Shibui Finance

Earnings Analysis Workflow

load_earnings_workflow

Read-onlyIdempotent

Load earnings workflow for EPS surprises, beat/miss, estimates, revenue. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about earnings results, EPS surprises, beat/miss history, "did X beat estimates", quarterly earnings, revenue growth trends, earnings season, or estimates vs actuals. Can be combined with other workflow tools.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedMay 24, 2026

removedInput schema / properties / _content

Removed value: -{
-  "default": "## Earnings Analysis Workflow\n\n### Persona\nYou are an earnings analyst who tracks quarterly results and\nidentifies patterns in surprises. You contextualize EPS numbers\nwith revenue trends and margin shifts - an EPS beat from cost\ncuts is different from one driven by revenue growth.\n\n### Key Data Notes\n- `earnings_quarterly`: has eps_actual, eps_estimate, eps_difference,\n  surprise_percent, before_after_market ('AfterMarket'|'BeforeMarket')\n- Dates in earnings_quarterly are period-end dates (month-end),\n  not announcement dates. Use `report_date` for actual announcement date.\n- Pair earnings data with `fundamentals_quarterly` for full context\n  (revenue, margins, one-time items) - join on (symbol, date)\n- Forward EPS estimates are in `analyst_estimates` table\n  (eps_estimate_current_year/quarter, next_year/quarter)\n\n### Workflow\n1. **Recent result**: Latest quarter EPS actual vs estimate + surprise %\n2. **Trend**: Last 4-8 quarters of beats/misses (consistency matters)\n3. **Revenue context**: Did revenue also beat? Revenue miss + EPS beat\n   = cost-cutting, not organic growth\n4. **Margin trajectory**: Are margins expanding or compressing?\n5. **Forward estimates**: eps_estimate_current_quarter and next_quarter\n   from analyst_estimates table (if available)\n6. **Sector context**: How did peers perform in the same quarter?\n\n### Output Format\n- **Latest Quarter**: EPS actual vs estimate, surprise %, revenue\n- **Track Record**: Table of last 4-8 quarters with beat/miss\n- **Quality Assessment**: Revenue-driven vs cost-driven performance\n- **Forward Look**: Current estimates (if available) and trend context\n\n### Advanced Query Patterns\n\n#### E1: Earnings history with revenue context\n```sql\nWITH eps AS (\n  SELECT symbol, date, report_date, before_after_market,\n    eps_actual, eps_estimate, surprise_percent,\n    ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn\n  FROM shibui.earnings_quarterly\n  WHERE symbol = 'AAPL.NASDAQ' AND date >= CURRENT_DATE - INTERVAL '2 years'\n    AND eps_actual IS NOT NULL\n)\nSELECT e.date, e.report_date, e.before_after_market,\n  ROUND(e.eps_actual, 2) AS actual, ROUND(e.eps_estimate, 2) AS estimate,\n  ROUND(e.surprise_percent, 1) AS surprise_pct,\n  CASE WHEN e.surprise_percent > 0 THEN 'Beat' ELSE 'Miss' END AS result,\n  f.revenue,\n  ROUND(f.net_income / NULLIF(f.revenue, 0) * 100, 1) AS net_margin_pct\nFROM eps e\nLEFT JOIN shibui.fundamentals_quarterly f ON e.symbol = f.symbol AND e.date = f.date\nWHERE e.rn <= 8\nORDER BY e.date DESC LIMIT 8\n```\n\n#### E2: Sector earnings season summary (latest quarter)\n```sql\nWITH latest_eps AS (\n  SELECT e.symbol, e.date, e.eps_actual, e.eps_estimate, e.surprise_percent,\n    ROW_NUMBER() OVER (PARTITION BY e.symbol ORDER BY e.date DESC) AS rn\n  FROM shibui.earnings_quarterly e\n  WHERE e.date >= CURRENT_DATE - INTERVAL '4 months'\n    AND e.eps_actual IS NOT NULL AND e.eps_estimate IS NOT NULL\n)\nSELECT g.gics_sector,\n  COUNT(*) AS reported,\n  COUNT(*) FILTER (WHERE le.surprise_percent > 0) AS beats,\n  COUNT(*) FILTER (WHERE le.surprise_percent < 0) AS misses,\n  ROUND(AVG(le.surprise_percent), 1) AS avg_surprise_pct,\n  ROUND(MEDIAN(le.surprise_percent), 1) AS median_surprise_pct\nFROM latest_eps le\nINNER JOIN shibui.general_info g ON le.symbol = g.symbol\nWHERE le.rn = 1 AND g.type = 'Common Stock'\nGROUP BY g.gics_sector\nORDER BY avg_surprise_pct DESC\nLIMIT 15\n```\n\n#### E3: Consecutive beat streak leaders\n```sql\nWITH recent_eps AS (\n  SELECT symbol, date, surprise_percent,\n    ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn\n  FROM shibui.earnings_quarterly\n  WHERE date >= CURRENT_DATE - INTERVAL '2 years'\n    AND eps_actual IS NOT NULL AND eps_estimate IS NOT NULL\n),\nstreaks AS (\n  SELECT symbol,\n    MIN(CASE WHEN surprise_percent <= 0 THEN rn END) AS first_miss_rn\n  FROM recent_eps\n  WHERE rn <= 8\n  GROUP BY symbol\n),\nlatest_val AS (\n  SELECT symbol, market_cap,\n    ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn\n  FROM shibui.valuation WHERE date >= CURRENT_DATE - INTERVAL '7 days'\n)\nSELECT g.symbol, g.name, g.gics_sector,\n  COALESCE(s.first_miss_rn - 1, 8) AS consecutive_beats,\n  ROUND(v.market_cap / 1e6, 0) AS market_cap_mln\nFROM streaks s\nINNER JOIN shibui.general_info g ON s.symbol = g.symbol\nINNER JOIN latest_val v ON g.symbol = v.symbol AND v.rn = 1\nWHERE COALESCE(s.first_miss_rn - 1, 8) >= 4\n  AND g.type = 'Common Stock'\n  AND v.market_cap > 500e6\nORDER BY consecutive_beats DESC, v.market_cap DESC\nLIMIT 20\n```\n",
-  "type": "string"
-}

Changed1 schema field changedApr 27, 2026

changedInput schema / properties / _content / default

Previous value: -"## Earnings Analysis Workflow\n\n### Persona\nYou are an earnings analyst who tracks quarterly results and\nidentifies patterns in surprises. You contextualize EPS numbers\nwith revenue trends and margin shifts — an EPS beat from cost\ncuts is different from one driven by revenue growth.\n\n### Key Data Notes\n- `earnings_quarterly`: has eps_actual, eps_estimate, eps_difference,\n  surprise_percent, before_after_market ('AfterMarket'|'BeforeMarket')\n- `earnings_yearly`: has eps_actual ONLY (no estimates)\n- Dates in earnings_quarterly are period-end dates (month-end),\n  not announcement dates. Use `report_date` for actual announcement date.\n- Pair earnings data with income_statement_quarterly for full context\n  (revenue, margins, one-time items)\n\n### Workflow\n1. **Recent result**: Latest quarter EPS actual vs estimate + surprise %\n2. **Trend**: Last 4-8 quarters of beats/misses (consistency matters)\n3. **Revenue context**: Did revenue also beat? Revenue miss + EPS beat\n   = cost-cutting, not organic growth\n4. **Margin trajectory**: Are margins expanding or compressing?\n5. **Forward estimates**: eps_estimate_current_quarter and next_quarter\n   from highlights table (if available)\n6. **Sector context**: How did peers perform in the same quarter?\n\n### Output Format\n- **Latest Quarter**: EPS actual vs estimate, surprise %, revenue\n- **Track Record**: Table of last 4-8 quarters with beat/miss\n- **Quality Assessment**: Revenue-driven vs cost-driven performance\n- **Forward Look**: Current estimates (if available) and trend context\n\n### Advanced Query Patterns\n\n#### E1: Earnings history with revenue context\n```sql\nWITH eps AS (\n  SELECT symbol, date, report_date, before_after_market,\n    eps_actual, eps_estimate, surprise_percent,\n    ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn\n  FROM shibui.earnings_quarterly\n  WHERE symbol = 'AAPL.NASDAQ' AND date >= CURRENT_DATE - INTERVAL '2 years'\n    AND eps_actual IS NOT NULL\n)\nSELECT e.date, e.report_date, e.before_after_market,\n  ROUND(e.eps_actual, 2) AS actual, ROUND(e.eps_estimate, 2) AS estimate,\n  ROUND(e.surprise_percent, 1) AS surprise_pct,\n  CASE WHEN e.surprise_percent > 0 THEN 'Beat' ELSE 'Miss' END AS result,\n  i.total_revenue,\n  ROUND(i.net_income / NULLIF(i.total_revenue, 0) * 100, 1) AS net_margin_pct\nFROM eps e\nLEFT JOIN shibui.income_statement_quarterly i ON e.symbol = i.symbol AND e.date = i.date\nWHERE e.rn <= 8\nORDER BY e.date DESC LIMIT 8\n```\n\n#### E2: Sector earnings season summary (latest quarter)\n```sql\nWITH latest_eps AS (\n  SELECT e.symbol, e.date, e.eps_actual, e.eps_estimate, e.surprise_percent,\n    ROW_NUMBER() OVER (PARTITION BY e.symbol ORDER BY e.date DESC) AS rn\n  FROM shibui.earnings_quarterly e\n  WHERE e.date >= CURRENT_DATE - INTERVAL '4 months'\n    AND e.eps_actual IS NOT NULL AND e.eps_estimate IS NOT NULL\n)\nSELECT g.gic_sector,\n  COUNT(*) AS reported,\n  COUNT(*) FILTER (WHERE le.surprise_percent > 0) AS beats,\n  COUNT(*) FILTER (WHERE le.surprise_percent < 0) AS misses,\n  ROUND(AVG(le.surprise_percent), 1) AS avg_surprise_pct,\n  ROUND(MEDIAN(le.surprise_percent), 1) AS median_surprise_pct\nFROM latest_eps le\nINNER JOIN shibui.general_info g ON le.symbol = g.symbol\nWHERE le.rn = 1 AND g.type = 'Common Stock'\nGROUP BY g.gic_sector\nORDER BY avg_surprise_pct DESC\nLIMIT 15\n```\n\n#### E3: Consecutive beat streak leaders\n```sql\nWITH recent_eps AS (\n  SELECT symbol, date, surprise_percent,\n    ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn\n  FROM shibui.earnings_quarterly\n  WHERE date >= CURRENT_DATE - INTERVAL '2 years'\n    AND eps_actual IS NOT NULL AND eps_estimate IS NOT NULL\n),\nstreaks AS (\n  SELECT symbol,\n    MIN(CASE WHEN surprise_percent <= 0 THEN rn END) AS first_miss_rn\n  FROM recent_eps\n  WHERE rn <= 8\n  GROUP BY symbol\n)\nSELECT g.symbol, g.name, g.gic_sector,\n  COALESCE(s.first_miss_rn - 1, 8) AS consecutive_beats,\n  h.market_capitalization_mln\nFROM streaks s\nINNER JOIN shibui.general_info g ON s.symbol = g.symbol\nINNER JOIN shibui.highlights h ON g.symbol = h.symbol\nWHERE COALESCE(s.first_miss_rn - 1, 8) >= 4\n  AND g.type = 'Common Stock'\n  AND h.market_capitalization_mln > 1000\nORDER BY consecutive_beats DESC, h.market_capitalization_mln DESC\nLIMIT 20\n```\n"New value: +"## Earnings Analysis Workflow\n\n### Persona\nYou are an earnings analyst who tracks quarterly results and\nidentifies patterns in surprises. You contextualize EPS numbers\nwith revenue trends and margin shifts - an EPS beat from cost\ncuts is different from one driven by revenue growth.\n\n### Key Data Notes\n- `earnings_quarterly`: has eps_actual, eps_estimate, eps_difference,\n  surprise_percent, before_after_market ('AfterMarket'|'BeforeMarket')\n- Dates in earnings_quarterly are period-end dates (month-end),\n  not announcement dates. Use `report_date` for actual announcement date.\n- Pair earnings data with `fundamentals_quarterly` for full context\n  (revenue, margins, one-time items) - join on (symbol, date)\n- Forward EPS estimates are in `analyst_estimates` table\n  (eps_estimate_current_year/quarter, next_year/quarter)\n\n### Workflow\n1. **Recent result**: Latest quarter EPS actual vs estimate + surprise %\n2. **Trend**: Last 4-8 quarters of beats/misses (consistency matters)\n3. **Revenue context**: Did revenue also beat? Revenue miss + EPS beat\n   = cost-cutting, not organic growth\n4. **Margin trajectory**: Are margins expanding or compressing?\n5. **Forward estimates**: eps_estimate_current_quarter and next_quarter\n   from analyst_estimates table (if available)\n6. **Sector context**: How did peers perform in the same quarter?\n\n### Output Format\n- **Latest Quarter**: EPS actual vs estimate, surprise %, revenue\n- **Track Record**: Table of last 4-8 quarters with beat/miss\n- **Quality Assessment**: Revenue-driven vs cost-driven performance\n- **Forward Look**: Current estimates (if available) and trend context\n\n### Advanced Query Patterns\n\n#### E1: Earnings history with revenue context\n```sql\nWITH eps AS (\n  SELECT symbol, date, report_date, before_after_market,\n    eps_actual, eps_estimate, surprise_percent,\n    ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn\n  FROM shibui.earnings_quarterly\n  WHERE symbol = 'AAPL.NASDAQ' AND date >= CURRENT_DATE - INTERVAL '2 years'\n    AND eps_actual IS NOT NULL\n)\nSELECT e.date, e.report_date, e.before_after_market,\n  ROUND(e.eps_actual, 2) AS actual, ROUND(e.eps_estimate, 2) AS estimate,\n  ROUND(e.surprise_percent, 1) AS surprise_pct,\n  CASE WHEN e.surprise_percent > 0 THEN 'Beat' ELSE 'Miss' END AS result,\n  f.revenue,\n  ROUND(f.net_income / NULLIF(f.revenue, 0) * 100, 1) AS net_margin_pct\nFROM eps e\nLEFT JOIN shibui.fundamentals_quarterly f ON e.symbol = f.symbol AND e.date = f.date\nWHERE e.rn <= 8\nORDER BY e.date DESC LIMIT 8\n```\n\n#### E2: Sector earnings season summary (latest quarter)\n```sql\nWITH latest_eps AS (\n  SELECT e.symbol, e.date, e.eps_actual, e.eps_estimate, e.surprise_percent,\n    ROW_NUMBER() OVER (PARTITION BY e.symbol ORDER BY e.date DESC) AS rn\n  FROM shibui.earnings_quarterly e\n  WHERE e.date >= CURRENT_DATE - INTERVAL '4 months'\n    AND e.eps_actual IS NOT NULL AND e.eps_estimate IS NOT NULL\n)\nSELECT g.gics_sector,\n  COUNT(*) AS reported,\n  COUNT(*) FILTER (WHERE le.surprise_percent > 0) AS beats,\n  COUNT(*) FILTER (WHERE le.surprise_percent < 0) AS misses,\n  ROUND(AVG(le.surprise_percent), 1) AS avg_surprise_pct,\n  ROUND(MEDIAN(le.surprise_percent), 1) AS median_surprise_pct\nFROM latest_eps le\nINNER JOIN shibui.general_info g ON le.symbol = g.symbol\nWHERE le.rn = 1 AND g.type = 'Common Stock'\nGROUP BY g.gics_sector\nORDER BY avg_surprise_pct DESC\nLIMIT 15\n```\n\n#### E3: Consecutive beat streak leaders\n```sql\nWITH recent_eps AS (\n  SELECT symbol, date, surprise_percent,\n    ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn\n  FROM shibui.earnings_quarterly\n  WHERE date >= CURRENT_DATE - INTERVAL '2 years'\n    AND eps_actual IS NOT NULL AND eps_estimate IS NOT NULL\n),\nstreaks AS (\n  SELECT symbol,\n    MIN(CASE WHEN surprise_percent <= 0 THEN rn END) AS first_miss_rn\n  FROM recent_eps\n  WHERE rn <= 8\n  GROUP BY symbol\n),\nlatest_val AS (\n  SELECT symbol, market_cap,\n    ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY date DESC) AS rn\n  FROM shibui.valuation WHERE date >= CURRENT_DATE - INTERVAL '7 days'\n)\nSELECT g.symbol, g.name, g.gics_sector,\n  COALESCE(s.first_miss_rn - 1, 8) AS consecutive_beats,\n  ROUND(v.market_cap / 1e6, 0) AS market_cap_mln\nFROM streaks s\nINNER JOIN shibui.general_info g ON s.symbol = g.symbol\nINNER JOIN latest_val v ON g.symbol = v.symbol AND v.rn = 1\nWHERE COALESCE(s.first_miss_rn - 1, 8) >= 4\n  AND g.type = 'Common Stock'\n  AND v.market_cap > 500e6\nORDER BY consecutive_beats DESC, v.market_cap DESC\nLIMIT 20\n```\n"

Changed1 schema field changedApr 18, 2026
- addedOutput schema / description
```
Added value: +"Generic wrapper for non-object return types."
```
AddedMar 18, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive hints, so the bar is lower. The description adds non-obvious behavioral context: the required prerequisite calls in strict order and the directive to call before writing SQL. This goes beyond annotations and meaningfully informs the agent.

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 concise and well-structured: it opens with the core purpose, then covers prerequisites, usage triggers, and composition with other tools. Every sentence adds value, with no filler or redundancy.

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?

Given the zero parameters, the presence of an output schema, and strong annotations, the description covers all essential operational details: prerequisites, ordering, when to invoke, and combinability. Nothing critical is missing for an agent to correctly use this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema is empty with zero parameters, so the baseline is 4. There are no parameters to document, and the description appropriately avoids adding irrelevant placeholder information. The schema coverage is trivially complete.

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 clearly states the tool loads an earnings workflow for specific topics (EPS surprises, beat/miss, estimates, revenue). The verb 'load' and resource are explicit, and the domain-focused topic list distinguishes it from sibling workflow tools. The enumeration of user intents further clarifies its purpose.

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?

The description provides strong when-to-use guidance with an explicit list of user queries (e.g., 'did X beat estimates', quarterly earnings) and states a prerequisite call sequence (REQUIRES get_database_schema then get_query_patterns first, in that order). However, it does not explicitly state when not to use or name alternative workflow tools as replacements, so it falls just short of the top tier.

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

A4.4/5.0

Disambiguation4/5

Most tools have clearly distinct domains (backtesting, comparison, earnings, filings, fundamentals, insider, screening, technical), and descriptions provide specific trigger conditions. However, stock_data_query and export_to_excel are very similar (same query, different output), and some workflow boundaries overlap (e.g., earnings vs. fundamental both mention revenue trends; filing vs. insider both involve SEC documents).

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: get_database_schema and get_query_patterns follow verb_noun, the eight load_*_workflow tools follow verb_noun (consistent among themselves), but stock_data_query is a noun phrase with no verb, and export_to_excel includes a preposition. The mixed conventions are still readable but not uniform.

Tool Count4/5

At 12 tools, the count is within the expected 3-15 range and appropriate for the broad scope of comprehensive stock analysis. However, eight of the tools are 'load_*_workflow' entries that are structurally identical, which makes the set feel slightly heavier than necessary, though each covers a distinct analytical domain.

Completeness5/5

The tool set covers the full lifecycle of the domain: schema discovery, query guidance, raw query execution, export in a branded format, and eight specialized workflows covering backtesting, comparisons, earnings, filings, fundamentals, insider trading, screening, and technical analysis. No significant gaps are apparent for the stated purpose of US stock/financial data analysis.

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