SEC/FINRA short-sale VOLUME % for a ticker (last few trading days).
Heavy short volume = selling pressure or a squeeze setup (direction-ambiguous). This is daily short VOLUME (flow), NOT short INTEREST (outstanding). Free. Not advice.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| ticker | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It clarifies that it returns daily short VOLUME (flow) not short INTEREST (outstanding), and mentions it is free. However, it omits details like data source freshness, potential delays, or any rate limits, which are important 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three clear, front-loaded sentences with no redundancy. The first sentence states the core purpose, the second adds interpretative context, and the third clarifies key distinctions and disclaimers. Every sentence serves a purpose.
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?
For a simple tool with two parameters and an output schema, the description covers the core functionality, the critical distinction from short interest, and a usage hint. It could mention data freshness or limitations, but overall it is adequately complete for the tool's complexity.
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?
With 0% schema description coverage, the description should explain parameter meaning. It mentions 'last few trading days' loosely tying to the 'days' parameter, but does not specify the expected ticker format, the range or behavior of 'days' (e.g., max value, handling of weekends/holidays). The description adds minimal value beyond the schema.
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?
The description clearly states the tool returns 'SEC/FINRA short-sale VOLUME % for a ticker' (last few days), specifying the verb (returns), resource (short volume percentage), and constraints (last few trading days). It distinguishes itself from sibling tools by focusing on short volume rather than other metrics.
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?
The description gives context like 'Heavy short volume = selling pressure or a squeeze setup (direction-ambiguous)', but does not explicitly state when to use this tool versus alternatives. It notes it is 'Free' and 'Not advice', but lacks guidance on when not to use it or which siblings cover related but different data.
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.