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Glama
AletaIndex

AletaIndex Narrative Intelligence

Official
by AletaIndex
OverviewSchemaSourceRelated ServersDiscussions(1)Admin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
NARRATIVE_API_KEYYesAPI key from aletaindex-narrative.com. Free trial available.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
get_narrativesA

Get financial narrative intelligence for one or more stocks.

Returns structured narrative data: persistent story threads (global narratives),
daily article clusters, individual articles, and sentiment scores. Use this to
understand what stories are driving a stock and how sentiment is evolving.

Args:
    tickers: Comma-separated ticker symbols. Examples: "NVDA" or "NVDA,TSLA,AAPL".
             Maximum 10 tickers per request. Free tier: TSLA, NVDA, AAPL, MSFT,
             AMZN, GOOGL, META, AMD, NFLX, JPM. Plus/Scale: all 109 tickers.
    from_date: Start date in YYYY-MM-DD format. Defaults to 7 days ago.
    to_date: End date in YYYY-MM-DD format. Defaults to today.

Returns:
    Dict with narrative data per ticker, including global_narratives (persistent story
    threads with title, sentiment, and dominance), daily_topics (day-level article
    clusters), and articles (individual news items with relevance and sentiment scores).
get_portfolio_riskA

Analyze narrative risk across a portfolio of stocks.

Groups narratives across all holdings by macro theme (e.g. "AI Regulation",
"Interest Rate Sensitivity") and identifies which stories create concentrated
narrative exposure across multiple positions simultaneously.

Args:
    holdings: Portfolio positions as TICKER:WEIGHT pairs, comma-separated.
              Weights represent portfolio allocation (should sum to ~1.0).
              Example: "NVDA:0.30,AAPL:0.25,TSLA:0.20,MSFT:0.25"
              Maximum 50 holdings.

Returns:
    Dict with macro risk themes, each showing affected tickers, dominant narrative
    titles, sentiment trajectory, and weighted exposure score. Also includes an
    overall portfolio narrative concentration score.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.5/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: get_narratives retrieves narrative data for specific tickers, while get_portfolio_risk analyzes cross-holding narrative exposure. There is no overlap in functionality.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (get_narratives, get_portfolio_risk), making the naming predictable and clear.

Tool Count4/5

With only 2 tools, the surface is slightly thin, but they cover the core use cases of narrative retrieval and portfolio risk analysis. The small count is acceptable given the focused domain.

Completeness4/5

The tools cover the primary functions for narrative intelligence: retrieving narratives for stocks and assessing portfolio risk. Minor gaps exist (e.g., no narrative search or comparison), but the set is sufficient for its stated purpose.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources