Directory of every as-reported XBRL concept (us-gaap/dei/ifrs-full) a company has filed — unit(s), observation count and period coverage — from the in-house SEC EDGAR companyfacts corpus. Call it FIRST to find the concept tag for /fundamentals/xbrl/{ticker}/{concept}. Point-in-time, no vendor restatement.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| search | No | ||
| 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 transparency burden. It discloses data provenance ('in-house SEC EDGAR companyfacts corpus'), data state ('Point-in-time, no vendor restatement'), and the response shape ('unit(s), observation count and period coverage'). However, it does not mention pagination, rate limits, or other behavioral traits such as how search/limit affect results.
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 concise and dense, with no wasted words. It fronts the core purpose, then the call-first workflow, then the data provenance note, all in three sentences. Every sentence adds value.
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?
The description covers the tool's core purpose and return contents, but no output schema exists. It omits details on the optional parameters (search, limit) and their behavior, and does not describe the overall response structure beyond listing included fields. For a discovery/directory tool with one required parameter, it is adequate but incomplete.
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 description coverage is only 33%: only 'ticker' has a description in the schema. The description does not explain the 'search' or 'limit' parameters at all, nor does it add detail on how 'ticker' should be formatted. Given low schema coverage, the description should compensate but does not.
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's purpose: it is a directory of XBRL concepts filed by a company, with specific attributes (unit(s), observation count, period coverage). It distinguishes itself by explicitly positioning it as the first call to find a concept tag for the XBRL endpoint, which sets it apart from sibling tools like company_facts or xbrl.
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 explicitly instructs to 'Call it FIRST to find the concept tag for /fundamentals/xbrl/{ticker}/{concept}', which is clear when-to-use guidance. It does not provide explicit when-not-to-use or alternative tool comparisons, but the directive is strong and actionable.
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.
