Blended retail sentiment for a ticker across X, StockTwits, and Reddit.
Use this to corroborate (or challenge) an analyst's view with the broader crowd.
sources defaults to all three.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| ticker | Yes | ||
| sources | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so description carries full burden. It does not explain how the blend is computed, data freshness, rate limits, or any caveats about the aggregation method. Only mentions default sources.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with purpose. Every word adds value. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Brief but covers core purpose. With an output schema present, return values may be documented, but the description lacks details on sentiment calculation methodology, which is important for trusting the blend.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must explain parameters. It clarifies that `sources` defaults to all three (null means all), but does not describe valid values for sources, nor does it explain `ticker` or `limit` beyond obvious semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it provides blended retail sentiment across three platforms (X, StockTwits, Reddit) for a ticker, and distinguishes itself from sibling tools like reddit_attention or stocktwits_symbol which target single platforms.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to use for corroborating or challenging analyst views with crowd sentiment. Does not explicitly state when not to use, but the sibling tools imply single-source alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes (e.g., analyst_views fetches views, analyst_debate compares them, analyst_track_record scores accuracy). Some overlap exists between sentiment tools (stocktwits_symbol, ticker_social_sentiment) but descriptions clarify boundaries. Overall, an agent can differentiate them.
Naming is mostly lowercase with underscores, but conventions vary: some use prefixes (analyst_, direction_review_), some are single words (quote, leaderboard), and others are verb_noun (score_ticker, screen_stocks). This inconsistency makes patterns less predictable, though prefixes help group related tools.
With 24 tools, the server is slightly above the ideal range of 3-15 for coherence. While each tool seems justified for the financial analysis domain, the volume could be overwhelming. Some tools (e.g., tweet_store_stats, direction_review_batch) are operator-only, reducing the surface for typical agents.
The tool set covers core workflows: fetching analyst views, tracking accuracy, SEC fundamentals, insider activity, material events, live quotes, social sentiment, and screening. Gaps like earnings calendar or portfolio management are minor given the focus on analyst-driven analysis. The operator tools for direction review add internal completeness.