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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Describe Data Schema

describe_schema
Read-onlyIdempotent

Returns the Parquet schema for all tables in the Valuein SEC data warehouse. Includes table descriptions, column names, types, primary keys, and foreign-key references. Use this tool to understand the data model before querying with other tools. No data reads required — schema is embedded in the manifest. Available on all plans.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableNoFilter to a single table name (e.g. 'fact', 'entity', 'references'). Omit to return the full schema for all tables.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
tableNoSingle-table mode: the requested table name
tablesNoFull-schema mode: map of table name → { description, column_count, columns }
columnsNoSingle-table mode: map of column name → definition
projectNoFull-schema mode: source project name
descriptionNoSingle-table mode: the table's description
schema_versionYesParquet schema version from the active R2 manifest

TDQS

A4.5/5.0
Behavior4/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description explains that the schema is embedded in the manifest and available on all plans, adding value about the tool's behavior and availability. No contradictions.

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 two sentences with no wasted words. It efficiently conveys purpose, usage guidance, and a key behavioral note.

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 annotations, input schema (1 optional parameter, 100% coverage), and existence of an output schema, the description is complete. It explains what to expect and when to use it.

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?

Schema coverage is 100% and the parameter has a description. The tool description adds context by giving example table names ('fact', 'entity', 'references') and clarifying that omitting the parameter returns full schema, which enriches the schema information.

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 it returns the Parquet schema for all tables, including column details and keys. It also explicitly distinguishes the tool's purpose as a data model exploration tool before querying, which differentiates it from sibling tools that perform data analysis or actions.

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 advises using this tool to understand the data model before querying with other tools, and notes that no data reads are required. While it doesn't explicitly mention when not to use it, the context is clear and helpful.

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