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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

List Citation Overrides

list_citation_overrides
Read-onlyIdempotent

Author-only newest-first listing of the caller's citation corrections. Filterable by ticker (e.g. all AAPL corrections) or by a single fact_id (returns 0 or 1 row). Pair with save_citation_override and delete_citation_override. Sample tier rejected.

Agent use: call with ticker to introspect what corrections the user has previously applied on that ticker — useful for system prompts that respect prior corrections during regeneration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of citation overrides to return (1–100). Defaults to 20.
cursorNoCursor from the previous response's `next_cursor` — the updated_at of the last row on that page. Omit for first page.
tickerNoOptional ticker filter, case-insensitive. Uppercased internally.
fact_idNoOptional fact_id filter — returns at most one row.

Output Schema

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

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds that the listing is author-only and newest-first, which aligns with annotations but adds modest behavioral context. No contradictions.

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 concise and front-loaded with the key purpose. The 'Sample tier rejected' sentence is somewhat unclear but does not significantly detract. Overall, it is well-structured with minimal redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of annotations, complete schema, and output schema, the description adequately covers filtering, ordering, and usage context. It could mention pagination explicitly, but the cursor parameter is explained in the schema. Sufficient for an agent.

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 provides full descriptions for all four parameters (limit, cursor, ticker, fact_id). The tool description adds no new semantic detail beyond restating the ticker filter in the usage guidance. Baseline 3 is appropriate given high schema 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?

The description clearly states that the tool lists the caller's own citation corrections in newest-first order, with filtering by ticker or fact_id. It distinguishes from siblings by mentioning pairing with save and delete, and provides a concrete use case (introspection for system prompts).

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 explains when to use the tool (to introspect prior corrections, especially with ticker filtering) and pairs it with related tools. It does not explicitly state when not to use or compare to alternatives, but the context is clear enough for typical use.

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