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COT Visual Artifact

cot_visual_artifact
Read-only

Same payload as cot_data, but with MCP Apps chart metadata. By default it charts noncommercial net positioning; pass metric to chart another COT field.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metricNoField to plot from each COT row. Typical values: noncommercial_net, noncommercial_net_zscore, noncommercial_long, noncommercial_short, open_interest.noncommercial_net
currencyYes3-letter ISO currency code for the FX futures contract (case-insensitive). Supported: AUD, CAD, CHF, EUR, GBP, JPY, MXN, NZD, USD, XAU.
end_dateNoInclusive upper bound, YYYY-MM-DD.
start_dateNoInclusive lower bound, YYYY-MM-DD.

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false), so the description adds real value by disclosing the default charted field (noncommercial net positioning), the override mechanism via `metric`, and the output nature (MCP Apps chart metadata). These behavioral traits go beyond what the schema or annotations state.

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 compact, front-loaded sentences with zero filler. The first sentence establishes identity and default behavior; the second covers the primary customization. Every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a chart-oriented tool with 100% schema coverage and a read-only annotation profile, the essential context is present: payload equivalence with cot_data, default field, override parameter, and output type ('MCP Apps chart metadata'). The main gap is that no output schema exists and the description does not detail the chart metadata structure, but 'MCP Apps chart metadata' plus the visual-artifact naming convention carries enough meaning for correct invocation.

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 description coverage is 100%, so the baseline is 3 and the schema already documents all four parameters including metric's default and examples. The description adds marginal value by confirming the metric's role and default in plain language and by pointing to cot_data's shared payload semantics, but it does not substantially enrich parameter meaning beyond the 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 states a specific verb ('charts') and resource ('COT field'), names its data sibling cot_data, and draws the exact contrast: same payload but with MCP Apps chart metadata. An agent can immediately distinguish it from cot_data and from other asset-class visual artifacts like commodities_visual_artifact or forex_visual_artifact.

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

Usage Guidelines3/5

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

The phrase 'Same payload as cot_data, but with MCP Apps chart metadata' implies this is the chart-producing variant of the cot_data tool, giving the agent a selection heuristic. However, it never explicitly states when to choose this over alternatives (e.g., plot_visual_artifact or the raw cot_data feed), leaving usage to inference rather than explicit when/when-not 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

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions; the visual_artifact variants are explicitly duplicate payloads for chart rendering. However, several task and analysis tools (macro_briefing_task, macro_research_pack_task, indicator_intel_task) have overlapping scopes and could cause misselection despite different outputs.

Naming Consistency4/5

Tool names are consistently snake_case with systematic _task and _visual_artifact suffixes, making the pattern predictable. Minor deviations like 'ping', 'subscribe_for_mcp_access', and a few noun-only names (e.g., 'forex', 'commodities') break a strict verb_noun pattern but remain readable.

Tool Count2/5

At 48 tools, the surface is far beyond the typical well-scoped server and risks overwhelming agents. The broad macro/FX domain justifies some size, but 48 is excessive and could be consolidated (e.g., merging visual artifact pairs or grouping task tools).

Completeness5/5

The tool set covers the full macro/FX workflow: data discovery (data_catalogue), raw queries (indicator_query, forex, commodities), visual artifacts, release calendar, news, COT, sentiment, seasonality, backtesting, scenario modeling, portfolio risk, and reference tools. No obvious dead ends or missing lifecycle operations for a read-heavy data server.