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Valuein — SEC EDGAR Fundamentals & Smart-Money Data

List Agent Runs

list_agent_runs
Read-onlyIdempotent

List the caller's own standing-agent runs, newest first — status, goal, cost, and timing for each. A run may have been kicked off by this same agent (e.g. via create_rule's run_team action, a schedule_task wake, or run_agent) OR by the customer's own Workspace UI; this tool lets any MCP client check on ANY run belonging to the authenticated customer regardless of what triggered it. Filter by an exact status match (e.g. "completed", "failed", "running"), and/or by agent_id (from save_agent/list_agents) to see only that agent's run history. Tier: sp500+ (sample rejected).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax runs to return (1-50, default 10).
statusNoFilter to an exact status match.
agent_idNoFilter to runs belonging to one agent (from save_agent/list_agents).

Output Schema

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

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds behavioral context beyond annotations: ordering (newest first), the scope (any run regardless of trigger), the fields returned, and the tier restriction (sp500+ sample rejected).

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 moderately verbose but each sentence adds value: it front-loads the core purpose (list runs, newest first, with key fields), then explains scope, filters, and tier. The sentence about possible triggers could be trimmed, but it helps disambiguate from sibling tools.

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 presence of an output schema and complete annotations, the description covers all essential aspects: purpose, scope, ordering, fields, filters, and access restrictions. There is no obvious gap for this listing tool.

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%, so the baseline is 3. The description adds semantics beyond the schema: it clarifies that status filter is an exact match, agent_id comes from save_agent/list_agents, and implicitly that limit controls the number of results. This enhances the structured parameter definitions.

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 the specific verb 'List' with the resource 'agent runs', states the scope is the caller's own runs, specifies ordering (newest first), and lists the included fields (status, goal, cost, timing). It clearly distinguishes from sibling get_agent_run by covering all runs regardless of what triggered them.

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 gives clear usage context: it can check any run belonging to the authenticated customer regardless of trigger. It also explains filtering by exact status match and by agent_id, and notes agent_id comes from save_agent/list_agents. It does not explicitly name an alternative when to use another tool, but the scope is well-defined.

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.