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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

List Figure Reviews

list_figure_reviews
Read-onlyIdempotent

List every figure review recorded for one report, plus a state-count summary — the coverage view for 'which figures in this report still need a human?' A report with no reviews yet returns an empty list and an all-zero summary; that is a legitimate answer, not an error. Owner-scoped — only returns your own review marks. Tier: sp500+ (sample rejected).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
report_idYesIdentifier of the report to list figure reviews for.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
reviewsYes
summaryYes
report_idYes

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description adds key behavioral context: empty result semantics ('returns an empty list and an all-zero summary; that is a legitimate answer, not an error'), data scoping ('only returns your own review marks'), and access tier. This genuinely helps the agent interpret tool behavior.

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?

Four sentences, each adding distinct value: purpose, empty-result handling, owner scope, and tier. No redundant or filler content; front-loaded with the core action.

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?

For a simple one-parameter read-only list tool with an output schema available, the description covers behavior, edge cases, scope, and access restrictions. Nothing important is missing; it is complete for its complexity.

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?

Schema coverage is 100% for the single report_id parameter, and its schema description is adequate. The tool description does not add extra parameter-level meaning, but none is needed; baseline 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 clearly states the action ('List every figure review recorded for one report') with a specific resource and scope, and adds the state-count summary and coverage-view purpose. This distinguishes it from siblings like save_figure_review and other list_* 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?

The description implies when to use it ('the coverage view for which figures in this report still need a human?') and includes scope constraints ('Owner-scoped', 'Tier: sp500+'). However, it does not explicitly contrast with alternative tools or state when-not-to-use, so it's clear but not fully explicit.

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.