get_inflation_indicatorsInflation pillar indicators: headline + core CPI, headline + core PCE (YoY and MoM), 5Y/10Y breakevens, wage growth. Returns classification (supportive/neutral/headwind).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| cpi | No | ||
| pce | No | ||
| as_of | No | ||
| breakevens | No | ||
| wage_growth | No | ||
| data_sources | No | ||
| classification | No | ||
| terms_glossary | No | ||
| staleness_flags | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds a small behavioral detail by stating it 'Returns classification (supportive/neutral/headwind)', but it does not discuss data freshness, units, or coverage constraints. There is no contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two crisp sentences: the first enumerates the content, the second states the returned classification. No filler, and the most important scope-setting phrase is front-loaded. It earns every word.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless read-only indicator tool with an output schema, the description covers all salient data categories and the classification output. The only minor omissions are temporal scope or interpretation of the classification, but the output schema likely covers structure. This is complete enough for selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema's description coverage is 100% by vacuity. With no parameters to document, there is nothing for the description to add beyond confirming it takes no arguments, which it implicitly does. Baseline 4 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific resource ('Inflation pillar indicators') and itemizes the included series (headline/core CPI and PCE, breakevens, wage growth), which clearly distinguishes it from sibling tools like get_growth_indicators or get_macro_regime. Even though there's no explicit verb, the title 'Get' and the 'Returns classification' clause make the operation clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies a use case: retrieve the inflation pillar dashboard. However, it does not explicitly state when to choose this over alternatives, nor does it mention exclusions or complementary tools. Unlike a definition that names siblings or conditionals, this relies on the tool's name and category.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target clearly distinct resources—regime, liquidity, conditions, prices, ETF profiles, data health—and the three history tools are explicitly separated as price, flow, and judgment. The main ambiguity is get_chapter vs run_chapter, which both return chapter framework content and differ only in usage logging, though the descriptions call this out explicitly.
The set follows a consistent snake_case verb_noun pattern: get_ for reads, list_ for enumeration, run_ for framework text, and score_ for position drift. The only wrinkle is run_chapter/get_chapter, where 'run' doesn't mean execution but rather 'return framework text and log usage,' making the verb semantics slightly less predictable.
22 tools is on the heavy side but the set is organized into recognizable clusters: macro regime, liquidity/conditions, histories, portfolio drift, ETF/prices, loops/framework, and data health. Each tool appears to earn its place, so the count is slightly high but not bloated.
The surface is comprehensive for a read-and-analyze macro/portfolio server: current reads, historical timeseries, data freshness, event calendar, ETF look-through, drift scoring, and loop navigation are all covered. Minor gaps exist—no direct portfolio/position listing tool and non-US central-bank event dates are intentionally not tracked—but these are acknowledged and workable.