data_healthFreshness, provenance, and fault status for every underlying series (FRED, NY Fed, OFR, Treasury). Call this to confirm the board is current before relying on a reading.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| ok | No | False for a tool failure. | |
| faults | No | ||
| reason | No | ||
| status | No | ||
| reading | No | ||
| version | No | ||
| category | No | ||
| provenance | No | ||
| generated_at | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds useful context about the tool's scope (which series are covered) and that it reports 'fault status', which implies error detection. No contradiction with annotations and no additional side-effect disclosure needed for a read-only tool.
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 sentences with no wasted words. It front-loads the core purpose and then gives a usage directive, making every sentence earn its place.
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?
The tool is simple (0 parameters, read-only, output schema provided) and the description fully covers when and why to use it, along with the data sources involved. The output schema likely captures return details, so the description does not need to explain them.
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 input schema has zero parameters, so there is no parameter semantics to clarify. Per the baseline, a zero-parameter tool receives a 4; the description correctly implies the tool takes no inputs and runs globally, which is consistent with the schema.
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 states a specific function—reporting freshness, provenance, and fault status for underlying data series—and names the concrete sources (FRED, NY Fed, OFR, Treasury). It also gives an actionable framing ('Call this to confirm the board is current') that distinguishes it from the sibling tools, which cover market or funding contexts rather than data health.
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 gives a clear when-to-use directive: 'Call this to confirm the board is current before relying on a reading.' This is explicit context for its intended use, though it does not mention when not to use it or name alternatives; given the uniqueness of the tool among siblings, this is sufficient.
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.
The tools are largely separated by domain — funding, fx, oil, crypto, flows, historical day, and health — and the long descriptions make the relationships clear. Still, the broad context tools overlap in cover for mixed cross-market, and there are a few tool pairs with shared concepts (e.g. `funding_stress_now` vs `money_market_context`, or `money_market_context` vs `world_market_context`) that require an agent to read carefully to pick the right one.
All tools consistently use lower_snake_case and long informational noun-phrase names, so there is a clear internal style rather than personalized as a fixed verb_noun pattern. The names read predictably as domain-specific evidence tools; only the noun/verse asymmetry from trivial subjects and adjectives is enough to leave a small amount of irregularity.
Eleven tools is a reasonable size for a specialized evidence terminal: each tool adds a distinct evidence or context capability — live funding, health, history, backtest, flows, assets, and broad context. No tool feels redundant or pure padding.
The set covers the full reading workflow: current state, granular context, historical analog, proof and health checks, plus specific stress-excessive modules for oil, FX/commodities, and crypto. The terminal scope is evidence/context only and the tools clearly declare their restrictions, so there are no egregious dead-ends within its stated purpose.