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get_ai_stock_analysis

Read-onlyIdempotent

TipRanks AI Stock Analysis — the 0-100 AI score for one or more stocks.

Six frontier models (OpenAI, Anthropic, Gemini, xAI, DeepSeek, Perplexity)
research each covered stock independently. Every model returns its own
0-100 score, rating (outperform / neutral / underperform), price target,
and a weighted factor breakdown across financial performance, technical
analysis, valuation, earnings call and corporate events.

Use for: "what's the AI score for NVDA", "AI rating on my watchlist",
"compare the AI scores of AAPL, MSFT and NVDA", "why do the models
disagree on Tesla".

Pass every symbol in one call — a multi-ticker call returns one compact row
per ticker, which is what a watchlist or ranking question needs. A single
ticker also returns every model's score with its factor breakdown plus the
bull and bear key points.

This is NOT the Smart Score (1-10, eight quantitative factors). It is a
separate system, and the two routinely disagree by design.

`ai_score` is the headline score and matches the AI Stock Analysis page;
`consensus` holds the cross-model average, the high and low scoring models,
and the split of rating labels. `upside_pct` is the model's price target
against the current price. `as_of` is when the report was generated —
reports regenerate on new earnings or a significant price move, so an older
date means nothing material has changed since.

Coverage is a subset of the stock universe and excludes ETFs. Symbols with
no report at all come back under `not_covered`; symbols that are covered but
lack a report from the requested `provider` come back separately under
`no_report_from_provider`, each listing the models that did score them — so
a missing provider is never reported as "this stock has no AI analysis".

Args:
    tickers: Comma-separated tickers (e.g. 'AAPL' or 'AAPL,MSFT'), max 25.
    provider: Optional single provider to report on. Omit for the
               headline score that matches the website.
    detail: 'consensus' (default) or 'full' to add each model's written
             reasoning. Ignored on multi-ticker calls.

Returns JSON: {stocks: [{ticker, company, ai_score, rating,
  headline_model, price, price_target, upside_pct, as_of, reflects,
  consensus: {models, avg_score, score_high, score_low, ratings_split,
  avg_price_target, avg_upside_pct, reports_dated}, providers: [...],
  key_points: [...]}], not_covered: [...],
  no_report_from_provider: [{ticker, covered_by}]}.
  `consensus.reports_dated` appears only when the models did not all run on
  the same date; `as_of` is always the headline report's own date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailNo'consensus' (default) returns each model's score and factor breakdown; 'full' adds each model's written reasoning. Ignored on multi-ticker calls.consensus
tickersYesComma-separated tickers (e.g. 'AAPL' or 'AAPL,MSFT,NVDA'), up to 25
providerNoOptional single AI provider to report on. Map what the user said to the provider: Claude is Anthropic, ChatGPT/GPT is OpenAI, Grok is xAI, Sonar is PerPlexity. Omit for the headline score that matches the website.

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations that mark it read-only and idempotent, the description discloses regeneration behavior ('reports regenerate on new earnings or a significant price move'), the meaning of an older as_of date, and how missing reports are handled (not_covered vs no_report_from_provider). It also explains that detail is ignored on multi-ticker calls and that consensus.reports_dated appears only when model runs differ.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured with a lead definition, usage bullets, caveats, field explanations, and an Args/Returns breakdown. While long, each section serves a purpose for a tool with complex return variations; it is slightly repetitive with the schema but well-organized and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The absence of an output schema is compensated by a detailed 'Returns JSON' block defining ai_score, consensus, upside_pct, as_of, and the coverage edge cases. The description covers what the score is, how to request it, what the fields mean, and how missing coverage is reported, making the tool fully usable without external docs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the Args section largely restates the schema's descriptions, e.g., tickers max 25 and provider's 'Omit for the headline score' line. No new parameter semantics beyond the schema are added, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'TipRanks AI Stock Analysis — the 0-100 AI score for one or more stocks' and details the six frontier models contributing scores. It explicitly distinguishes this from the Smart Score sibling, stating 'This is NOT the Smart Score (1-10, eight quantitative factors)', making the tool's specific purpose and scope unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit 'Use for:' examples and advises passing all tickers in one call for watchlist/ranking queries. It excludes ETFs and explicitly differentiates the tool from Smart Score, giving the agent clear when-to-use and when-not-to-use guidance relative to siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation3/5

Many tools have overlapping purposes, e.g. get_etf_analysis vs get_etf_forecast both provide ETF analyst consensus, get_etf_holdings vs get_etf_top_stocks both list constituents, and get_portfolio_overview vs get_portfolio_performance both return returns/performance. The detailed descriptions help, but the sheer number of similar tools creates ambiguity in selection.

Naming Consistency4/5

The set is largely consistent with a 'get_' prefix and descriptive nouns (get_stock_quotes, get_crypto_quote, get_dividend_history). Minor deviations include 'list_my_portfolios' instead of 'get_my_portfolios' and singular/plural variants like get_all_commodities_quotes vs get_commodity_quote, but the pattern remains predictable.

Tool Count1/5

With 71 tools, the count far exceeds the 50+ threshold described as an extreme mismatch. Even though the server covers a broad financial domain, such a large surface is overwhelming for an agent and includes many redundant or highly specific tools that could be consolidated.

Completeness5/5

The tool set provides comprehensive coverage of TipRanks data: quotes and historical data for all major asset classes, news, earnings and economic calendars, analyst and sentiment data, financial statements, technical analysis, options, portfolios, and screeners. There are no obvious dead ends for typical financial research tasks.

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