f1ow

Twitter Sentiment

sentiment_twitter

Read-only

Searches Twitter/X and returns matching tweets with engagement (likes, retweets, replies, views) and author reach (followers, verified). Raw crowd voice for judging social sentiment on a coin or topic. Sorted by relevance; set latest=true for the newest tweets instead.

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Max tweets to return (1-50). Defaults to 10.
`query`	Yes	Search keywords, $cashtag or #hashtag, e.g. "$BTC" or "bitcoin etf".
`latest`	No	Return the newest tweets instead of top-relevance. Defaults to false.
`minLikes`	No	Only tweets with at least this many likes. Useful for cutting spam in top-relevance mode; avoid combining with latest=true (brand-new tweets have no likes yet, so it returns nothing). Defaults to 0.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`tweets`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations provide readOnlyHint=true, so the description doesn't need to restate safety. It adds useful behavioral detail beyond the schema: results are 'Sorted by relevance,' the latest=true switch changes that ordering, and the response includes engagement and author-reach fields. This helps an agent anticipate behavior before invoking.

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?

Three tight sentences with no filler. The core action and output are in the first sentence, the use case in the second, and the sorting behavior in the third. Every sentence earns its place.

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?

For a search tool with a full input schema, an output schema, and readOnlyHint=true, the description covers the essential context: what it searches, what fields it returns, which use case it serves, and the key sorting option. Nothing an agent needs to decide whether to call it or how to interpret its result is missing.

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 description coverage is 100%, with every parameter already documented in the input schema. The description reinforces the latest parameter and the relevance default, but doesn't add meaningful parameter-level meaning beyond what the schema provides. Baseline 3 is appropriate.

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?

Description opens with a specific verb-resource pair: 'Searches Twitter/X and returns matching tweets with engagement...' It names the platform, the action, and the exact returned data (likes, retweets, replies, views, followers, verified). It also frames the use case as 'Raw crowd voice for judging social sentiment,' which distinguishes it from news or fear/greed indices among siblings.

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 states the intended context: 'for judging social sentiment on a coin or topic,' which tells an agent when this tool is appropriate. It also explains the relevance-versus-latest sorting choice. It doesn't explicitly name sibling alternatives or exclusions, but the use-case framing plus the sibling names (news_search, sentiment_fear_greed) make the decision sufficiently clear.

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

A4/5.0

Disambiguation4/5

Tools are grouped by clear prefixes and mostly target distinct resources; market_quotes vs research_token_view, market_top_movers vs market_trending, and funding_current vs market_quotes have some field or purpose overlap, but descriptions draw enough scope boundaries for an agent to choose correctly in most cases. No two tools are truly interchangeable.

Naming Consistency5/5

All 30 tool names use lowercase snake_case with a consistent domain-prefix convention such as market_, flow_, research_, sentiment_, and catalysts_, making the surface predictable. Even helpers like utc_time and risk_position_size fit the same noun-oriented pattern without style mixing.

Tool Count2/5

At 30 tools this set crosses the 'too many' threshold, and several tools reproduce data already available through broader ones such as research_token_view and market_quotes. The breadth is defensible for a crypto-research platform, but the surface feels heavy and could be consolidated.

Completeness5/5

The tool set covers the read-only crypto research workflow thoroughly: market data, candles, derivatives, funding, OI, order book, whale flows, ETF flows, on-chain metrics, news, sentiment, prediction odds, technicals, regime/positioning scans, and position sizing. It also includes health and UTC helpers that close practical workflow gaps, with no obvious dead ends.

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