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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

List Claims

list_claims
Read-onlyIdempotent

List the caller's saved claims, most-recent-first, with AND-composed filters and cursor pagination. Filter by ticker, claim_type (assertion/prediction/judgment), tag, or lifecycle status (open/confirmed/refuted/expired/stale/needs_review). Archived claims are excluded unless include_archived is set.

Tier: all paid + free tiers (sample rejected).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagNoFilter to claims carrying this topical tag.
limitNoPage size (max 100).
cursorNoPagination cursor from a previous page's next_cursor.
statusNoFilter by lifecycle status, or 'all'.all
tickerNoFilter to claims referencing this ticker.
claim_typeNoFilter by epistemic type.
include_archivedNoInclude soft-deleted claims.

Output Schema

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

TDQS

A4.3/5.0
Behavior4/5

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

Annotations confirm read-only, idempotent, non-destructive behavior. Description adds value by explaining archived claims exclusion, AND-composed filters, cursor pagination, and tier restrictions. 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?

Extremely concise: two sentences plus a tier note. Front-loaded with core action, no redundant information. Every sentence serves a purpose.

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 presence of an output schema and annotations, the description fully covers purpose, filtering, ordering, pagination, and access constraints. No gaps for a list tool.

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 detailed descriptions. The description adds context (AND-composed filters, cursor pagination) but mostly reiterates filter capabilities already in schema. Baseline 3 is appropriate.

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 lists the caller's saved claims with specific ordering (most-recent-first), filtering options, and pagination. It distinguishes from siblings like list_claims_for_thesis and list_public_claims_by_user by specifying 'caller's saved claims' and explicit filtering.

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?

Description tells when to use (list own claims) but does not explicitly state when not to use or suggest alternatives. However, the context from sibling tools implies differentiation, and the description is clear enough.

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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