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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

List Research Playbooks (SOPs)

list_sops
Read-onlyIdempotent

List Valuein's expert research playbooks — the step-by-step procedures a senior equity analyst follows, each encoding the exact tool sequence, parallel-wave grouping, and output structure for one task (research brief, screen and shortlist, forensic quality audit, capital-allocation review, survivorship-free backtest, smart-money brief, thesis lifecycle, and more).

CALL THIS FIRST for any multi-step financial research request, then load the matching playbook with get_sop. Following a playbook produces materially better results than improvising a tool order — the sequences encode which figures must be fetched before others and which calls can run concurrently.

First-party Valuein content. No data reads. Available on all plans.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNoCase-insensitive substring matched against each playbook's name, title, and description — e.g. 'smart money', 'thesis', 'backtest'. Omit to list all.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sopsYes
_metaYesProvenance envelope — data lineage for every MCP response
sop_countYes
content_typeYes

TDQS

A4.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds minor context: 'First-party Valuein content. No data reads. Available on all plans.' No contradictions, but little extra behavioral detail beyond annotations.

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?

Three concise paragraphs: definition with examples, usage guidelines with alternative, and a short tagline. Every sentence earns its place, no fluff.

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?

With output schema present and rich annotations, description covers resource nature, usage guidance, sibling tool link, and content overview. Complete for a listing tool.

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?

Single parameter `filter` with 100% schema coverage. Description enriches by providing concrete filter examples ('smart money', 'thesis', 'backtest'), which aids agent understanding beyond basic schema.

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?

Title and description clearly state the tool lists research playbooks (SOPs), with examples of playbook types. Explicitly distinguishes from sibling `get_sop`, making purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs 'CALL THIS FIRST for any multi-step financial research request, then load the matching playbook with `get_sop`.' Contrasts with improvising tool order, providing clear when-to-use and follow-up guidance.

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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