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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

List Agents

list_agents
Read-onlyIdempotent

List the caller's own standing agents (id, name, goal, tickers, agent_type, trigger config, schedule, enabled state, last/next run). Optionally filter by agent_type ("workflow" or "autonomous"). Use get_agent for one agent's full detail, list_agent_runs for run history, or run_agent to fire one now. Tier: sp500+ (sample rejected).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax agents to return (1-50, default 20).
agent_typeNoFilter to one agent_type.

Output Schema

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

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the read-only nature is known. The description adds valuable context by restricting scope to the caller's own standing agents and noting the optional agent_type filter. It does not discuss pagination or rate limits, but with annotations present, the added scope/filter details justify a 4.

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 concise: one sentence for purpose, one for alternatives, and a brief tier note. It front-loads the core purpose and does not waste words, scoring high on efficiency.

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 simple list tool with an output schema, the description covers all necessary context: purpose, scope, optional filter, alternatives, and access tier. It is complete enough for an agent to select and invoke correctly without additional information.

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?

Input schema has 100% coverage; both limit and agent_type have detailed descriptions and agent_type has an enum. The description only restates that agent_type can filter, providing no new syntax or behavior beyond the schema. The output field list is useful but not parameter-specific, so baseline 3 is appropriate.

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 lists the caller's own standing agents with specific fields (id, name, goal, tickers, etc.). It uses the specific verb 'List' and resource 'agents', and explicitly distinguishes from siblings like get_agent, list_agent_runs, and run_agent.

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?

It provides explicit guidance on when to use this tool (listing standing agents, optionally filtered by agent_type) and directs users to alternatives: get_agent for full detail, list_agent_runs for run history, and run_agent to execute one. This is clear and actionable.

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.