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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Submit Artifact Feedback

submit_artifact_feedback
Idempotent

File EXPLICIT, structured feedback about a specific artifact you (or the model) produced — a chat message, a report, a thesis, a claim, a tool call, or the schema. Use this (not submit_feedback) when you can name WHAT was judged and HOW: pass target_type + target_id + a sentiment (positive/negative/correction), and optionally a structured reason (e.g. wrong_number, bad_citation, hallucinated_fact), the request_id of the turn, the disputed fact_id WITH its ticker, and an expected_value (the value it SHOULD have been, in your words). Available on EVERY tier including guest/sample. This is a one-way intake channel — it records your assertion, it NEVER computes or validates a number, and expected_value is stored verbatim, never trusted as data. Retried submissions of the same judgement on the same request_id file exactly once. Returns the recorded feedback id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoOptional structured error-mode: 'wrong_number', 'bad_citation', 'missing_data', 'wrong_company', 'formatting', 'hallucinated_fact', 'tool_error', 'coverage_gap', or 'other'.
tickerNoOptional ticker the disputed figure belongs to (e.g. 'AAPL', 'BRK.B'). ALWAYS send this alongside `fact_id` — a `fact_id` is a one-way hash that does not contain the symbol, so without it nobody can locate the filing and your report cannot be checked against the source. Supplying it is what turns a complaint into a verifiable one.
fact_idNoOptional disputed `fact_id` (most useful for wrong_number / bad_citation).
messageNoOptional free-text detail (≤4000 chars). What you expected and what happened.
sentimentYesREQUIRED. How you judge the artifact: 'positive' (it was right/useful), 'negative' (it was wrong/unhelpful), or 'correction' (you are supplying the right value via `expected_value`).
target_idYesREQUIRED. The id of the artifact this feedback targets (a report id, thesis id, claim id, message id, tool-call id, or table/schema name).
request_idNoOptional `_meta` request id of the turn that produced the artifact. Folded into the idempotency key so a retried submission of the same judgement files once.
target_typeYesREQUIRED. The kind of artifact this feedback is about: 'chat_message', 'report', 'thesis', 'claim', 'tool_call', 'schema', or 'other'.
expected_valueNoOptional: what the value SHOULD have been, in your own words. Stored verbatim for triage — NEVER computed, restated, or trusted as data by Valuein.
idempotency_keyNoOptional explicit dedupe key (1–64 chars). Used to dedupe when no `request_id` is supplied; safe to retry on a network error.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
statusYes
feedback_idYes

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare idempotentHint=true and readOnlyHint=false, and the description adds substantial context: it is a one-way intake channel, never computes or validates, expected_value is stored verbatim and never trusted, and retried submissions file exactly once. This goes well beyond the annotations.

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 dense and front-loaded with the core purpose, but it is long and somewhat redundant with the schema's enum repetitions. Nonetheless, nearly every sentence adds meaningful guidance, so only a slight deduction for verbosity.

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?

For a 10-parameter tool with complex field dependencies, idempotency behavior, and a sibling alternative, the description covers all necessary selection, invocation, and behavioral aspects. An output schema exists for return values, so the description need not detail them.

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?

Schema coverage is 100%, so baseline is 3, but the description adds rich semantics: the interaction of target_type/target_id/sentiment, when to supply reason, the critical ticker/fact_id relationship (fact_id is a one-way hash requiring ticker), and the meaning of expected_value. This materially enhances correct invocation.

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 the specific verb phrase 'File EXPLICIT, structured feedback about a specific artifact,' enumerates artifact kinds, names the required triad target_type + target_id + sentiment, and explicitly contrasts with the sibling tool `submit_feedback`. This unambiguously distinguishes the tool from its alternatives.

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?

Provides explicit selection guidance: 'Use this (not `submit_feedback`) when you can name WHAT was judged and HOW.' It further specifies when optional fields matter (e.g., ticker with fact_id) and states availability on every tier. This is clear when-to-use and alternative-naming guidance.

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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