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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Compare Financial Periods

compare_periods
Read-onlyIdempotent

Compare a company's core financial metrics across two fiscal periods side-by-side. Shows absolute and percentage changes with significance classification (minor < 5%, notable 5–15%, significant > 15%). The response includes a material_changes count: this is the number of metrics whose significance ∈ {notable, significant} (i.e. absolute percentage change > 5%). Use it as a quick scalar to triage filings — anything > ~3 typically signals a material event worth deeper review. Use period format: 'FY2024' for annual, 'Q1-2024' for quarterly. Pass period_a as the EARLIER period and period_b as the LATER one — if you invert them the server auto-swaps and sets swapped: true in the response so deltas always carry the correct sign (rather than silently flipping). Point-in-time safe via as_of_date. Available on all plans.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker symbol, e.g. AAPL, MSFT, BRK.B
period_aYesEarlier fiscal period. Format: 'FY2023' for annual or 'Q1-2023' for quarterly.
period_bYesLater fiscal period. Format: 'FY2024' for annual or 'Q1-2024' for quarterly.
as_of_dateNoPoint-in-time date (YYYY-MM-DD). Only returns facts with accepted_at on or before this date — eliminates look-ahead bias for backtesting.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
tickerYes
changesYesPer-metric deltas: metric, label, period_a, period_b, delta, delta_pct, significance
swappedYesTrue when inputs were reordered so period_b is the more recent period
period_aYesEarlier period descriptor: label, fiscal_year, fiscal_period, period_end, filing_date
period_bYesLater period descriptor, same shape as period_a
as_of_dateNo
company_nameNo
total_metricsYesCount of metrics compared across the two periods
material_changesYesCount of metrics flagged as a material change

TDQS

A4.8/5.0
Behavior5/5

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

The description adds significant context beyond annotations: auto-swap on inverted periods, point-in-time safety via as_of_date, and availability on all plans. This aligns with readOnlyHint and idempotentHint, providing full 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 concise (5 sentences), front-loaded with the core purpose, and every sentence adds essential information. No fluff 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 tool's complexity (comparison metrics, significance classification, period handling) and the presence of an output schema, the description covers all key aspects: purpose, usage, behavior, parameters, and output interpretation. It is fully complete.

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

Parameters5/5

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

Although schema coverage is 100%, the description adds valuable semantics: period ordering, auto-swap behavior, and the use of material_changes for triage. This gives the agent richer understanding beyond parameter names and schema descriptions.

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 compares core financial metrics across two fiscal periods side-by-side, with absolute and percentage changes and significance classification. This distinguishes it from sibling tools like get_financial_ratios or compute_dcf, which have different purposes.

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?

Provides explicit guidance on period format, ordering, and the material_changes count for triage. It implicitly distinguishes from sibling tools by focusing on period-over-period comparison, but does not explicitly state when not to use it or list alternatives.

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

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose with detailed descriptions that clarify differences. Overlaps like get_peer_comparables vs screen_universe are well-differentiated by scope (single company vs cross-sectional). Similarly, get_insider_sentiment vs get_smart_money_flow are clearly distinguished by data sources and methodology.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., create_report, get_financial_ratios, delete_alert). No mixing of conventions or inconsistent verbs.

Tool Count2/5

With 69 tools, the count far exceeds the 25+ threshold for 'too many'. While the domain is broad, the sheer volume likely overwhelms agents and increases selection complexity.

Completeness4/5

The tool set covers a wide range of SEC filings, ratios, smart-money data, alerts, reports, and more. Minor gaps exist (e.g., no options or detailed debt data), but most analyst workflows are supported.

Resources