xfinlab

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

With no annotations, the description carries the full burden and does well: it discloses output composition, data sources, region scope, and the intentional absence of AI narrative or directional signal. It does not cover latency, failure behavior, or side effects, but for a read-only snapshot tool this is reasonable and informative.

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 a single, information-dense passage with a clear front-loaded purpose, followed by structured detail and region enumeration. Every clause adds useful information; nothing is filler or repetition of the schema.

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?

Despite having no output schema, the description sufficiently specifies what the caller should expect: macro indicators with sources, regional headlines, per-region sentiment, a global feed, and the region key set. Combined with the fully documented schema, the tool is contextually complete for invocation decisions.

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 schema already covers 100% of parameters, so the baseline is 3. The description adds value by enumerating the valid region keys and making it clear that sentiment is computed per region, which supplements the schema without redundancy.

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 opens with a specific verb and resource — 'Get a cross-region global market snapshot' — and enumerates the exact contents: macro indicators, regional headlines, FinBERT sentiment, and a GDELT global feed. It also distinguishes itself by explicitly stating 'No AI narrative, no directional signal — structured real data only', which separates it from sentiment-only or analysis tools.

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 makes the use case clear: use this when you need a cross-region snapshot with macro and sentiment data. It implies the distinction from siblings by emphasizing structured real data and no directional signal, but it does not explicitly name alternatives or state when not to use it, so it stops short of full 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full burden. It transparently discloses the AI-structured nature of the output, the fields included, the narrative summary, and the explicit exclusions (no article text, no directional trading signal or probability estimate). It does not cover authentication or error behavior, but the schema documents the API key and the operation is a read-style feed.

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 filler. The core purpose is front-loaded, and the second sentence provides important boundary-setting exclusions that help an agent understand the tool's scope. Every clause earns 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?

For a tool with no output schema, the description does a good job of summarizing what the response contains and what it deliberately omits. It also clarifies ticker scoping and the default cross-market behavior. It could add a note on response envelope or pagination, but this is not essential for a correct first invocation.

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?

Schema description coverage is 100%, so the baseline is 3; all parameters already have descriptions in the schema. The description adds useful semantics for 'ticker' by explaining optional scoping and the cross-market fallback, but it does not add meaning for 'lang', 'limit', or 'api_key' beyond the schema. This meets the baseline without exceeding it.

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 uses a specific verb and resource: 'Get AI-structured event clusters from recent news.' It specifies the output shape—same-story headline clusters with entity/sentiment/quant-context fields and a narrative summary—and distinguishes the feed from article text or directional signal tools. This makes it clearly distinguishable from siblings like get_sentiment or get_technical_analysis.

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 provides clear context: optionally scope to one ticker, otherwise get the cross-market feed. It also clarifies what the tool is not for ('not republished article text, and never includes a directional trading signal'), which helps an agent avoid inappropriate use. It does not explicitly name sibling tools or give when-not-to-use alternatives, but the usage context is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden and does well: it states the feed is RSS-sourced and deduplicated, lists the exact fields returned, and explicitly notes that article body text is not included. It does not mention ordering, pagination, or API-key behavior, but those are either minor or covered by the schema.

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 compact, front-loads the core purpose, and includes useful qualifiers ('RSS-sourced, deduplicated', 'no article body text') without unnecessary words. Every sentence contributes information an agent needs.

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, has no required parameters, and no output schema, so the description's specification of return fields and its explicit limitation about article body text provide enough context for correct invocation. It could be slightly stronger with ordering or time-window expectations, but it is largely complete.

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?

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds only a brief mention of optional ticker/company-name filtering, which mostly restates the schema's ticker parameter rather than adding new meaning.

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 clearly identifies a specific verb ('Get'), resource ('recent market/company news headlines'), and data source (XFINLAB's aggregated real-time news feed). It does not explicitly contrast itself with siblings like get_intelligence_feed, so it stops short of full sibling differentiation.

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 when to use the tool: whenever recent market/company news headlines are needed, optionally filtered by ticker. However, it gives no explicit guidance about when not to use it or what alternative tools like get_intelligence_feed or get_sentiment are for.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description takes on the full transparency burden. It discloses that this is real model inference rather than a fabricated estimate and specifies the exact return contents (per-headline label/confidence/score plus an average), which is valuable behavioral context beyond the schema.

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?

Two sentences with no filler. The primary action and return shape are front-loaded, and the second sentence adds meaningful credibility context without bloating the description.

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 simple 3-parameter tool with no output schema, the description covers what it does, the return values, and the model's authenticity. The schema handles parameter details, so nothing essential is missing for an agent to call it correctly.

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?

Schema coverage is 100%, so the schema already fully documents ticker, limit, and api_key. The description adds no parameter-specific detail beyond 'recent headlines' and 'ticker', which matches the schema; baseline 3 is appropriate.

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 uses a specific verb ('Get') and resource ('FinBERT-based sentiment analysis of recent headlines for a ticker'), and the sentiment focus clearly differentiates it from siblings like technical analysis, market events, and the intelligence feed.

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 purpose implicitly gives the use case — sentiment over recent headlines for a stock — which is distinct from the sibling tools. It does not explicitly name alternatives or say when not to use it, so it stops short of a 5, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations exist, so the description carries the full burden. It discloses that computation uses real OHLC price data and that it's the same engine powering XFINLAB pages, adding useful provenance. However, it does not mention rate limits, error cases, side effects, or output structure, making behavioral transparency partial.

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 one dense sentence that communicates core purpose and output categories without filler. It could be more structurally readable as a list, but it is efficient, front-loaded, and every word adds value.

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 description lists what signals are returned and the data source, which is useful. However, with no output schema and no annotations, the agent is left without information about response structure, how confidence is represented, or possible error conditions, so the context is only partially complete.

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?

All five parameters are described in the schema (100% coverage), so the baseline is 3. The description itself adds no parameter-level detail beyond the schema's examples and defaults, which is acceptable given the schema's completeness.

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 uses a specific verb ('Get') and resource ('technical analysis'), enumerating concrete signal types (trend, MACD, volume, chart patterns, BOS/CHOCH, order-flow, volume-profile) and the data source (real OHLC). This makes the tool's purpose unambiguous and clearly distinct from siblings like get_sentiment or get_market_events.

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 usage for technical analysis of a single ticker, but it provides no explicit when-to-use guidance or comparison with alternatives. Sibling tools are clearly different, so an agent can infer this is for chart-based analysis, but no exclusions or routing hints are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.