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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Save Claim

save_claim
Idempotent

Persist a single falsifiable, evidence-backed CLAIM — the atomic unit of the research graph. Use this for each discrete assertion an analysis produces (e.g. 'NVDA gross margin stays above 70% through FY2026'), then compose claims into a thesis with link_claim_to_thesis. Claims are scored independently of theses, so claim accuracy is tracked as its own track record.

Pick claim_type by HOW it's judged, not what it's about: assertion = true now, checked against data; prediction = resolves at horizon_days via verifiable_condition; judgment = qualitative, not auto-scored. Use tags for the topic (financial, valuation, macro, …). Set eval_mode: 'auto' + a verifiable_condition for deterministic grading, else 'agent'/'manual'.

Tier: all paid + free tiers (sample rejected — guest has no customerId). Verifiable claims must cite evidence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoTopical labels (controlled vocab). Multi-valued; drives filtering + learning segmentation, not scoring.
tickersYesEntities referenced (uppercased). 1 for most claims; 2+ for a comparison claim.
evidenceNoEvidence grounding the claim.
directionYesDirectional polarity of the claim.
eval_modeNoHow the outcome is resolved: auto (deterministic grade vs data via verifiable_condition), agent (an LLM judges at resolution), manual (a human marks it).agent
statementYesThe atomic, falsifiable statement. One claim, not a paragraph.
antecedentNoScenario precondition — the claim only resolves when this holds. Null = unconditional.
claim_typeYesEpistemic type — drives scoring. assertion=true now (verified vs data); prediction=future (resolves at horizon via verifiable_condition); judgment=qualitative (not auto-scored).
confidenceYesAuthor confidence in [0,1]. Used as the Brier/log-loss weight when scored.
visibilityNo'private' (default) owner-only; 'unlisted' visible at a direct URL; 'public' surfaces on the author's profile and contributes to the claim-accuracy reputation.private
horizon_daysNoResolution horizon in days (predictions). Null for assertions/judgments.
idempotency_keyNoOptional client key for at-most-once semantics from a retrying agent.
source_report_idNoOptional id of a report that contains the supporting analysis.
verifiable_conditionNoMachine-evaluable condition for eval_mode='auto'. Null otherwise.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
claimYes
capacityYes
deduplicatedYes

TDQS

A4.4/5.0
Behavior4/5

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

Annotations indicate `idempotentHint=true` and `destructiveHint=false`. The description adds behavioral context: that claims are scored independently of theses, and that verifiable claims must cite evidence. It does not detail side effects like output format, but output schema exists to cover that. The description enriches the annotations without contradiction.

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 well-structured in three paragraphs: purpose, parameter guidance, and tier info. It is front-loaded with the core purpose. Though slightly long, every sentence adds value, and it includes examples. Minor redundancy could be trimmed, but overall effective.

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 parameters, nested objects, multiple enums, and an output schema), the description covers all essential aspects: core functionality, parameter selection guidance, tier restrictions, and citation requirements. It is complete enough for an agent to select and invoke correctly without additional context.

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 description coverage is 100%, so baseline is 3. The description adds significant meaning by explaining `claim_type` semantics (judged by how, not what), `tags` for topics, and `eval_mode` choices with `verifiable_condition`. This goes beyond schema 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's purpose: to persist a single falsifiable, evidence-backed claim as the atomic unit of the research graph. It distinguishes itself from sibling tools like `link_claim_to_thesis` and `delete_claim`, and provides an example use case.

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 provides explicit guidance on when to use the tool (for each discrete assertion), how to select `claim_type` based on judgment method, and mentions `eval_mode` and `verifiable_condition` for automated grading. It also notes tier restrictions and citation requirements. However, it lacks explicit 'when not to use' and direct comparison to alternative tools like `save_freeform_report`.

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