggested transcript fetch actions.
fmp_get_earnings_call_prepared_remarks Return prepared remarks / call-start text for a selected period.
fmp_get_earnings_call_q_and_a Return Q&A text for a selected period.
fmp_get_statement_tables Return income statement, balance sheet, cash flow, metrics, ratios and growth tables.
fmp_search_sec_filings Return SEC filing candidates from FMP.
get_earnings_release Fetch an earnings release from public PR sources via FMP and convert it to LLM-friendly Markdown.
fmp_build_research_evidence_pack Build selected periods, evidence manifest, context notes and recommended next actions.
fmp_build_research_pack Compatibility alias for the evidence-pack tool.
fmp_validate_research_evidence Return informational notes and recommended next actions from a payload.
research_report_contract Return suggested report structure and sector lenses.

get_earnings_release inputs

The FMP earnings release tool intentionally exposes only three model-facing parameters:

{
  "symbol": "SPT",
  "fiscalYear": 2026,
  "fiscalQuarter": 1
}

The tool infers the SEC filing internally by ranking public SEC filings and candidate exhibits using filing metadata, exhibit metadata, earnings-release language, requested fiscal period signals, and table density. Do not pass filingDate in recommended_next_actions.

Evidence-pack behavior

fmp_build_research_evidence_pack returns:

  • evidence_pack_version
  • symbol
  • selected_periods
  • latest_completed_fiscal_year
  • evidence_manifest
  • context_notes
  • recommended_next_actions

The evidence-pack output omits strict templates, policies, source flags, gates and embedded SEC filing prioritization. Use fmp_search_sec_filings separately when filing discovery is needed.

Philosophy

The MCP provides information and suggestions only. It does not tell the LLM what it is allowed to conclude, does not block downstream work, and does not enforce downstream decision rules.

from github.com/FGHMX/fmp-mcp-research

Установить Fmp Research в Claude Desktop, Claude Code, Cursor

Рекомендуется · одна команда, все IDE
unyly install fmp-mcp-research

Ставит в Claude Desktop, Claude Code, Cursor и VS Code — сам разбирается с npx, uvx и сборкой из исходников.

Впервые? Поставь CLI: curl -fsSL https://unyly.org/install | sh

Или настроить вручную

Выполни в терминале:

claude mcp add fmp-mcp-research -- uvx --from git+https://github.com/FGHMX/fmp-mcp-research fmp-mcp-research

FAQ

Fmp Research MCP бесплатный?

Да, Fmp Research MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Fmp Research?

Нет, Fmp Research работает без API-ключей и переменных окружения.

Fmp Research — hosted или self-hosted?

Доступен hosted-вариант: Unyly запускает сервер в облаке, локальная установка не обязательна.

Как установить Fmp Research в Claude Desktop, Claude Code или Cursor?

Открой Fmp Research на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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