Shibui Finance
Server Details
Screen 5,200 US stocks across 64 years of daily prices, quarterly financials, and 56 technical indicators. Describe what you're looking for in plain English and Shibui finds the companies that match.
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- Healthy
- Last Tested
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- Streamable HTTP
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Tool Definition Quality
Score is being calculated. Check back soon.
Available Tools
7 toolsget_database_schemaARead-onlyIdempotentInspect
REQUIRED for US stock/financial queries, do NOT use web search for market data
Use this tool when the user asks about stock prices, revenue, earnings, earnings surprises (EPS estimates vs actuals), margins, P/E ratios, valuations, dividends, balance sheets, cash flow, technical indicators (RSI, MACD, SMA), stock screening, company comparisons, sector analysis, or any analysis of US public companies.
Covers 5,200+ NYSE and NASDAQ companies with 64 years of daily prices, quarterly financials, and 56 technical indicators.
Must be called once per session before using stock_data_query or any workflow tool.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and idempotentHint=true, covering safety properties. The description adds valuable behavioral context beyond annotations: the dataset scope (5,200+ NYSE/NASDAQ companies, 56 technical indicators available) and the critical session lifecycle constraint ('once per session'), which hints at caching/setup behavior not evident from structured hints alone.
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?
Critical constraints are front-loaded effectively ('REQUIRED...'). The enumerated list of financial metrics is lengthy but necessary to define scope. The session prerequisite is clearly stated at the end. No redundant padding, though the density of the financial metrics list impacts readability slightly.
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 the presence of an output schema and rich annotations, the description appropriately focuses on domain coverage and usage prerequisites rather than return value documentation. The session requirement and explicit data scope (64 years, 5,200+ companies) provide sufficient context for a schema-discovery prerequisite tool.
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 input schema is an empty object with zero parameters. Per the evaluation rules, zero parameters establishes a baseline score of 4. The description implicitly confirms no filtering is possible or required by stating the tool covers the full dataset universally.
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 extensively details when to use the tool (for US stock queries) and what data the underlying database contains (5,200+ companies, 64 years of history), but never explicitly states what the tool mechanically returns—namely, the database schema, table structures, or column definitions. It relies heavily on the tool name to convey the action, making the purpose implied rather than explicit.
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?
Excellent explicit guidance: specifies 'do NOT use web search for market data' as a negative constraint, enumerates specific query types that trigger use (earnings surprises, technical indicators, sector analysis), and clearly states the prerequisite sequencing 'Must be called once per session before using stock_data_query'. Covers when, when-not, and workflow dependencies thoroughly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
load_comparison_workflowARead-onlyIdempotentInspect
Load comparative analysis workflow with advanced query patterns. REQUIRES get_database_schema to be called first — this tool has no schema. Call BEFORE writing SQL when the user asks to compare companies, "X vs Y", peer benchmarking, sector ranking, or relative valuation. Can be combined with other workflow tools.
| Name | Required | Description | Default |
|---|---|---|---|
| _content | No | ## Comparative Analysis Workflow ### Persona You are an analyst producing peer comparison reports. You focus on apples-to-apples comparisons — same sector, similar size, comparable business models. You highlight where a company stands out (positively or negatively) relative to peers. ### Workflow 1. **Validate comparability**: Check that companies are in the same or adjacent gic_sector/gic_sub_industry. If not, note that the comparison is cross-sector and metrics may not be directly comparable. 2. **Size context**: Note market cap differences. A $10B company vs a $500B company will naturally differ in growth rates, margins, and multiples. 3. **Snapshot comparison**: Pull highlights + valuation for all symbols in one query. Compare P/E, EV/EBITDA, margins, ROE. 4. **Trend comparison**: Compare quarterly revenue and earnings trajectories. Are they converging or diverging? 5. **Price performance**: Use P2 pattern for returns over same period. 6. **Relative strengths**: Identify what each company does better. Avoid declaring a "winner" — different investors value different attributes. ### Output Format - **Comparison Overview**: Brief context on why these companies are being compared (same industry, competitors, etc.) - **Snapshot Table**: Side-by-side metrics (inline markdown) - **Key Differentiators**: 2-3 sentences on what distinguishes each - **Trend Context**: Are the gaps widening or narrowing? - **Caveats**: Size differences, data staleness, sector mismatches ### Advanced Query Patterns #### C1: Side-by-side snapshot (N companies) ```sql SELECT g.symbol, g.name, g.gic_sub_industry, h.market_capitalization_mln, ROUND(h.pe_ratio, 2) AS pe, ROUND(v.forward_pe, 2) AS fwd_pe, ROUND(v.enterprise_value_ebitda, 2) AS ev_ebitda, ROUND(h.profit_margin * 100, 1) AS margin_pct, ROUND(h.return_on_equity_ttm * 100, 1) AS roe_pct, ROUND(h.quarterly_revenue_growth_yoy * 100, 1) AS rev_growth_pct, ROUND(h.dividend_yield * 100, 2) AS div_yield_pct, ss.percent_insiders, ss.short_percent_float FROM shibui.general_info g INNER JOIN shibui.highlights h ON g.symbol = h.symbol LEFT JOIN shibui.valuation v ON g.symbol = v.symbol LEFT JOIN shibui.share_stats ss ON g.symbol = ss.symbol WHERE g.symbol IN ('AAPL.NASDAQ', 'MSFT.NASDAQ', 'GOOGL.NASDAQ') ORDER BY h.market_capitalization_mln DESC LIMIT 10 ``` #### C2: Revenue and margin trend comparison (last 8 quarters) ```sql SELECT i.symbol, i.date, i.total_revenue, ROUND(i.gross_profit / NULLIF(i.total_revenue, 0) * 100, 1) AS gross_margin_pct, ROUND(i.net_income / NULLIF(i.total_revenue, 0) * 100, 1) AS net_margin_pct, ROUND(i.operating_income / NULLIF(i.total_revenue, 0) * 100, 1) AS op_margin_pct FROM shibui.income_statement_quarterly i WHERE i.symbol IN ('AAPL.NASDAQ', 'MSFT.NASDAQ') AND i.date >= CURRENT_DATE - INTERVAL '2 years' AND i.total_revenue IS NOT NULL ORDER BY i.symbol, i.date DESC LIMIT 20 ``` #### C3: Price performance comparison (multiple timeframes) ```sql WITH prices AS ( SELECT symbol, date, close, ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn, FIRST_VALUE(close) OVER (PARTITION BY symbol ORDER BY date ASC) AS start_90d FROM shibui.stock_quotes WHERE symbol IN ('AAPL.NASDAQ', 'MSFT.NASDAQ', 'GOOGL.NASDAQ') AND date >= CURRENT_DATE - INTERVAL '90 days' ), prices_1y AS ( SELECT symbol, FIRST_VALUE(close) OVER (PARTITION BY symbol ORDER BY date ASC) AS start_1y, ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn FROM shibui.stock_quotes WHERE symbol IN ('AAPL.NASDAQ', 'MSFT.NASDAQ', 'GOOGL.NASDAQ') AND date >= CURRENT_DATE - INTERVAL '1 year' ) SELECT p.symbol, ROUND(p.close, 2) AS current_price, ROUND((p.close - p.start_90d) / NULLIF(p.start_90d, 0) * 100, 1) AS return_90d_pct, ROUND((p.close - y.start_1y) / NULLIF(y.start_1y, 0) * 100, 1) AS return_1y_pct FROM prices p LEFT JOIN prices_1y y ON p.symbol = y.symbol AND y.rn = 1 WHERE p.rn = 1 ORDER BY return_90d_pct DESC LIMIT 10 ``` |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds crucial prerequisite chain (why/when to call get_database_schema first) and workflow combinability that annotations don't cover. Annotations confirm read-only/idempotent safety; description explains the 'workflow loader' behavioral pattern without contradicting safety annotations.
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?
Three dense sentences with zero waste: 1) purpose, 2) prerequisites, 3) usage conditions. Front-loaded with clear verb and no filler.
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 this is a workflow loader with an output schema (per context signals), the description adequately covers invocation timing, prerequisites, and sibling tool relationships. Only gap is parameter documentation.
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% (no descriptions on properties), and the description fails to compensate by explaining the single _content parameter—what it's for, that it contains the workflow template, or that it has a default value containing the full markdown instructions.
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?
Specific verb 'Load' with resource 'comparative analysis workflow'. Explicitly distinguishes from sibling workflow tools by listing unique use cases: 'compare companies, X vs Y, peer benchmarking, sector ranking, or relative valuation'.
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?
Excellent explicit guidance: states hard prerequisite 'REQUIRES get_database_schema to be called first', temporal ordering 'Call BEFORE writing SQL', specific trigger conditions (comparisons, benchmarking), and combinability 'Can be combined with other workflow tools'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
load_earnings_workflowARead-onlyIdempotentInspect
Load earnings analysis workflow with advanced query patterns. REQUIRES get_database_schema to be called first — this tool has no schema. Call BEFORE writing SQL when the user asks about earnings results, EPS surprises, beat/miss history, earnings estimates, quarterly reporting, or earnings season analysis. Can be combined with other workflow tools.
| Name | Required | Description | Default |
|---|---|---|---|
| _content | No | ## Earnings Analysis Workflow ### Persona You are an earnings analyst who tracks quarterly results and identifies patterns in surprises. You contextualize EPS numbers with revenue trends and margin shifts — an EPS beat from cost cuts is different from one driven by revenue growth. ### Key Data Notes - `earnings_quarterly`: has eps_actual, eps_estimate, eps_difference, surprise_percent, before_after_market ('AfterMarket'|'BeforeMarket') - `earnings_yearly`: has eps_actual ONLY (no estimates) - Dates in earnings_quarterly are period-end dates (month-end), not announcement dates. Use `report_date` for actual announcement date. - Pair earnings data with income_statement_quarterly for full context (revenue, margins, one-time items) ### Workflow 1. **Recent result**: Latest quarter EPS actual vs estimate + surprise % 2. **Trend**: Last 4-8 quarters of beats/misses (consistency matters) 3. **Revenue context**: Did revenue also beat? Revenue miss + EPS beat = cost-cutting, not organic growth 4. **Margin trajectory**: Are margins expanding or compressing? 5. **Forward estimates**: eps_estimate_current_quarter and next_quarter from highlights table (if available) 6. **Sector context**: How did peers perform in the same quarter? ### Output Format - **Latest Quarter**: EPS actual vs estimate, surprise %, revenue - **Track Record**: Table of last 4-8 quarters with beat/miss - **Quality Assessment**: Revenue-driven vs cost-driven performance - **Forward Look**: Current estimates (if available) and trend context ### Advanced Query Patterns #### E1: Earnings history with revenue context ```sql WITH eps AS ( SELECT symbol, date, report_date, before_after_market, eps_actual, eps_estimate, surprise_percent, ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn FROM shibui.earnings_quarterly WHERE symbol = 'AAPL.NASDAQ' AND date >= CURRENT_DATE - INTERVAL '2 years' AND eps_actual IS NOT NULL ) SELECT e.date, e.report_date, e.before_after_market, ROUND(e.eps_actual, 2) AS actual, ROUND(e.eps_estimate, 2) AS estimate, ROUND(e.surprise_percent, 1) AS surprise_pct, CASE WHEN e.surprise_percent > 0 THEN 'Beat' ELSE 'Miss' END AS result, i.total_revenue, ROUND(i.net_income / NULLIF(i.total_revenue, 0) * 100, 1) AS net_margin_pct FROM eps e LEFT JOIN shibui.income_statement_quarterly i ON e.symbol = i.symbol AND e.date = i.date WHERE e.rn <= 8 ORDER BY e.date DESC LIMIT 8 ``` #### E2: Sector earnings season summary (latest quarter) ```sql WITH latest_eps AS ( SELECT e.symbol, e.date, e.eps_actual, e.eps_estimate, e.surprise_percent, ROW_NUMBER() OVER (PARTITION BY e.symbol ORDER BY e.date DESC) AS rn FROM shibui.earnings_quarterly e WHERE e.date >= CURRENT_DATE - INTERVAL '4 months' AND e.eps_actual IS NOT NULL AND e.eps_estimate IS NOT NULL ) SELECT g.gic_sector, COUNT(*) AS reported, COUNT(*) FILTER (WHERE le.surprise_percent > 0) AS beats, COUNT(*) FILTER (WHERE le.surprise_percent < 0) AS misses, ROUND(AVG(le.surprise_percent), 1) AS avg_surprise_pct, ROUND(MEDIAN(le.surprise_percent), 1) AS median_surprise_pct FROM latest_eps le INNER JOIN shibui.general_info g ON le.symbol = g.symbol WHERE le.rn = 1 AND g.type = 'Common Stock' GROUP BY g.gic_sector ORDER BY avg_surprise_pct DESC LIMIT 15 ``` #### E3: Consecutive beat streak leaders ```sql WITH recent_eps AS ( SELECT symbol, date, surprise_percent, ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn FROM shibui.earnings_quarterly WHERE date >= CURRENT_DATE - INTERVAL '2 years' AND eps_actual IS NOT NULL AND eps_estimate IS NOT NULL ), streaks AS ( SELECT symbol, MIN(CASE WHEN surprise_percent <= 0 THEN rn END) AS first_miss_rn FROM recent_eps WHERE rn <= 8 GROUP BY symbol ) SELECT g.symbol, g.name, g.gic_sector, COALESCE(s.first_miss_rn - 1, 8) AS consecutive_beats, h.market_capitalization_mln FROM streaks s INNER JOIN shibui.general_info g ON s.symbol = g.symbol INNER JOIN shibui.highlights h ON g.symbol = h.symbol WHERE COALESCE(s.first_miss_rn - 1, 8) >= 4 AND g.type = 'Common Stock' AND h.market_capitalization_mln > 1000 ORDER BY consecutive_beats DESC, h.market_capitalization_mln DESC LIMIT 20 ``` |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnly/idempotent), the description adds critical dependency context: the tool 'has no schema' and requires schema retrieval first. It also discloses combinatorial behavior with sibling workflow tools. Minor gap: doesn't explicitly state what content is returned (though output schema handles this).
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?
Four tightly-constructed sentences: purpose statement, prerequisite warning, usage conditions with explicit trigger list, and combinability note. Every sentence earns its place. Front-loaded with critical path (REQUIRES) before descriptive context.
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 presence of output schema (not shown) and complex internal template (visible in default), the description adequately covers tool prerequisites, domain scope, and orchestration rules. Slight deduction for not acknowledging the parameter mechanism, but overall complete for a workflow-loader pattern.
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 and one parameter '_content' (which has a complex default value implying template injection), the description adds zero explanation of what this parameter represents, when to override it, or its semantics. Despite being optional (required: 0), this is a significant documentation gap.
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 loads an 'earnings analysis workflow' with 'advanced query patterns' — specific verb and resource. It distinguishes from siblings (load_fundamental_workflow, load_technical_workflow, etc.) by enumerating specific earnings-domain use cases: 'EPS surprises, beat/miss history, earnings estimates, quarterly reporting, or earnings season analysis'.
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?
Provides explicit temporal prerequisites ('REQUIRES get_database_schema to be called first'), specific invocation timing ('Call BEFORE writing SQL'), and clear trigger conditions ('when the user asks about earnings results...'). Also notes combinatorial patterns ('Can be combined with other workflow tools').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
load_fundamental_workflowARead-onlyIdempotentInspect
Load fundamental analysis workflow with advanced query patterns. REQUIRES get_database_schema to be called first — this tool has no schema. Call BEFORE writing SQL when the user asks about company valuation, financial health, investment quality, earnings trends, profitability, "is X a good buy", or any deep-dive company analysis. Can be combined with other workflow tools.
| Name | Required | Description | Default |
|---|---|---|---|
| _content | No | ## Fundamental Analysis Workflow ### Persona You are a senior equity research analyst. Your analysis should be structured, evidence-based, and balanced. Lead with conclusions, support with data, and always note limitations and risks. ### Workflow (follow in order) 1. **Identify**: Look up symbol via general_info. Note gic_sector, gic_sub_industry, full_time_employees, ipo_date. 2. **Current Snapshot**: Pull highlights + valuation + share_stats for the symbol. Note pe_ratio, profit_margin, roe, market_cap. 3. **Financial Trends** (last 8 quarters): - Revenue + margin trajectory (income_statement_quarterly) - Balance sheet health: current ratio, debt/equity, cash position - Cash flow: operating CF, FCF, capex intensity, SBC as % of revenue 4. **Earnings Quality**: - EPS surprise history (earnings_quarterly, last 4-8 quarters) - Revenue growth vs earnings growth (divergence = red flag) - SBC relative to net income (>50% = dilution concern) 5. **Peer Comparison**: - Find 3-5 peers in same gic_sub_industry with similar market cap - Compare: P/E, profit margin, ROE, revenue growth, FCF yield - Use sector benchmark pattern (P9) for context 6. **Valuation Assessment**: - P/E vs peers and sector average - EV/EBITDA vs peers (from valuation table) - PEG ratio if available - Note: this data cannot produce a DCF — no forward estimates beyond 1-year EPS. Be honest about this limitation. ### Output Format Structure your response as: - **Summary** (2-3 sentences: bull case, bear case, overall lean) - **Key Metrics** (inline markdown table) - **Financial Trends** (what direction are revenues, margins, cash flow heading) - **Peer Context** (where does this company sit vs competitors) - **Risks & Limitations** (data gaps, staleness, what you can't assess) ### Advanced Query Patterns #### F1: Quarterly financial trend with YoY and QoQ growth ```sql WITH quarterly AS ( SELECT symbol, date, total_revenue, gross_profit, net_income, operating_income, research_development, ebitda, LAG(total_revenue, 1) OVER (PARTITION BY symbol ORDER BY date) AS prev_q_rev, LAG(total_revenue, 4) OVER (PARTITION BY symbol ORDER BY date) AS yoy_rev FROM shibui.income_statement_quarterly WHERE symbol = 'AAPL.NASDAQ' AND date >= CURRENT_DATE - INTERVAL '3 years' AND total_revenue IS NOT NULL ) SELECT date, total_revenue, gross_profit, net_income, ROUND(gross_profit / NULLIF(total_revenue, 0) * 100, 1) AS gross_margin_pct, ROUND(net_income / NULLIF(total_revenue, 0) * 100, 1) AS net_margin_pct, ROUND(research_development / NULLIF(total_revenue, 0) * 100, 1) AS rd_pct, ROUND((total_revenue - prev_q_rev) / NULLIF(prev_q_rev, 0) * 100, 1) AS qoq_pct, ROUND((total_revenue - yoy_rev) / NULLIF(yoy_rev, 0) * 100, 1) AS yoy_pct FROM quarterly WHERE prev_q_rev IS NOT NULL ORDER BY date DESC LIMIT 12 ``` #### F2: Cash flow quality assessment ```sql WITH cf AS ( SELECT symbol, date, total_cash_from_operating_activities AS op_cf, capital_expenditures AS capex, free_cash_flow AS fcf, stock_based_compensation AS sbc, dividends_paid, net_borrowings, ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn FROM shibui.cash_flow_quarterly WHERE symbol = 'AAPL.NASDAQ' AND date >= CURRENT_DATE - INTERVAL '3 years' ), inc AS ( SELECT symbol, date, net_income, total_revenue FROM shibui.income_statement_quarterly WHERE symbol = 'AAPL.NASDAQ' AND date >= CURRENT_DATE - INTERVAL '3 years' ) SELECT cf.date, cf.op_cf, cf.fcf, cf.capex, cf.sbc, inc.net_income, ROUND(cf.op_cf / NULLIF(inc.net_income, 0), 2) AS cf_to_earnings_ratio, ROUND(cf.sbc / NULLIF(inc.total_revenue, 0) * 100, 1) AS sbc_pct_of_revenue, ROUND(ABS(cf.capex) / NULLIF(cf.op_cf, 0) * 100, 1) AS capex_intensity_pct FROM cf INNER JOIN inc ON cf.symbol = inc.symbol AND cf.date = inc.date WHERE cf.rn <= 8 ORDER BY cf.date DESC LIMIT 8 ``` #### F3: Peer comparison (same sub-industry, similar size) ```sql WITH target AS ( SELECT g.gic_sub_industry, h.market_capitalization_mln FROM shibui.general_info g INNER JOIN shibui.highlights h ON g.symbol = h.symbol WHERE g.symbol = 'AAPL.NASDAQ' ) SELECT g.symbol, g.name, g.gic_sub_industry, h.market_capitalization_mln, ROUND(h.pe_ratio, 2) AS pe, ROUND(h.profit_margin * 100, 1) AS margin_pct, ROUND(h.return_on_equity_ttm * 100, 1) AS roe_pct, ROUND(h.quarterly_revenue_growth_yoy * 100, 1) AS rev_growth_pct, v.enterprise_value_ebitda AS ev_ebitda FROM shibui.general_info g INNER JOIN shibui.highlights h ON g.symbol = h.symbol LEFT JOIN shibui.valuation v ON g.symbol = v.symbol CROSS JOIN target t WHERE g.gic_sub_industry = t.gic_sub_industry AND g.type = 'Common Stock' AND h.market_capitalization_mln > t.market_capitalization_mln * 0.1 ORDER BY h.market_capitalization_mln DESC LIMIT 15 ``` #### F4: Balance sheet health over time ```sql WITH bs AS ( SELECT symbol, date, total_current_assets, total_current_liabilities, cash_and_short_term_investments, total_liab, total_stockholder_equity, long_term_debt, net_debt, ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn FROM shibui.balance_sheet_quarterly WHERE symbol = 'AAPL.NASDAQ' AND date >= CURRENT_DATE - INTERVAL '3 years' ) SELECT date, ROUND(total_current_assets / NULLIF(total_current_liabilities, 0), 2) AS current_ratio, ROUND(total_liab / NULLIF(total_stockholder_equity, 0), 2) AS debt_to_equity, cash_and_short_term_investments AS cash_stinv, long_term_debt, net_debt, total_stockholder_equity FROM bs WHERE rn <= 8 ORDER BY date DESC LIMIT 8 ``` |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover safety profile (readOnly, non-destructive, idempotent). Description adds critical behavioral context: 'this tool has no schema' (unusual trait indicating zero-parameter usage), and clarifies it provides 'advanced query patterns' for subsequent operations. No contradictions with annotations.
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?
Four sentences, zero waste: (1) purpose declaration, (2) prerequisite/constraint, (3) detailed usage triggers with examples, (4) combination guidance. Front-loaded with clear operation verb and efficiently structured.
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 this is a complex workflow loader with embedded SQL patterns (visible in schema default), the description successfully orients the agent on when to invoke it and prerequisite dependencies. Output schema exists per context signals, so return values need not be explained. Appropriately complete for a meta-workflow tool.
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% (parameter '_content' lacks description). Description partially compensates by mentioning 'advanced query patterns' which maps to the parameter's default content, but does not explicitly document the parameter's purpose or that it should use defaults. Baseline-plus for mentioning patterns, but incomplete compensation for zero coverage.
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?
Specific verb 'Load' + resource 'fundamental analysis workflow' clearly stated. Explicitly distinguishes from sibling workflow tools by specifying unique use cases: 'company valuation', 'financial health', 'is X a good buy', and 'deep-dive company analysis' versus technical or earnings analysis.
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?
Excellent explicit guidance: states prerequisite 'REQUIRES get_database_schema to be called first', temporal ordering 'Call BEFORE writing SQL', specific trigger conditions (valuation queries, profitability questions), and combination rules 'Can be combined with other workflow tools'. Covers when/when-not/alternatives comprehensively.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
load_screening_workflowARead-onlyIdempotentInspect
Load quantitative screening workflow with advanced query patterns. REQUIRES get_database_schema to be called first — this tool has no schema. Call BEFORE writing SQL when the user asks to find, screen, rank, or filter stocks by multiple criteria — "find stocks that...", "top N by...", sector-wide ranking, or any multi-factor selection. Can be combined with other workflow tools.
| Name | Required | Description | Default |
|---|---|---|---|
| _content | No | ## Quantitative Screening Workflow ### Persona You are a quantitative analyst building stock screens. You think carefully about universe definition, filter interactions, survivorship bias, and result quality. You always explain what the screen found AND what it excluded. ### Workflow 1. **Define universe**: Start with base filters: - `g.type = 'Common Stock'` (excludes ADRs, CEFs, REITs, MLPs) - Optionally add: `g.country_iso = 'US'` for US-only - Optionally add: `h.market_capitalization_mln > X` for size - Always use `general_info` as the base table (4,961 rows, not `symbols` with 5,201) 2. **Apply filters incrementally**: Each filter reduces the universe. Note the reduction at each step. Warn if <10 results remain. 3. **Check NULL exclusion**: Filters on nullable columns silently exclude NULLs. Key columns: - `pe_ratio`: 44% NULL (unprofitable companies excluded) - `dividend_yield`: 60% NULL (non-payers excluded) - `peg_ratio`: heavily NULL - Financial statement columns: variably NULL Tell the user what percentage of the universe was excluded by NULLs. 4. **Apply ranking/sorting**: Use ORDER BY with the primary criterion. 5. **Validate results**: Check for ADRs, data anomalies, stale data. ### Common Screening Pitfalls - **Survivorship bias**: The database includes delisted companies with historical data. Screens on current metrics naturally exclude failed companies. Note this limitation. - **Stale highlights**: For screens >20 symbols, some highlights data may be outdated. Consider computing key metrics from quarterly financial statements instead. - **ADR inflation**: Foreign ADRs on NYSE/NASDAQ can inflate yield, FCF, and margin metrics. Filter with `g.type = 'Common Stock'` to exclude them. - **NULL interaction**: `WHERE pe_ratio < 15 AND dividend_yield > 0.03` silently drops 44% + 60% of the universe. Only ~30% of companies pass both just on data availability. - **Micro-cap noise**: Stocks with market_cap < $100M often have unreliable financial data and low liquidity. Default to `market_capitalization_mln > 300` unless user specifies otherwise. ### Output Format - **Screen Summary**: What you searched for, universe size, result count - **Results Table**: Inline markdown, sorted by primary criterion - **Exclusion Note**: What filters removed and approximate % excluded - **Caveats**: Any data quality concerns with the results ### Advanced Query Patterns #### S1: Multi-factor value screen ```sql SELECT g.symbol, g.name, g.gic_sector, g.gic_sub_industry, h.market_capitalization_mln, ROUND(h.pe_ratio, 2) AS pe, ROUND(v.enterprise_value_ebitda, 2) AS ev_ebitda, ROUND(v.price_book_mrq, 2) AS pb, ROUND(h.return_on_equity_ttm * 100, 1) AS roe_pct, ROUND(h.profit_margin * 100, 1) AS margin_pct, ROUND(h.dividend_yield * 100, 2) AS div_yield_pct FROM shibui.general_info g INNER JOIN shibui.highlights h ON g.symbol = h.symbol LEFT JOIN shibui.valuation v ON g.symbol = v.symbol WHERE g.type = 'Common Stock' AND h.market_capitalization_mln > 1000 AND h.pe_ratio BETWEEN 5 AND 20 AND h.return_on_equity_ttm > 0.12 AND h.profit_margin > 0.08 ORDER BY h.pe_ratio ASC LIMIT 30 ``` #### S2: Growth screen (revenue acceleration + earnings beats) ```sql WITH rev_growth AS ( SELECT symbol, date, total_revenue, LAG(total_revenue, 4) OVER (PARTITION BY symbol ORDER BY date) AS rev_yoy, ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn FROM shibui.income_statement_quarterly WHERE date >= CURRENT_DATE - INTERVAL '2 years' AND total_revenue IS NOT NULL AND total_revenue > 0 ), latest_rev AS ( SELECT symbol, ROUND((total_revenue - rev_yoy) / NULLIF(rev_yoy, 0) * 100, 1) AS yoy_growth_pct FROM rev_growth WHERE rn = 1 AND rev_yoy IS NOT NULL ), recent_beats AS ( SELECT symbol, COUNT(*) FILTER (WHERE surprise_percent > 0) AS beats, COUNT(*) AS quarters FROM shibui.earnings_quarterly WHERE date >= CURRENT_DATE - INTERVAL '1 year' AND eps_actual IS NOT NULL AND eps_estimate IS NOT NULL GROUP BY symbol ) SELECT g.symbol, g.name, g.gic_sector, h.market_capitalization_mln, lr.yoy_growth_pct, rb.beats || '/' || rb.quarters AS beat_rate, ROUND(h.pe_ratio, 2) AS pe FROM latest_rev lr INNER JOIN shibui.general_info g ON lr.symbol = g.symbol INNER JOIN shibui.highlights h ON g.symbol = h.symbol LEFT JOIN recent_beats rb ON g.symbol = rb.symbol WHERE g.type = 'Common Stock' AND h.market_capitalization_mln > 500 AND lr.yoy_growth_pct > 15 AND rb.beats >= 3 ORDER BY lr.yoy_growth_pct DESC LIMIT 30 ``` #### S3: Short squeeze candidates NOTE: In share_stats, only short_percent_float is populated (decimal: 0.15 = 15%). shares_short, shares_short_prior_month, short_ratio, short_percent_outstanding are ALL NULL. percent_insiders and percent_institutions are percentages (50.0 = 50%). ```sql SELECT g.symbol, g.name, g.gic_sector, h.market_capitalization_mln, ROUND(ss.short_percent_float * 100, 2) AS short_pct_float, ROUND(ss.percent_institutions, 1) AS inst_pct, ss.shares_float, ROUND(h.pe_ratio, 2) AS pe FROM shibui.general_info g INNER JOIN shibui.highlights h ON g.symbol = h.symbol INNER JOIN shibui.share_stats ss ON g.symbol = ss.symbol WHERE g.type = 'Common Stock' AND ss.short_percent_float > 0.15 -- decimal: 0.15 = 15% AND h.market_capitalization_mln BETWEEN 300 AND 10000 ORDER BY ss.short_percent_float DESC LIMIT 20 ``` #### S4: Dividend quality screen (yield + coverage + growth) ```sql WITH div_coverage AS ( SELECT c.symbol, c.date, ABS(c.dividends_paid) AS dividends, c.free_cash_flow, ROUND(c.free_cash_flow / NULLIF(ABS(c.dividends_paid), 0), 2) AS fcf_coverage, ROW_NUMBER() OVER (PARTITION BY c.symbol ORDER BY c.date DESC) AS rn FROM shibui.cash_flow_quarterly c WHERE c.date >= CURRENT_DATE - INTERVAL '6 months' AND c.dividends_paid IS NOT NULL AND c.dividends_paid != 0 ) SELECT g.symbol, g.name, g.gic_sector, h.market_capitalization_mln, ROUND(h.dividend_yield * 100, 2) AS yield_pct, h.dividend_share, dc.fcf_coverage, ROUND(h.pe_ratio, 2) AS pe FROM shibui.general_info g INNER JOIN shibui.highlights h ON g.symbol = h.symbol INNER JOIN div_coverage dc ON g.symbol = dc.symbol AND dc.rn = 1 WHERE g.type = 'Common Stock' AND g.country_iso = 'US' AND h.dividend_yield > 0.02 AND dc.fcf_coverage > 1.2 AND h.market_capitalization_mln > 1000 ORDER BY h.dividend_yield DESC LIMIT 20 ``` |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations establish safety (readOnly=true, destructive=false), while the description adds critical behavioral context: the prerequisite dependency on get_database_schema and the fact that this tool 'has no schema' (requires external schema context). It could further clarify what the tool returns (workflow instructions content).
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?
Four sentences, each high-value: purpose statement, prerequisite warning, usage triggers with examples, and composability note. No redundancy or waste. Appropriately front-loaded with critical constraints.
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 the presence of an output schema and the tool's nature as a workflow loader, the description adequately covers invocation prerequisites and use cases. It appropriately omits return value explanations (covered by output schema) but could clarify that the tool loads analytical methodology content rather than raw data.
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 and one parameter (_content), the description does not explicitly explain what the parameter represents. However, the tool has 0 required parameters and the description frames it as a loader, implying the parameter carries the workflow payload. The description compensates minimally by explaining the tool's function.
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 specific action 'Load' and resource 'quantitative screening workflow,' distinguishing it from siblings like load_comparison_workflow or load_earnings_workflow. It specifically identifies screening contexts ('find stocks that...', 'top N by...') that differentiate it from other workflow loaders.
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?
Excellent explicit guidance: states the hard prerequisite 'REQUIRES get_database_schema to be called first,' specifies exactly when to invoke ('Call BEFORE writing SQL when...'), provides specific trigger phrases, and notes composability ('Can be combined with other workflow tools').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
load_technical_workflowARead-onlyIdempotentInspect
Load technical analysis workflow with indicator interpretation guide and advanced query patterns. REQUIRES get_database_schema to be called first — this tool has no schema. Call BEFORE writing SQL when the user asks about RSI, MACD, moving averages, support/resistance, overbought/oversold, chart patterns, momentum, trend analysis, or entry/exit signals. Can be combined with other workflow tools.
| Name | Required | Description | Default |
|---|---|---|---|
| _content | No | ## Technical Analysis Workflow ### Persona You are a technical analyst specializing in momentum and trend- following strategies. You interpret indicator combinations, not single signals. You always note the limitations of backward- looking indicators and never claim predictive certainty. ### Indicator Interpretation Guide **Trend indicators** (direction of the trend): - `sma_200`: Long-term trend. Price > SMA-200 = bullish. Below = bearish. - `ema_50` / `ema_20`: Medium/short-term trend. - Golden cross: ema_50 crosses above sma_200. Death cross: below. **Momentum indicators** (strength and speed): - `rsi_14`: >70 overbought, <30 oversold, 40-60 neutral. Divergence (price up, RSI down) = weakening momentum. - `macd` / `macdsignal` / `macdhist`: MACD > signal = bullish. Histogram expanding = momentum increasing. Contracting = fading. - `stoch_k` / `stoch_d`: >80 overbought, <20 oversold. - `cci_20`: >100 overbought, <-100 oversold. - `willr_14`: >-20 overbought, <-80 oversold. **Trend strength:** - `adx_14`: >25 strong trend, <20 weak/no trend. Direction: `plus_di_14` > `minus_di_14` = uptrend. **Volatility:** - `bb_upper` / `bb_middle` / `bb_lower`: Bollinger Bands. Price near upper = overbought, near lower = oversold. Squeeze (bands narrow) = breakout imminent. - `atr_14`: Absolute volatility. Use for position sizing context. **Volume confirmation:** - `volume_ma_20`: Compare daily volume to 20-day average. Breakout on >2x average volume = stronger signal. - `obv`: Rising OBV with rising price confirms trend. Rising OBV with flat price = accumulation. - `mfi_14`: Money Flow Index. >80 overbought, <20 oversold. **Candlestick patterns** (values: 100=bullish, -100=bearish, 0=none): - Reversal: cdl_hammer (bullish), cdl_shootingstar (bearish), cdl_engulfing, cdl_morningstar (bullish), cdl_eveningstar (bearish) - Continuation: cdl_3whitesoldiers (bullish), cdl_3blackcrows (bearish) - Indecision: cdl_doji, cdl_spinningtop **Parabolic SAR** (`sar`): - SAR below price = uptrend. SAR above price = downtrend. - SAR flips = potential trend reversal. ### Signal Combination Framework Strong signals combine multiple confirming indicators: - **Strong bullish**: RSI rising from <30 + MACD crossover + price > SMA-200 + volume > 1.5x average + bullish candlestick pattern - **Strong bearish**: RSI falling from >70 + MACD bearish cross + price < SMA-200 + increasing volume + bearish pattern - **Caution**: ADX < 20 (weak trend) makes all signals less reliable - **Divergence**: Price makes new high but RSI/MACD doesn't = weakening ### Workflow 1. **Trend**: Where is price relative to SMA-200, EMA-50, EMA-20? 2. **Momentum**: RSI, MACD histogram, stochastic readings 3. **Strength**: ADX reading + directional indicators 4. **Volatility**: Bollinger Band position, ATR level 5. **Volume**: Is volume confirming the move? 6. **Patterns**: Any candlestick signals on recent dates? 7. **Synthesis**: Combine into overall signal with confidence level ### Output Format - **Trend Summary**: Bullish/Bearish/Neutral with timeframe context - **Key Signals** (inline table: indicator, value, interpretation) - **Confluences**: Which signals agree? Which disagree? - **Risk Levels**: What would invalidate the current thesis? - **Caveat**: Technical indicators are backward-looking and do not predict future price movement. ### Advanced Query Patterns #### T1: Full technical dashboard (single symbol) ```sql WITH latest AS ( SELECT sq.symbol, sq.date, sq.open, sq.high, sq.low, sq.close, sq.volume, ti.*, ROW_NUMBER() OVER (PARTITION BY sq.symbol ORDER BY sq.date DESC) AS rn FROM shibui.stock_quotes sq INNER JOIN shibui.technical_indicators ti ON sq.symbol = ti.symbol AND sq.date = ti.date WHERE sq.symbol = 'AAPL.NASDAQ' AND sq.date >= CURRENT_DATE - INTERVAL '7 days' ) SELECT date, close, volume, -- Trend ROUND(sma_200, 2) AS sma_200, ROUND(ema_50, 2) AS ema_50, ROUND(ema_20, 2) AS ema_20, CASE WHEN close > sma_200 THEN 'Above' ELSE 'Below' END AS vs_sma200, -- Momentum ROUND(rsi_14, 1) AS rsi, ROUND(macd, 4) AS macd, ROUND(macdsignal, 4) AS signal, ROUND(macdhist, 4) AS hist, ROUND(stoch_k, 1) AS stoch_k, ROUND(stoch_d, 1) AS stoch_d, -- Strength ROUND(adx_14, 1) AS adx, CASE WHEN plus_di_14 > minus_di_14 THEN 'Bullish' ELSE 'Bearish' END AS di_signal, -- Volatility ROUND(bb_upper, 2) AS bb_up, ROUND(bb_lower, 2) AS bb_low, ROUND(atr_14, 2) AS atr, -- Volume ROUND(volume / NULLIF(volume_ma_20, 0), 2) AS vol_ratio, ROUND(mfi_14, 1) AS mfi, -- SAR ROUND(sar, 2) AS sar, CASE WHEN close > sar THEN 'Uptrend' ELSE 'Downtrend' END AS sar_signal FROM latest WHERE rn = 1 LIMIT 1 ``` #### T2: RSI divergence detection (price up, RSI down over N days) ```sql WITH data AS ( SELECT sq.symbol, sq.date, sq.close, ti.rsi_14, FIRST_VALUE(sq.close) OVER (PARTITION BY sq.symbol ORDER BY sq.date ASC) AS start_price, FIRST_VALUE(ti.rsi_14) OVER (PARTITION BY sq.symbol ORDER BY sq.date ASC) AS start_rsi, ROW_NUMBER() OVER (PARTITION BY sq.symbol ORDER BY sq.date DESC) AS rn FROM shibui.stock_quotes sq INNER JOIN shibui.technical_indicators ti ON sq.symbol = ti.symbol AND sq.date = ti.date WHERE sq.date >= CURRENT_DATE - INTERVAL '30 days' AND ti.rsi_14 IS NOT NULL ) SELECT symbol, date, close, ROUND(rsi_14, 1) AS rsi, ROUND(start_price, 2) AS start_price, ROUND(start_rsi, 1) AS start_rsi, CASE WHEN close > start_price AND rsi_14 < start_rsi THEN 'Bearish divergence' WHEN close < start_price AND rsi_14 > start_rsi THEN 'Bullish divergence' ELSE 'No divergence' END AS divergence_signal FROM data WHERE rn = 1 AND symbol IN ('AAPL.NASDAQ', 'MSFT.NASDAQ', 'GOOGL.NASDAQ') LIMIT 20 ``` #### T3: Multi-symbol technical screen (oversold + trend + volume) ```sql WITH latest AS ( SELECT sq.symbol, sq.date, sq.close, sq.volume, ti.rsi_14, ti.sma_200, ti.ema_50, ti.macd, ti.macdsignal, ti.bb_lower, ti.adx_14, ti.volume_ma_20, ti.cdl_hammer, ti.cdl_engulfing, ti.cdl_morningstar, ROW_NUMBER() OVER (PARTITION BY sq.symbol ORDER BY sq.date DESC) AS rn FROM shibui.stock_quotes sq INNER JOIN shibui.technical_indicators ti ON sq.symbol = ti.symbol AND sq.date = ti.date WHERE sq.date >= CURRENT_DATE - INTERVAL '7 days' AND ti.rsi_14 IS NOT NULL ) SELECT l.symbol, g.name, l.date, l.close, ROUND(l.rsi_14, 1) AS rsi, ROUND(l.close / NULLIF(l.sma_200, 0) * 100 - 100, 1) AS pct_vs_sma200, ROUND(l.adx_14, 1) AS adx, ROUND(l.volume / NULLIF(l.volume_ma_20, 0), 2) AS vol_ratio, CASE WHEN l.cdl_hammer = 100 OR l.cdl_engulfing = 100 OR l.cdl_morningstar = 100 THEN 'Bullish pattern' ELSE 'None' END AS pattern FROM latest l INNER JOIN shibui.general_info g ON l.symbol = g.symbol WHERE l.rn = 1 AND l.rsi_14 < 30 AND l.close < l.bb_lower AND g.type = 'Common Stock' ORDER BY l.rsi_14 ASC LIMIT 20 ``` #### T4: Trend change detection (SMA crossovers, last 30 days) ```sql WITH data AS ( SELECT sq.symbol, sq.date, sq.close, ti.ema_50, ti.sma_200, LAG(ti.ema_50) OVER (PARTITION BY sq.symbol ORDER BY sq.date) AS prev_ema50, LAG(ti.sma_200) OVER (PARTITION BY sq.symbol ORDER BY sq.date) AS prev_sma200 FROM shibui.stock_quotes sq INNER JOIN shibui.technical_indicators ti ON sq.symbol = ti.symbol AND sq.date = ti.date WHERE sq.symbol = 'AAPL.NASDAQ' AND sq.date >= CURRENT_DATE - INTERVAL '60 days' AND ti.ema_50 IS NOT NULL AND ti.sma_200 IS NOT NULL ) SELECT date, ROUND(close, 2) AS close, ROUND(ema_50, 2) AS ema_50, ROUND(sma_200, 2) AS sma_200, CASE WHEN ema_50 > sma_200 AND prev_ema50 <= prev_sma200 THEN 'Golden Cross' WHEN ema_50 < sma_200 AND prev_ema50 >= prev_sma200 THEN 'Death Cross' ELSE NULL END AS crossover FROM data WHERE (ema_50 > sma_200 AND prev_ema50 <= prev_sma200) OR (ema_50 < sma_200 AND prev_ema50 >= prev_sma200) ORDER BY date DESC LIMIT 10 ``` |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnly/idempotent/destructive hints. The description adds critical context about the prerequisite (database schema) and discloses that the workflow includes specific components (indicator guide, query patterns), but does not address idempotency or caching behavior explicitly.
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?
Three highly information-dense sentences with no waste. Front-loaded with purpose, followed by requirements, then usage triggers. No redundant information.
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 the presence of an output schema, the description appropriately focuses on prerequisites and content scope rather than return values. Complete regarding workflow capabilities but lacks explicit parameter documentation.
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% (the _content parameter lacks a description field). While the description implies the workflow content (indicators, SQL patterns), it never explicitly documents the _content parameter, its optionality, or that the tool can be invoked with no arguments to receive the default payload.
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 uses a specific verb 'Load' with a clear resource 'technical analysis workflow', and explicitly lists specific indicators (RSI, MACD, moving averages) that distinguish it from sibling tools like load_fundamental_workflow or load_earnings_workflow.
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?
Provides explicit prerequisites ('REQUIRES get_database_schema to be called first'), clear timing ('Call BEFORE writing SQL'), specific trigger conditions (user asks about technical indicators), and sibling interaction guidance ('Can be combined with other workflow tools').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
stock_data_queryARead-onlyIdempotentInspect
Stock prices, earnings, revenue, P/E, dividends, margins, screener, comparisons
Run a SQL query against 64 years of US stock market data.
REQUIRES get_database_schema to be called first — this tool has no schema.
Use this after calling get_database_schema. Supports stock screening, fundamental analysis, technical analysis, company comparisons, and backtesting across 5,200+ public companies.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Read-only SQL query to execute. Requires shibui. table prefix and a LIMIT clause. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context not present in annotations: the data scope ('64 years', '5,200+ public companies') and a strict temporal dependency (must call schema first). It aligns with annotations (readOnlyHint) by implying query execution rather than mutation. It does not disclose rate limits or caching behavior, but covers the essential domain constraints.
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 front-loaded with the SQL mechanism, but suffers from minor structural issues: the opening line is a fragmented noun list rather than a sentence, and the prerequisite requirement is stated twice ('REQUIRES...' and 'Use this after...'). While the repetition emphasizes a critical dependency, it prevents the text from being perfectly concise.
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 the existence of an output schema (which handles return value documentation) and rich annotations, the description provides appropriate context: it specifies the data universe scope (5200+ companies, 64 years), mandatory prerequisites, and supported analysis types. For a single-parameter SQL interface, this is sufficiently complete to enable correct invocation.
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 100% schema coverage, the baseline is 3. The description adds significant semantic value by mapping the single 'query' parameter to specific financial use cases ('stock screening', 'fundamental analysis', 'backtesting'), helping the agent understand what SQL constructs to generate. It reinforces the schema's constraint about requiring a LIMIT clause by contextualizing it within the data analysis domain.
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 core action ('Run a SQL query') and resource ('64 years of US stock market data'). It distinguishes itself from sibling 'load_*_workflow' tools by emphasizing its raw SQL nature and explicit dependency on get_database_schema. The opening fragment listing data types (prices, earnings, etc.) is slightly unfocused, preventing a perfect score.
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 states the critical prerequisite ('REQUIRES get_database_schema to be called first') and reiterates the temporal sequence ('Use this after calling get_database_schema'). It outlines supported use cases (screening, backtesting, comparisons) but could strengthen guidance by contrasting when to use this raw SQL tool versus the higher-level 'load_*_workflow' siblings.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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