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Glama
Etha0916

praesentire-mcp

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
PRAESENTIRE_API_KEYYesYour Praesentire API key (pr_...). Get one at https://praesentire.com/dashboard.
PRAESENTIRE_BASE_URLNoOverride base URL for self-hosting or staging. Default: https://praesentire.com/api/v1https://praesentire.com/api/v1

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
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
get_sentimentA

Get aggregated financial news sentiment for a single stock ticker over a rolling time window. Returns an -1 to +1 average score, confidence, article count, bullish/neutral/bearish distribution, and up to 3 latest articles with one-line rationale each. Sources: Reuters, Bloomberg, Yahoo Finance (English) + 經濟日報, 工商時報, Anue 鉅亨 (Traditional Chinese). Useful for: gauging market mood before opening a position, monitoring sentiment shifts post-earnings, explaining unusual price moves with news context.

get_sentiment_batchA

Get sentiment for multiple tickers in one call. More efficient than calling get_sentiment N times when you have a watchlist. Each ticker counts as one request against the daily rate limit (so 10 tickers = 10 requests billed). latest_3 articles are omitted from each result to keep the response small — call get_sentiment for the full payload on any specific ticker.

compare_languagesA

Compare English vs Traditional Chinese news sentiment for a ticker side-by-side, plus a divergence score. Designed for cross-market arbitrage signal detection — US press (Reuters/Bloomberg) often leads Taiwan/Asia press by hours. A large divergence (|divergence| > 0.3) often precedes mood shifts on Asia-listed semis or US names with strong TW supply chain exposure. Returns: english + chinese aggregates (article_count, average_score, confidence, distribution) and divergence = english.average_score - chinese.average_score. Positive divergence = English more bullish; negative = Chinese more bullish.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A4.2/5.0

Scored across 3 tools

Disambiguation4/5

Tools are mostly distinct: compare_languages is unique (cross-language comparison), while get_sentiment and get_sentiment_batch overlap but descriptions clarify batch is a more efficient bulk call without full article details.

Naming Consistency4/5

Naming follows a consistent pattern: get_sentiment and get_sentiment_batch share the get_ prefix, compare_languages uses compare_ which is fitting. Minor deviation but overall predictable.

Tool Count4/5

Three tools is reasonable for a focused sentiment analysis server. Each tool has a distinct role (single, batch, compare) without being overly minimal.

Completeness4/5

Covers key operations: single sentiment, batch sentiment, and cross-language comparison. Could benefit from historical trends or time-series, but core functionality is present.

Maintenance

ActivityInactive
ResponsivenessNo issues