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tengu-firm

by io.github.Hlobo-dev

tengu_v3_earnings_next

Use when: the user asks for a specific ticker's next earnings date, when a company reports, the earnings calendar entry for a name, or anything of the form "when is X's next earnings?". This is the CANONICAL multi-source consensus tool — fans out to market-data, market-data, newswire, news, and web search in parallel; reconciles via primacy-weighted majority; returns a single canonical answer with per-source breakdown, deduplicated citations, and a vendor_coverage_alert when paid vendors silently lack data the web confirms. _meta.confidence is high (≥2 sources agree, or 1 primary source = market-data|web_search), medium (1 secondary source), or low (no confirmed date — projections demoted to _meta.next_earnings_date_projection_only). Top-level summary field for FE rendering. 15-min cache.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesPath parameter 'ticker' (required).

TDQS

A4.5/5.0
Behavior5/5

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

No annotations are present, so the description carries the full burden. It discloses multi-source fan-out, reconciliation via primacy-weighted majority, per-source breakdowns, deduplicated citations, vendor_coverage_alert, confidence level definitions, the projection-only flag, and 15-min caching. This is exemplary transparency.

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?

The description is dense but front-loaded with usage guidance, and every clause in the two sentences adds value. There is no repetition or filler; it efficiently packs operational detail into a compact form.

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?

It thoroughly covers the return structure (summary, per-source breakdown, citations), confidence semantics, data quality alert, caching, and the low-confidence edge case with projection-only flag. This equips an agent to both invoke and interpret results without an output schema.

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?

The schema has a single parameter 'ticker' with a trivial description ('Path parameter'). The description adds no additional meaning about ticker format or examples. Schema coverage is 100%, so the baseline of 3 applies.

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?

The description opens with 'Use when' and explicitly defines the query intent ('next earnings date', 'when a company reports'), followed by a clear statement that it returns a single canonical answer. It distinguishes itself from siblings by labeling itself the 'CANONICAL multi-source consensus tool' and specifying the exact resource (next earnings date for a ticker).

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?

It provides concrete usage triggers, including the natural language form 'when is X's next earnings?', and positions itself as canonical. However, it does not explicitly name alternative tools or state when not to use it, though the canonical label implies preference over other earnings-related tools.

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

C2.9/5.0
Disambiguation2/5

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.

Naming Consistency2/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources