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Santiment

by io.github.santiment

Combined Trends

combined_trends_tool
Read-only

Combined trends tool that fetches trending words, stories, and documents in parallel.

This tool provides a unified view of all trending data - words with their documents and stories - in a single response across all crypto projects.

When to use vs trending_stories_tool

This is a superset of trending_stories_tool: same stories, plus trending words, their context and AI-generated bull/bear summaries. It calls an LLM, so it is slower and has a tighter per-tool rate-limit sub-cap than every other tool. If only trending stories are needed, call trending_stories_tool instead; set include_words: false / include_stories: false to drop a half that is not needed. Do not call both tools for the same question.

Parameters

  • time_period - Time period for trending data (e.g., '1h', '6h', '1d', '7d'). Defaults to '1h' (last hour).

  • size - Number of items per category to return (max 30). Defaults to 10.

  • include_stories - Include trending stories in response. Defaults to true.

  • include_words - Include trending words in response. Defaults to true.

Response

  • trends - Combined trending data containing stories and words.

  • metadata - Request metadata including time period, size, and included data types.

  • errors - Any non-fatal errors encountered during data fetching.

Trending Data Structure

Stories

  • title - Title of the trending story.

  • summary - Summary of the story.

  • score - Trending score.

  • query - Search query used to find the story.

  • related_tokens - List of related crypto tokens (format: "BTC_bitcoin").

  • bullish_sentiment_ratio - Bullish sentiment ratio.

  • bearish_sentiment_ratio - Bearish sentiment ratio.

Words

  • word - The trending word.

  • score - Trending score.

  • slug - Associated project slug (if word is project-related).

  • summary - AI-generated summary of discussions.

  • bullish_summary - Summary of bullish sentiment.

  • bearish_summary - Summary of bearish sentiment.

  • positive_sentiment_ratio - Positive sentiment ratio.

  • negative_sentiment_ratio - Negative sentiment ratio.

  • neutral_sentiment_ratio - Neutral sentiment ratio.

  • positive_bb_sentiment_ratio - Positive bull/bear sentiment ratio.

  • negative_bb_sentiment_ratio - Negative bull/bear sentiment ratio.

  • neutral_bb_sentiment_ratio - Neutral bull/bear sentiment ratio.

  • context - Related words that appear with this trending word.

  • documents_summary - AI-generated summary of related social media discussions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoNumber of items per category to return (max 30). Defaults to 10.
time_periodNoTime period for trending data (e.g., '1h', '6h', '1d', '7d'). This parameter defines how far back to look for trending data. Defaults to '1h' (last hour).
include_wordsNoInclude trending words in the response. Defaults to true.
include_storiesNoInclude trending stories in the response. Defaults to true.

TDQS

A4.8/5.0
Behavior5/5

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

The description discloses that it calls an LLM (adding latency and tighter rate-limit sub-cap) and fetches data in parallel, both beyond what the annotations provide. It also clarifies it returns errors non-fatally. No contradiction with readOnly/openWorld/destructive hints, and the extra context is valuable.

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 well-organized with clear headings and front-loaded purpose and usage guidance. It is longer than average, and the parameter section largely duplicates schema info, but the detailed response structure is essential because there is no output schema, so the length is justified.

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 there is no output schema, the description thoroughly documents the response structure, including nested fields for stories and words, metadata, and error handling. Combined with parameter descriptions and usage guidance, it provides a complete mental model for an agent.

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

Parameters4/5

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

The schema already documents all four parameters with defaults, max values, and descriptions (100% coverage). The description adds practical guidance on how include_words/include_stories interact to control the response scope and performance, which goes beyond the schema's basic field semantics.

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 states it 'fetches trending words, stories, and documents in parallel' and provides a 'unified view of all trending data,' clearly naming the resource and actions. It explicitly differentiates from trending_stories_tool as a superset, so the purpose is distinct and unambiguous.

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?

A dedicated 'When to use vs trending_stories_tool' section gives explicit guidance: use this for combined data, call the sibling if only stories are needed, set include flags to drop unneeded halves, and 'Do not call both tools for the same question.' This is comprehensive and actionable.

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

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: screening assets vs fetching timeseries vs catalog discovery vs charting vs insights discovery/fetch vs trending stories vs combined trends. The overlapping trending tools are explicitly differentiated through a superset relationship with usage guidance.

Naming Consistency3/5

All names are snake_case and most end in '_tool', but the pattern is mixed: some start with verbs (fetch_*, show_*) while others are noun phrases (assets_by_metric_tool, combined_trends_tool, insight_discovery_tool). This is readable but not a consistent verb_noun convention.

Tool Count5/5

8 tools is well-scoped for a crypto analytics server, covering discovery, data retrieval, screening, charting, and content access without bloat or thinness. Each tool earns its place in the workflow.

Completeness4/5

The toolset covers the core workflows: catalog discovery, metric timeseries, asset screening, chart rendering, insights list/fetch, and trending data. Minor gaps include no multi-metric timeseries fetch and a limited set of chart overlay options, but agents can work around these.

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