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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Get Auditable Research File

get_research_file
Read-onlyIdempotent

Fetch the Auditable Research File behind one of the caller's own agent runs — the complete evidence chain an examiner asks for: the originating prompt, every tool the agent called in order, every fact_id it cited, every human approval, and which models were used. Assembled from the immutable audit ledger written as the run executed; nothing here is reconstructed or inferred. Name the subject EITHER way, and pass exactly one: report_id (a report you wrote or found — from create_report, list_my_reports or search_reports) or run_id (from list_agent_runs). Naming a REPORT is the richer call: it resolves the run behind that report AND adds two sections a run's ledger cannot carry — human_review (each figure a HUMAN verified, corrected, rejected or sourced externally, with who and when) and sources (the SEC filing, form, period and filed date behind each cited fact_id). It also echoes the resolved run_id. A run-keyed call omits both, because a run may produce several reports and 'the report for this run' has no honest answer; empty or absent there means NOT RESOLVED, never 'no sources'. format: "pdf" returns the SAME assembled file as a branded compliance PDF instead of inline JSON — a 15-minute presigned download URL (url + filename) for the human-facing artifact (cover with the completeness verdict, evidence chain table, provenance with clickable sec.gov links). The PDF is rendered fresh on every call — never cached — because an in-flight run's ledger can gain entries, and a stale 'complete' verdict is exactly the lie this document exists to prevent. ⚠️ ALWAYS READ completeness FIRST AND REPORT IT. completeness.complete is computed from the ledger, and completeness.gaps names every hole found — an irreversible action taken with no named approver, a state-changing action that cited no fact_id, an unrecorded model, a failed step. If you present this run as evidence, present the gaps too; a chain with holes that is quoted as if whole is the one thing this artifact exists to prevent. ⚠️ found: false IS NOT A FINDING ABOUT THE WORK. It is returned (not as an error) for an unknown id, an id belonging to another customer, and a report with no run on record — deliberately indistinguishable, so no caller can probe which. It means we hold no audit trail under that id. It does NOT mean the report is unaudited, unverified, or that the id does not exist, and it must never be reported that way. Tier: sp500+ (sample rejected).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNojson (default) = the assembled file inline. pdf = a presigned download URL for the branded compliance PDF — the human-facing artifact; rendered fresh on every call, never cached.json
run_idNoRun identifier, as returned by list_agent_runs. Pass this OR report_id, not both.
report_idNoReport identifier — from create_report, list_my_reports or search_reports. Resolves the run behind that report and adds the human_review + sources sections. Pass this OR run_id, not both.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo
fileNo
_metaYesProvenance envelope — data lineage for every MCP response
foundYes
run_idNo
filenameNo
expires_atNo
size_bytesNo
content_typeNo
expires_in_secondsNo

TDQS

A4.9/5.0
Behavior5/5

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

The description adds crucial behavioral context beyond the readOnly/idempotent annotations: the file is assembled from an immutable audit ledger with nothing reconstructed, PDFs are rendered fresh to avoid stale 'complete' verdicts, and `found: false` is deliberately indistinguishable to prevent probing. It explicitly describes gaps and the deliberate non-error response, which is valuable disclosure.

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 long but information-dense; every sentence adds critical context. It is well-structured with bolded warnings, clear parameter explanations, and explicit caveats. While it could be tighter, the complexity of the tool justifies the length, and it is front-loaded with the core purpose.

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?

The description covers all key scenarios: both identifier types, output formats, gap handling, found:false semantics, and the caveat about in-flight runs. With the output schema present for return structure, the description provides complete operational guidance without needing to restate output fields.

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?

Even though schema coverage is 100%, the description enriches each parameter: it explains the semantic difference between report_id and run_id (resolving run, added sections), the default and pdf behavior of format, and the 'exactly one' constraint. This goes well beyond the schema's basic 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 fetches the Auditable Research File behind a caller's own agent run, with specific references to the evidence chain components. It distinguishes itself from siblings by explaining both identifier options (report_id vs run_id) and the unique added sections for report-keyed calls.

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 tells when to use report_id (richer, resolves run, adds human_review and sources) versus run_id (omits both sections because a run may have multiple reports), and explains the pdf format. It also provides strong guidance on reading `completeness` first and how to interpret `found: false`, preventing misuse.

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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