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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Save Citation Override

save_citation_override
Idempotent

Persist a correction of a citation value. The correction is keyed on the canonical fact_id (a stable hash of CIK + accession + concept + period) so it applies to every report that references that same fact — including agent-regenerated reports. Re-saving the same fact_id replaces the prior correction in place (no duplicate row).

The fact_id is VERIFIED against live SEC data (scoped to ticker) before the correction is stored — a fact_id that doesn't resolve to a real fact is rejected with FACT_NOT_FOUND and nothing is persisted. You therefore must supply the ticker the fact belongs to.

Use this when the user notices an inaccuracy in an AI-generated report and wants the fix to persist. Provide notes for the rationale (≤500 chars) and source_report_id for provenance. Flat 10,000-override anti-abuse cap per account (deleting frees a slot; never a tier limit).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
notesNoOptional free-form rationale, ≤500 chars.
tickerYesTicker the fact belongs to (REQUIRED) — scopes fact_id resolution against live SEC data and denormalises the row for fast filtering (the workspace UI's 'my corrections on AAPL' view).
fact_idYesCanonical fact identifier — usually returned in a citation's `fact_ids` array by get_report or any compute tool. Stable across report regenerations. Verified against live SEC data (scoped to `ticker`) before persistence — a fabricated or unresolvable fact_id is rejected.
corrected_valueYesUser-corrected value, stringified. The frontend interprets it based on the fact's known datatype (number, string, ISO date).
source_report_idNoOptional report id the user was viewing when they applied the correction (provenance).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
createdYes
capacityYes
overrideYes

TDQS

A4.6/5.0
Behavior5/5

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

The description goes beyond annotations (idempotentHint=true) by explaining re-saving replaces prior correction, verified against live SEC data, rejection with FACT_NOT_FOUND, and anti-abuse cap. No contradictions with 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?

The description is a single dense paragraph with no wasted words. Every sentence provides essential information, from behavior to usage constraints. It is appropriately sized for the tool's 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?

Given the complexity (5 params, output schema exists), the description covers key aspects like verification, rejection, idempotency, and anti-abuse cap. It does not describe the success response format, but the output schema likely handles that. Slight gap for completeness.

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. The description adds meaning by explaining fact_id as a stable hash, ticker scopes resolution, corrected_value as stringified, notes for rationale, and source_report_id for provenance. This adds value beyond the 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 it persists a correction of a citation value, keyed on a stable fact_id. It distinguishes from siblings like delete_citation_override by focusing on saving corrections, not deleting them.

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?

The description explicitly says when to use ('when the user notices an inaccuracy... wants the fix to persist') and provides prerequisites (must supply ticker, fact_id verified against SEC data). It mentions the anti-abuse cap but doesn't explicitly state when not to use.

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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