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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Save Markdown as a Draft Report

save_freeform_report
Idempotent

Save free-form markdown (e.g. a chat synthesis) as a DRAFT report you can refine in the editor and export to Word/PDF. Unlike create_report (which computes a structured reverse_dcf or thesis report), this accepts raw markdown and splits it into sections. PASS citations with the fact_ids behind the figures you wrote — without them every number in the report reads as unsourced and the report can never be signed off. Tier: sample rejected (reports are per-author state). Idempotency-key → stable report id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesReport title.
tickerNoOptional ticker for context/catalog. Case-insensitive.
abstractNoOptional 1–2 sentence summary.
markdownYesFree-form markdown body (≤100k chars). Headings become sections.
citationsNoLineage you already hold for the figures in `markdown` — pass it rather than dropping it. Each claim should quote the figure exactly as the prose writes it, so figure review can link the two. Persisted as-is and NOT verified here; sign-off re-resolves every fact_id before certifying.
idempotency_keyNoOptional key for at-most-once semantics. Same key from the same user always yields the same report id.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
reportYes
statusYes
versionYes
report_idYes

TDQS

A5/5.0
Behavior5/5

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

Beyond the annotations (readOnly=false, destructive=false, idempotent=true), the description explains that markdown is split into sections, reports are per-author state, citations are persisted but NOT verified here, and idempotency-key yields a stable report id. These are genuine behavioral disclosures not available in structured metadata.

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 information-dense yet still readable. It front-loads purpose, uses a bolded directive for the critical citations requirement, and every sentence earns its place with no redundancy or filler.

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 (6 parameters, citation objects, idempotency semantics, and an output schema), the description covers the core workflow, the critical citation requirement, the draft/per-author nature, and idempotency behavior. It parcels out field-level details to the schema, which is complete, so nothing essential is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the baseline is 3, but the description adds meaningful semantics: 'Headings become sections' for markdown, the warning that omitting citations makes numbers unsourced, and that idempotency-key produces a stable report id per user. This clearly elevates above baseline.

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 opens with a specific verb and resource: 'Save free-form markdown ... as a DRAFT report'. It explicitly contrasts with the sibling `create_report`, which computes a structured report, making the tool's niche unmistakable.

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?

It clearly states when to use this tool vs `create_report` ('Unlike create_report... this accepts raw markdown') and gives an actionable directive to pass `citations` with fact_ids, warning about sign-off failure if omitted. The tier/state note adds context about per-author drafts.

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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