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Stratalize Intelligence

get_macro_market_signal

Read-only

Use when a current macro environment snapshot is needed for a trading brief, CFO board presentation, or investment committee context. Returns Fed funds rate, Treasury yields, CPI, PCE, and employment data when FRED API is configured. Example: Fed funds 5.33%, 10Y 4.42%, CPI 3.1% — rates and inflation above long-run targets, labor market tight — LATE-CYCLE positioning signal. Source: FRED St. Louis Fed, daily update.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signal_typeNo

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds that the tool returns data only when FRED API is configured, mentions daily updates, and provides an example with interpretation. It does not explain behavior when FRED is unconfigured, but this is contextual beyond annotations.

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 three sentences with each serving a purpose: usage context, output list, and illustrative example. It is slightly dense but not wordy, and the example adds practical value. A score of 4 reflects its efficient yet informative structure.

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 simple read-only tool with one optional parameter and no output schema, the description provides a good overview including data points, source, update frequency, and an example. It is missing parameter semantics and failure-mode behavior, but overall it gives sufficient context for most use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one optional parameter, signal_type, with no description and 0% schema coverage. The tool description never mentions this parameter, leaving its meaning and allowed values entirely unexplained. Since the description fails to compensate for the low schema coverage, this is a critical gap.

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 it provides a current macro environment snapshot with specific data points (Fed funds rate, Treasury yields, CPI, PCE, employment) and gives use-case context (trading brief, CFO board presentation, investment committee). This distinguishes it from sibling tools focused on specific categories or company-level signals.

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 explicitly says 'Use when a current macro environment snapshot is needed for a trading brief, CFO board presentation, or investment committee context.' This provides clear when-to-use guidance. However, it does not mention exclusions or alternatives, so it earns a 4 rather than 5.

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

B3.4/5.0
Disambiguation2/5

Multiple tools overlap significantly: get_vendor_benchmark and get_vendor_market_rate both return pricing benchmarks with median/low/high; get_industry_spend_benchmark, get_industry_spend_profile, get_category_spend_benchmark, and get_spend_by_company_size all address spend benchmarking; get_saas_market_intelligence, get_category_ai_leaders, get_sector_ai_intelligence, and get_market_intelligence_brief all cover AI citation and market themes. These overlapping purposes make tool selection ambiguous.

Naming Consistency4/5

All tools follow the 'get_' prefix consistently, creating a predictable pattern. However, the object naming is inconsistent in ordering (e.g., get_category_ai_leaders vs get_top_vendors_by_category) and some use 'synthesis' vs 'signal' vs 'benchmark' without a clear rule. Overall, the pattern is readable and consistent.

Tool Count2/5

With 45 tools, the surface is extremely large. While the server's scope is broad (market intelligence, vendor benchmarks, regulatory data, etc.), this count overwhelms an agent and dilutes focus. Many related tools could be consolidated (e.g., vendor benchmarking into one tool with modes). A typical well-scoped server would be 3-15 tools.

Completeness3/5

The server covers numerous domains with read-only intelligence, including market trends, vendor pricing, compensation, regulatory, and patent data. However, there are gaps within those domains: no historical trend comparison, no side-by-side vendor comparison across multiple metrics beyond alternatives, and no write or action capabilities. The breadth is impressive, but the depth is uneven.

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