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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Generate DCF Workbook (xlsx)

generate_dcf_xlsx

Render a forward DCF result into a professional Excel workbook (Summary + 5×5 Sensitivity heatmap + Inputs sheet). Native conditional formatting — no chart images needed. Returns a 15-minute presigned R2 download URL.

SERVER-TRUST: the DCF is re-derived in-Worker from the supplied 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'. A fabricated per-share value can never appear as Valuein-authoritative.

Pair with compute_dcf for a typical analyst flow: agent calls compute_dcf({ticker, ...}), then passes the structured result straight to generate_dcf_xlsx({ticker, dcf_result, ...}) to materialise a shareable file.

Tier: pro+.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker symbol of the company the DCF workbook is built for, e.g. AAPL.
dcf_resultYesStructured DCF result — typically the `result` field returned by `compute_dcf`.
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

A4.5/5.0
Behavior5/5

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

Discloses server re-derivation, correction banner, verification status, and presigned URL. Annotations are minimal (readOnlyHint=false), so description carries full burden and does so comprehensively.

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?

Well-structured with front-loaded purpose, then details. Slightly long but every sentence adds value; no tautology.

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?

Covers workbook contents, trust mechanism, output format (presigned URL), and partner tool. Missing output schema is compensated by detailed description of return value.

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%, so baseline is 3. Description adds context for dcf_result (typically from compute_dcf) and mentions output nature, enhancing parameter understanding beyond 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?

The description clearly states the tool renders a DCF result into an Excel workbook with specific sheets (Summary, Sensitivity heatmap, Inputs). It distinguishes from sibling tools like generate_comps_xlsx and generate_lbo_xlsx by focusing on DCF.

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?

Explicitly pairs with compute_dcf for a typical flow, explaining how to pass the structured result. While it doesn't list when not to use, the context is clear and includes trust/verification details.

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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