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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Save Figure Review

save_figure_review
Idempotent

Record (or update) the review state of ONE figure inside a report — the durable answer to 'has a human traced this number back to its filing?' Upsert keyed on (report_id, figure_key): re-reviewing a figure REPLACES its prior mark, it never appends, so this is always the figure's current state, never a history. figure_key is an opaque id you mint yourself for one figure (common shapes: fact:{fact_id} for a dataset-backed figure, raw:{hash} for free-text prose) — reuse the exact same key to update that figure's review later. state: verified (traced and correct) | corrected (wrong — supply corrected_value) | external (legitimately not from Valuein data) | rejected (unsupported, should be removed). corrected_value is REQUIRED when state='corrected' and must be omitted otherwise. Owner-scoped — your reviews never leak to or from another user. Tier: sp500+ (sample rejected).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoOptional free-text reviewer note (e.g. what was checked, or why it was rejected).
stateYesverified = traced to its filing and correct. corrected = wrong (supply corrected_value). external = legitimately not from Valuein data (analyst's own source). rejected = unsupported, should be removed from the report.
report_idYesIdentifier of the report the figure belongs to, as returned by create_report / list_my_reports / save_freeform_report.
figure_keyYesOpaque id you mint for one figure inside the report. Never parsed or validated beyond length — use the exact same key to update this figure's review later. Common shapes: 'fact:{fact_id}' for a dataset-backed figure, 'raw:{hash}' for free-text prose.
corrected_valueNoThe correct value. REQUIRED when state='corrected'; must be omitted for every other state (a corrected_value on a non-corrected review is rejected).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
_metaYesProvenance envelope — data lineage for every MCP response
reviewYes

TDQS

A4.8/5.0
Behavior5/5

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

Discloses key behaviors beyond annotations: it is an upsert that never appends, always represents current state, corrected_value is conditionally required, reviews are owner-scoped, and there is a tier restriction. Consistent with idempotentHint=true and adds valuable replacement semantics.

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 well-structured: purpose, upsert semantics, key minting guidance, state definitions, conditional rule, scope, and tier. Every sentence earns its place, and it is front-loaded with the core purpose.

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 complex conditional logic (corrected_value), owner scoping, and tier restriction, the description covers all operational aspects needed for correct invocation. An output schema exists, so the omission of return value details is acceptable.

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 significant meaning: examples for figure_key ('fact:{fact_id}', 'raw:{hash}'), full state enum explanations, and reinforces the conditional requirement for corrected_value. This goes well beyond the schema's field 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 opens with a specific verb+resource: 'Record (or update) the review state of ONE figure inside a report' and frames it as the durable answer to 'has a human traced this number back to its filing?' This clearly distinguishes it from sibling tools like list_figure_reviews and other save_* tools.

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 strong usage context: upsert keyed on (report_id, figure_key), re-reviewing replaces prior mark, and the tier restriction 'sample rejected'. It does not explicitly name alternatives like list_figure_reviews, but the purpose and behavior make it clear when to use.

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.

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