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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

by io.github.valuein

Save Agent

save_agent

Create or update a standing agent — a saved {goal + tickers + schedule} that fires either a fixed step recipe (agent_type="workflow", free/deterministic) or an AI-directed team (agent_type="autonomous", charged — settles against the owner's BYO key first, falling back to the managed wallet only if funded). Upsert semantics: omit agent_id to CREATE a new agent; pass an existing agent_id to UPDATE it. There is no separate update_agent — this does both, matching save_watchlist/save_thesis's house style. agent_type is STRUCTURAL and immutable: always required, and on an update it is verified against the existing agent before anything is changed — passing a different agent_type than the agent already has is rejected (delete and recreate to change the type). steps (an array of {kind:"tool"|"sop", name, args, label?}) is required and non-empty when CREATING an agent_type="workflow" agent, and must be omitted for agent_type="autonomous" (use managed_model there instead, itself optional and only valid for agent_type="autonomous"). when picks the trigger: "manual" (fires only via run_agent or the Workspace UI) or "schedule" (requires a schedule object — cadence "weekly" needs day_of_week, "monthly" needs day_of_month). This tool does NOT itself fire a run — use run_agent for that. Tier: sp500+ (sample rejected).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYesWhat this agent is trying to accomplish — drives an autonomous agent's AI-directed team, and documents intent for a workflow agent's fixed steps.
nameYesHuman-readable label.
whenYesTrigger mode. "manual" fires only via run_agent/the UI; "schedule" requires the `schedule` object.
stepsNoFixed step recipe for agent_type="workflow". Required + non-empty when CREATING a workflow agent; must be omitted for agent_type="autonomous".
tickersYesUS tickers this agent operates over. Normalised to uppercase.
agent_idNoOmit to create a new agent; pass an existing agent's id (from save_agent/list_agents/get_agent) to update it.
scheduleNoRequired when when="schedule"; ignored otherwise.
agent_typeYesSTRUCTURAL and immutable. "workflow" = fixed step recipe, free/deterministic, requires `steps`. "autonomous" = AI-directed team, charged, may set `managed_model`.
managed_modelNoManaged-lane model override for agent_type="autonomous" only (e.g. when no BYO key is connected). Omit to use the account default.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
agentYes
createdYes

TDQS

A4.9/5.0
Behavior5/5

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

Annotations are minimal (all false hints), so the description carries full burden and delivers: it discloses charging behavior for autonomous agents, BYO key fallback, immutability of agent_type with rejection on mismatch, upsert semantics, and schedule cadence constraints. This goes far beyond what annotations offer.

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 long but dense and well-structured, opening with the core purpose and flowing naturally into upsert, agent_type, steps, schedule, and exclusions. Every clause carries essential information; the length is justified by the complexity of 9 parameters with nested objects and cross-field constraints. Slightly verbose but not padded.

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 tool's complexity (nested steps, schedule objects, two agent types, charging, tier gating), the description covers all critical contexts: what fires the agent, cost implications, immutability, trigger requirements, and relationship to run_agent. Output schema exists, so not detailing return values is acceptable. Nothing important is missing.

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%, but the description adds significant semantic value: it explains that agent_type is 'STRUCTURAL and immutable', that steps are required for workflow but must be omitted for autonomous, that when='schedule' requires schedule object, and that managed_model is only valid for autonomous. This transforms raw schema fields into a coherent mental model.

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 opens with a specific verb+resource: 'Create or update a standing agent.' It clearly distinguishes this from siblings by explaining upsert semantics and noting 'There is no separate update_agent — this does both, matching save_watchlist/save_thesis's house style.' This ties it to a family of save_* tools while making its unique scope explicit.

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?

The description gives explicit when-to-use and when-not-to-use guidance: 'This tool does NOT itself fire a run — use run_agent for that.' It also clarifies alternatives for deleting/recreating when changing agent_type, and specifies tier gating ('Tier: sp500+ (sample rejected)'). This is strong, actionable 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.

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