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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

Dismiss Inbox Item

dismiss_inbox_item
DestructiveIdempotent

Soft-delete a single inbox item by its id (from list_signal_inbox) — not a signal id; sets dismissed_at. The row stays queryable via list_signal_inbox(include_dismissed=true) for audit. Idempotent. Tier: sp500+ (sample rejected).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inbox_idYesIdentifier of the inbox item to dismiss (soft-delete), as returned by list_signal_inbox.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
inbox_idYes
dismissedYes
unread_countYes

TDQS

A4.7/5.0
Behavior5/5

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

The description adds significant context beyond the annotations: it explains the soft-delete mechanism ('sets `dismissed_at`'), that the row remains queryable for audit ('list_signal_inbox(include_dismissed=true)'), and that it is idempotent. It also clarifies the input is not a signal id. These details disclose the behavioral effects and constraints, going well beyond the annotation hints.

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 a tight, front-loaded sentence that covers the action, input source, effect, audit behavior, idempotence, and tier. No word is wasted; every clause earns its place.

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?

For a single-parameter mutation tool with an output schema, the description is complete. It explains the purpose, the critical input distinction, the side effect, the audit trail behavior, idempotence, and access tier. No important context is missing.

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 coverage is 100% and the schema already describes inbox_id as 'Identifier of the inbox item to dismiss (soft-delete), as returned by list_signal_inbox.' The description adds further disambiguation with 'not a signal id', which reinforces but adds marginal value. This exceeds the baseline of 3 for 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 specifies the action: 'Soft-delete a single inbox item by its id', with the resource (inbox item) and the source (from list_signal_inbox). It also explicitly distinguishes from a signal id, preventing confusion with sibling tools. This is a specific verb+resource+scope.

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 clear context: it is for soft-deleting an inbox item, is idempotent, and is restricted to Tier sp500+. It mentions the audit use case via include_dismissed=true. However, it does not explicitly contrast with alternatives like mark_inbox_read or restore_deleted, so it lacks explicit when-not 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.