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AletaIndex

AletaIndex Narrative Intelligence

Official
by AletaIndex
OverviewSchemaRelated ServersScoreDiscussions(1)
Python
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • 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.

  • Average 4.6/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    No annotations provided, so description carries full burden. It discloses output structure (global narratives, daily topics, articles, sentiment scores) and input constraints. Lacks explicit statement of read-only nature or rate limits, but adequately describes behavior for a data retrieval tool.

    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?

    Well-structured with summary, returns, and args sections. Front-loaded with purpose. Some redundancy (e.g., 'Args:' duplicates schema but adds value), making it slightly verbose but still efficient.

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

    Completeness4/5

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

    Given no output schema or annotations and three parameters, the description covers purpose, parameters, and return values adequately. Could clarify sentiment scale or 'dominance' field, but completeness is high for a retrieval tool.

    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?

    Schema description coverage is 0%, but description fully compensates: explains tickers format, maximum 10, free tier list; from_date and to_date defaults. Adds meaning far beyond the bare schema.

    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?

    Clearly states 'Get financial narrative intelligence for one or more stocks' and details the returned data (global narratives, daily topics, articles, sentiment scores). Distinct from sibling tool get_portfolio_risk which focuses on risk metrics.

    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?

    Explains when to use: 'understand what stories are driving a stock and how sentiment is evolving.' Provides practical parameter details (ticker limits, free/paid tiers) but does not explicitly exclude alternative tools or mention when not to use this tool.

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

  • Behavior5/5

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

    With no annotations, the description fully bears the burden of transparency. It details the grouping logic, output structure (risk themes, tickers, narratives, scores), and input constraints, providing complete behavioral context.

    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: a one-line summary, followed by detailed explanation of functionality, parameter format, and return value. Every sentence adds value, and it is appropriately sized for the tool's complexity.

    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?

    Given the simple input/output (one parameter, no output schema), the description is complete. It covers input constraints, output structure, and functional behavior, leaving no major gaps.

    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 schema has 0% description coverage, so the description compensates by explaining the 'holdings' parameter format (TICKER:WEIGHT pairs, comma-separated), constraints (max 50, weights sum ~1.0), and providing an example.

    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 purpose: 'Analyze narrative risk across a portfolio of stocks.' It specifies grouping by macro themes and identifying concentrated exposure, which distinguishes it from the sibling tool 'get_narratives'.

    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 provides clear context on input format and constraints (max 50 holdings, weights sum to ~1.0), but does not explicitly state when not to use the tool or suggest alternatives.

    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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  • Evaluate tool definition quality.

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