FINRA bi-monthly short interest: short_interest_shares, short_interest_pct_of_float, days_to_cover, short_interest_change_pct_30d (vs prior settlement), avg_daily_volume_at_settlement. Call for 'how shorted is X?' / squeeze questions. borrow_fee_pct_annualized is null — see /intel/borrow_cost. Market-data FINRA re-publish; 6h cache.
| Name | Required | Description | Default |
|---|---|---|---|
| ticker | Yes | Path parameter 'ticker' (required). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It discloses that the data is a FINRA re-publish, bi-monthly in frequency, and cached for 6 hours, and it explicitly notes that borrow_fee_pct_annualized is always null. It does not mention rate limits or whether multiple tickers can be requested, but the disclosures given are valuable and clear.
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 compact and information-dense: fields, use case, null behavior, alternative tool, data source, and cache all in three short sentences. No wasted words, and the most important information is front-loaded.
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?
Given there is no output schema, the description lists the return fields, which is helpful for an agent. It also notes the null field and cache behavior. It is complete enough for a simple single-ticker tool, but could briefly mention that it returns data for one ticker and that the value is as of the most recent settlement date.
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?
The schema has 100% coverage for the single parameter (ticker) with a basic description, so the baseline is 3. The tool description does not add any extra meaning about the ticker format, validation, or behavior beyond what the schema states.
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 identifies the tool as providing FINRA bi-monthly short interest data and enumerates the specific fields returned. It also distinguishes itself from siblings by noting that borrow fee is null and directing to a separate tool, and by providing example use cases like 'how shorted is X?' and squeeze questions.
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 explicit use cases ('Call for how shorted is X? / squeeze questions') and points to an alternative tool for borrow cost, which counts as when-not-to-use. It does not explicitly contrast with short_interest_history but the context clues are sufficient for an agent to differentiate.
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.
With 336 tools, there is substantial overlap. Over a dozen health/status tools share nearly identical 'is the system healthy?' descriptions (e.g., tengu_status, tengu_ready, tengu_ml_health, tengu_v3_system_health, tengu_v3_stream_status), and multiple single-ticker analysis (tengu_ml_predict, tengu_copilot_score_ticker, tengu_v3_intel_ml_prediction) and top-picks (tengu_copilot_top_picks, tengu_ml_top_picks, tengu_v3_trade_setups) tools have poorly defined boundaries. Agents would frequently misselect.
The server mixes no-version (tengu_crypto), v2 (tengu_v2_drift), v3 (tengu_v3_intel_*), and copilot (tengu_copilot_*) families, and within families there is inconsistent verb/noun ordering (tengu_v3_research_fetch_url vs tengu_v3_news_summary). While subfamilies like tengu_v3_private_markets_* are internally consistent, the overall naming pattern is chaotic and unpredictable.
336 tools is far beyond any reasonable tool set size, even for an all-in-one financial data platform. This extreme count creates choice paralysis, high latency in tool selection, and makes the server effectively unusable for autonomous agents. The calibration guideline marks 50+ as extreme; this is nearly 7x that threshold.
The platform covers a vast domain: equity and crypto prices, fundamentals, insider trading, options, news (including crypto and FX), private markets, streaming data, risk metrics, and execution planning. There are minor gaps (no direct multi-ticker comparison tool, no order placement), but the surface is remarkably comprehensive for an analysis-focused server.
