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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Submit Feedback

submit_feedback

File product feedback to the Valuein team — a bug, feature request, experience note, or data-quality issue — directly from the agent surface. Available on EVERY tier including guest/sample (no token required), so an agent can report a rough edge in-band without the human leaving the conversation. Provide a category and a message (other fields optional — see params). Authenticated callers can pass an idempotency_key so a retried submission files exactly once (the same key from the same account); guest/sample callers are never deduplicated. Returns a friendly acknowledgment you can relay to the user. Do NOT use this to query data; it is a one-way report channel.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoOptional structured error-mode reason: 'wrong_number', 'bad_citation', 'missing_data', 'wrong_company', 'formatting', 'hallucinated_fact', 'tool_error', 'coverage_gap', or 'other'.
contextNoOptional free-form context object (stored as JSON), e.g. { tool: 'get_company_fundamentals', ticker: 'AAPL', request_id: 'abc123' }. Avoid secrets.
fact_idNoOptional disputed `fact_id` (for wrong_number / bad_citation feedback).
messageYesThe feedback body (1–4000 chars). Be specific: what you expected, what happened, and any reproduction steps. May contain the user's own words — it is stored for triage and never used for arithmetic.
subjectNoOptional short title (≤140 chars) summarizing the feedback.
surfaceNoOptional product surface the feedback concerns: 'mcp', 'workspace', 'sdk', 'dashboard', or 'api'.
categoryYesWhat kind of feedback this is: 'bug' (something broke), 'feature_request' (something missing), 'experience' (UX / clarity / docs), 'data_quality' (a wrong/missing/stale figure), or 'other'.
severityNoOptional impact classification: 'low', 'medium', or 'high'.
sentimentNoOptional sentiment of this feedback: 'positive' (worked well), 'negative' (something was wrong), or 'correction' (you are supplying the right value).
target_idNoOptional id of the artifact this feedback targets (e.g. a report or thesis id).
request_idNoOptional `_meta` request id of the turn that produced the artifact, for correlation.
target_typeNoOptional kind of artifact the feedback targets: 'chat_message', 'report', 'thesis', 'claim', 'tool_call', 'schema', or 'other'.
expected_valueNoOptional caller-asserted correct value, in your own words. Stored verbatim for triage — NEVER computed or trusted as data.
idempotency_keyNoOptional client-supplied key (1–64 chars). For authenticated callers, reusing the same key files the feedback exactly once — safe to retry on a network error. Ignored for guest/sample callers (no account to scope dedup to).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
feedbackYes
acknowledgmentYes

TDQS

A4.9/5.0
Behavior5/5

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

The description adds significant context beyond annotations, such as the one-way nature, idempotency key handling for authenticated vs guest callers, and that it returns a friendly acknowledgment. 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 well-structured with the core purpose first, followed by key details. Every sentence adds value, and it is concise despite covering many aspects. No unnecessary repetition.

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 (14 params, 2 required, output schema present), the description covers all necessary aspects: purpose, usage, behavioral traits, parameter guidance, and expected return. It is fully adequate for an agent to use correctly.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds value by summarizing required fields (category, message) and optional fields, and explaining idempotency_key behavior. It provides helpful context that enhances 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 the tool is for filing product feedback (bug, feature request, etc.) and specifies that it is a one-way report channel. It distinguishes itself from sibling tools by emphasizing it is not for querying data and is available on every tier.

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 states when to use this tool (to report rough edges in-band) and when not to use it (do not use to query data). It also explains idempotency key behavior and availability across tiers, providing clear 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.

Resources