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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

Get Agent

get_agent
Read-onlyIdempotent

Fetch full detail for one of the caller's own standing agents by id (from save_agent/list_agents). Returns found: false (not an error) for an unknown id OR an id belonging to another customer — there is no distinguishing signal, by design, matching get_agent_run's posture. Tier: sp500+ (sample rejected).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesAgent identifier, from save_agent or list_agents.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_metaYesProvenance envelope — data lineage for every MCP response
agentNo
foundYes

TDQS

A4.5/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint=true, destructiveHint=false), the description discloses critical behavior: returns found:false for unknown ids or other customers' ids without a distinguishing signal, and mentions the access tier restriction (sp500+, sample rejected). This is valuable context not captured in 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?

Two sentences convey a complete picture: what the tool does, how ids are sourced, the not-found behavior, the security posture, and access tier. Every sentence earns its place without redundancy.

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?

The description covers all essential non-schema aspects: ownership, unknown-id handling, absence of distinguishing signal, and tier restriction. An output schema exists, so return structure need not be described. No gaps remain for a well-scoped read-only 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%, and the schema already describes agent_id as 'Agent identifier, from save_agent or list_agents.' The tool description repeats this source info but adds no additional parameter semantics, meeting the baseline 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 uses a specific verb ('Fetch') and resource ('full detail for one of the caller's own standing agents by id'), clearly distinguishing from siblings like list_agents (listing vs full detail) and get_agent_run (different agent type). It also clarifies ownership scope ('caller's own').

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?

Provides clear context on when to use: fetching a single agent's full detail by id, with source ids from save_agent/list_agents. It does not explicitly name alternatives to avoid, but the instruction to use ids from save_agent/list_agents implies the flow and differentiates from listing operations.

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.