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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

List Public Claims by User

list_public_claims_by_user
Read-onlyIdempotent

Return the PUBLIC claims + claim-accuracy reputation for a user identified by Stripe customer_id. Used by the /[handle] profile to render an analyst's claim-level track record — a separate signal from thesis-outcome accuracy. Only visibility='public' claims surface; private state never leaks. Accuracy is confirmed/(confirmed+refuted) over resolved claims; null when n < 5. Sample tier rejected; sp500+ only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax public claims to return. Defaults to 20.
customer_idYesTarget user's Stripe customer_id (resolved by the frontend from the handle).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
claimsYes
reputationYes

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral details beyond annotations: it specifies that only visibility='public' claims are surfaced, private state never leaks, explains how accuracy is calculated (confirmed/(confirmed+refuted) over resolved claims), and notes that null is returned when n < 5. It also mentions the sample tier restriction (sp500+ only). No contradiction 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 four sentences, no fluff, front-loaded with the core purpose. Every sentence adds value: purpose, usage context, privacy guarantee, accuracy formula, and tier restriction. It is well-structured and easy to read.

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 tool complexity (returns claims with computed reputation), annotations, and likely output schema (not shown but exists), the description covers purpose, filtering logic, privacy, accuracy formula, null condition, tier restriction, and usage context. It addresses potential concerns about data leakage and calculation edge cases. No obvious gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. The description adds context that customer_id is a Stripe ID resolved from the handle, but this is already in the schema description. For 'limit', the description doesn't add anything beyond schema defaults/max/min. Credit is baseline 3 due to high coverage.

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?

Description clearly states it returns public claims and claim-accuracy reputation for a user by Stripe customer_id, used on the /[handle] profile. It specifies the resource (public claims + reputation) and distinguishes itself from sibling tools like list_claims (which likely lists all claims) and list_claims_for_thesis (which lists claims for a specific thesis).

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 context for when to use this tool: 'Used by the /[handle] profile to render an analyst's claim-level track record.' It also differentiates this signal from thesis-outcome accuracy. However, it does not explicitly state when not to use it or mention alternative tools for other use cases.

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.