TipRanks

get_analysts_by_firm

Read-onlyIdempotent

List analysts at a given firm (case-insensitive substring match).

Each row: {name, firm, rank, avg_return_1y_pct, covered_tickers_sample,
total_covered}. Results are deduped by analyst name (keeping the best
rank) and sorted by rank ascending.

When `sector` is provided, the per-analyst coverage list is filtered
to tickers in that sector and the row shape becomes {name, firm,
rank, avg_return_1y_pct, sector, covered_tickers}.

Args:
    firm: Firm name or fragment (e.g. 'Goldman' matches 'Goldman Sachs').
    sector: Optional lowercase sector (e.g. 'technology', 'healthcare').

Input Schema

TableJSON Schema

Name	Required	Description	Default
`firm`	Yes
`sector`	No

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description adds valuable behavioral details: case-insensitive substring matching, deduplication by analyst name keeping best rank, sort order, and the output shape change when sector is provided. This significantly enriches the agent's understanding.

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

Conciseness5/5

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

The description is well-structured, beginning with the core action, then output format, behavioral rules, and parameter details. It is information-dense without redundancy—every sentence provides necessary context.

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?

With only two simple parameters and no output schema, the description is complete: it explains return row structures for both with/without sector, matching behavior, deduplication, and sorting. An agent can confidently invoke this tool without further clarification.

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

Parameters5/5

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

The input schema only provides names and defaults, but the description adds substantive meaning: firm is a case-insensitive substring (with example matching 'Goldman' to 'Goldman Sachs'), sector is optional lowercase and affects the result row shape. This fully compensates for the 0% schema description coverage.

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 clearly states the tool's function: 'List analysts at a given firm' with specific output rows. It distinguishes from sibling tools by focusing on firm-based analyst listing, which is unique among the many get_* tools.

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

Usage Guidelines4/5

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

The description clearly implies when to use it—when you need analysts for a firm. It does not explicitly compare to alternatives like get_recent_analyst_ratings, but provides clear context and parameter guidance, satisfying the 'clear context, no exclusions' level.

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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