aifu Agent Market

get_asset_metrics

Time series [{ts, value}] for one metric of one asset, ascending.

    Args:
        symbol: Ticker symbol, e.g. RENDER. Case-insensitive.
        key: Metric key — one of: price, price_chg_30d, fund_chg_30d,
            fees_30d_usd, revenue_fy_usd, revenue_yoy_pct.
        since_ts: Optional unix timestamp lower bound.
        limit: Max points, newest kept (default 90).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`key`	Yes
`limit`	No
`symbol`	Yes
`since_ts`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 10, 2026

TDQS

A3.9/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses the output format, ascending order, and the 'newest kept' behavior for limit, which is useful. However, it does not disclose potential rate limits, data availability constraints, or what happens when no data exists for the symbol/key. The description adds some behavioral context but not comprehensive transparency.

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 compact and front-loaded with the core output shape, followed by a concise parameter list. Every sentence earns its place. The only minor issue is the Args block formatting, but it is still efficient and scannable.

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?

Given the tool's moderate complexity (4 params, 2 required, no enums in schema), the description covers the essential semantics: output shape, valid keys, parameter meanings, and ordering. The output schema exists, so return values are partially covered. Missing details like error behavior or data freshness are minor for this kind of read-only metric 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 description coverage is 0%, so the description must compensate. It does: it explains symbol with an example and case-insensitivity, enumerates all valid key values, defines since_ts as a unix timestamp lower bound, and clarifies limit as 'max points, newest kept' with a default. This adds significant meaning beyond the bare schema properties.

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 ('get'), a resource ('asset metrics'), and the exact output shape ('Time series [{ts, value}] for one metric of one asset, ascending'). It also enumerates the valid metric keys, which distinguishes it from sibling tools like get_asset or compare_assets. The scope is precise: one metric, one asset, time series.

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 description implies usage context by specifying the metric keys and the optional since_ts/limit parameters, but it does not explicitly state when to use this tool versus alternatives like compare_assets or divergence_radar. There is no when-not-to-use guidance or mention of sibling tools. The parameter descriptions give some context, but the tool-level usage guidance is only implied.

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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