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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Generate LBO Workbook (xlsx)

generate_lbo_xlsx

Render an LBO result into a professional Excel workbook (Summary + year-by-year Projection table + Inputs sheet). Returns a 15-minute presigned R2 download URL.

SERVER-TRUST: the deal is re-derived in-Worker from the supplied lbo_result.inputs_echo (the math is pure + deterministic) and the workbook renders Valuein's recomputed figures — never the caller's claimed values. If the claimed figures disagree, the workbook is still produced but stamped with a visible correction banner and the response verification.status is 'corrected'.

Pair with compute_lbo for a typical flow: agent calls compute_lbo({ticker, ...}), then passes the structured result straight to generate_lbo_xlsx({ticker, lbo_result, ...}) to materialise a shareable file.

Tier: pro+.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker symbol of the LBO target, e.g. AAPL.
lbo_resultYesStructured LBO result — typically the `result` field returned by `compute_lbo`.
company_nameNoOptional — surfaces on the cover row. Falls back to ticker only.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
_metaYesProvenance envelope — data lineage for every MCP response
r2_keyYes
filenameYes
expires_atYes
size_bytesYes
content_typeYes
verificationYesServer-trust record. status='verified' when the caller's figures matched the server re-derivation; 'corrected' when they did not (the workbook shows the SERVER figures + a banner). `mismatches` lists every field that disagreed.
expires_in_secondsYes

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses a critical behavioral trait: the workbook is re-derived in-worker from inputs_echo, with a correction banner if figures disagree. This adds significant context beyond the annotations, which only indicate non-readonly and non-destructive, but do not cover the re-derivation and verification behavior.

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 well-structured: it starts with the core purpose, then details the server-trust behavior, then the typical pairing flow. Every sentence adds value, though the tier mention is less essential. It is appropriately sized for the complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains the output (download URL), the behavioral correction, and typical usage. Given the existence of an output schema, it doesn't need to detail return fields. It covers all necessary context for an agent to invoke the tool correctly.

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 parameters are well-described. The description adds context that lbo_result is typically from compute_lbo and that company_name is optional. It also hints at the verification.status field, which is not in the schema, adding extra semantic value.

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's purpose: 'Render an LBO result into a professional Excel workbook' with specific sheets (Summary, Projection, Inputs). It distinguishes itself from sibling tools like generate_comps_xlsx and generate_dcf_xlsx by focusing on LBO output.

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?

The description explicitly pairs this tool with compute_lbo, describing the typical flow: 'call compute_lbo, then pass the result to generate_lbo_xlsx'. This provides clear when-to-use guidance and references a specific sibling tool, making it easy for the agent to decide.

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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