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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Compute-Ready Stream

get_compute_ready_stream
Read-onlyIdempotent

Returns a short-lived (15-min) download URL for a bulk Parquet object that can be piped directly into Python/DuckDB/Polars for high-throughput computation that exceeds the MCP context window. The URL streams the object straight from Valuein storage and supports HTTP range reads, so duckdb.read_parquet(url) / pl.read_parquet(url) work without downloading the whole file first. Datasets: fact (per-entity partition — requires ticker), ratio (all computed ratios), valuation (DCF inputs), filing (SEC filing metadata), references (company universe), index_membership (historical index composition). Scoped to the caller's tier bucket; the link is signed and cannot be used to list the bucket or read other objects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerNoRequired when dataset_type is 'fact'. Resolves to the per-entity fact/{CIK}.parquet partition for that company.
dataset_typeYesDataset to access. 'fact' requires ticker (per-entity partition). All others are full-universe tables.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoSigned, time-limited (15-min) download URL for the Parquet object (Range-enabled)
_metaYesProvenance envelope — data lineage for every MCP response
scopeNoWhat the presigned URL is scoped to (method, object_key_only, etc.)
usageNoReady-to-run DuckDB / Polars snippets
bucketNo
formatNo
tickerNo
url_hashNo
expires_atNo
object_keyNo
dataset_typeYes
expires_in_secondsNo

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the description's job is to add details. It discloses the 15-minute URL lifetime, HTTP range read support, and security scoping (signed link, cannot list bucket). These add value beyond annotations, though missing details like error handling or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph front-loading the main purpose. It contains no filler, but could be slightly more structured (e.g., separate sentences for security vs dataset listing). Still efficient given the amount of detail.

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 (2 params, 6 dataset types, output schema exists), the description covers all key aspects: return value, usage patterns, constraints, dataset descriptions, and security. The presence of an output schema means return format need not be detailed here.

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% with descriptions for both parameters. The description adds context by explaining each dataset type (e.g., 'fact requires ticker', 'ratio all computed ratios'), which provides meaning beyond the schema's brief 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 returns a short-lived download URL for a bulk Parquet object, specifying the verb 'returns' and resource. It distinguishes from sibling tools by highlighting high-throughput streaming capability beyond MCP context window, and mentions specific use cases (Python/DuckDB/Polars).

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 this tool ('high-throughput computation that exceeds the MCP context window') and explains dataset scoping (fact requires ticker, others full-universe). However, it does not explicitly mention alternatives or when not to use it, which prevents a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Valuein — SEC EDGAR Fundamentals & Smart-Money Data toolsInspect
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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