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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Schedule Task

schedule_task

Defer a follow-up task ("re-check AAPL margin compression in 30 days") for up to 90 days. This is an AGENT-facing primitive — call it mid-conversation/mid-run when you decide something is worth re-checking later; it is NOT a human-authorable "new task" form (use the Workspace's standing-agent scheduler for recurring, human-configured monitoring instead). On wake, an inbox item ALWAYS lands for the owner ("scheduled task due: …"). Optionally pass context: {managed: true, team_id: "<standing_agent id>"} to ALSO kick off a managed agent re-run at wake time — this is LIVE: it fires a real run of that standing-agent team, grounded in the saved context. It degrades to the inbox notice alone only if this deploy can't reach the run endpoint (report the actual outcome, never assume). Persisted durably in D1 — never lost on a Worker recycle. Tier: sp500+ (sample rejected).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesHuman-readable description of the deferred work.
contextNoSaved thesis/claim/report ids and any other state needed to reconstitute a fresh prompt at wake time. Set `managed: true` + `team_id: "<standing_agent id>"` to also kick off a live managed re-run of that team at wake time (see description).
wake_in_daysYesHow many days from now this task becomes due (0 < n <= 90).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
statusYes
task_idYes
wake_atYesISO 8601 timestamp when this task becomes due.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations are minimal (no readOnly, idempotent, destructive hints). Description compensates fully: discloses persistence in D1, inbox item landing on wake, optional managed agent re-run, degradation behavior, and tier restriction. No contradictions 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is detailed but each sentence adds value. Front-loaded with core purpose. Could be slightly trimmed without losing info, but overall well-structured and efficient.

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 output schema exists, return values need not be explained. Description covers when to use, behavior on wake, optional managed re-run, durability, and tier restriction. Complete for a scheduling tool with 3 parameters and nested objects.

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

Parameters5/5

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

Schema coverage is 100% with adequate descriptions. Description adds significant context: explains the `context` parameter's managed re-run capability, clarifies `wake_in_days` max, and details degradation. Enhances understanding beyond schema.

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 defers a follow-up task for up to 90 days, distinguishes it from human-authored tasks, and specifies it's agent-facing. The verb 'defer' and resource 'follow-up task' are clear, and it distinguishes from sibling 'cancel_scheduled_task' and 'standing-agent scheduler'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'call it mid-conversation/mid-run when you decide something is worth re-checking later' and clarifies it is NOT a human-authorable new task form, directing to the standing-agent scheduler instead. Provides clear context for when to use and when not.

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