XFINLAB Intelligence

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

With no annotations provided, the description carries the behavioral transparency burden. It discloses non-obvious behavior: source attribution from world_bank/fred/ecb, GDELT as the global feed source, FinBERT sentiment inclusion, and an explicit 'structured real data only' promise with no directional signal. It does not describe response fields or error behavior, but for a read-style aggregation tool the stated constraints are meaningful.

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 dense and efficient, with no filler. It front-loads the core purpose, packs the output components and sources into one structured sentence, includes a compact region list, and ends with a crisp limitation statement. Every sentence 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?

The tool is complex and has no output schema, yet the description covers the major output areas: macro indicators with source attribution, regional headlines, sentiment, and a global feed. It also supplies the region vocabulary and rejects narrative output. Exact response fields and error behavior are unspecified, but the description is sufficient for an agent to select the tool and understand what it will return.

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 baseline is 3, but the description adds useful parameter semantics by enumerating the accepted region keys ('us, europe, japan, korea, china, hk, tw, sea, me, latam'), which the schema does not fully list. It also contextualizes sentiment and headline behavior in relation to the requested regions.

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 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 sets clear boundaries ('No AI narrative, no directional signal') that help distinguish it from sibling tools such as get_technical_analysis or get_sentiment.

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 intended use case is implied: call this when a cross-region, macro-plus-news-plus-sentiment snapshot is needed. However, it does not explicitly say when not to use it or name alternatives among the siblings, so an agent must infer the selection logic from the content description rather than being directly routed.

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 behavioral disclosure burden, and it does so well by stating this is structured fact extraction, not republished article text, and that it never includes a directional trading signal or probability estimate. It also clarifies output format expectations. It does not mention rate limits, auth requirements, or refresh cadence, but the provided disclaimers are meaningfully transparent.

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?

Three sentences, each earning its place: the first defines the resource and output, the second explains the scoping behavior, and the third sets expectations about content and limitations. It is front-loaded with the most important functional information and contains no filler.

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?

Given there is no output schema and no annotations, the description does a strong job of explaining what the agent will receive and what it will not receive. It covers the key output fields, scoping behavior, and the absence of trading signals. Minor gaps remain around pagination, date ranges, or error behavior, but these are not critical for basic correct 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 schema already documents lang, limit, ticker, and api_key. The description adds context for ticker scoping ('Optionally scoped to one ticker; otherwise returns the latest cross-market feed') but does not add meaning for the other parameters beyond what the schema provides.

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') and clearly enumerates the output contents: same-story headline clusters with entity/sentiment/quant-context fields plus an AI-written narrative summary. It also distinguishes itself from siblings by emphasizing news-derived structured extraction rather than market maps, raw events, sentiment, or 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 gives clear usage context: optionally scope to one ticker or get the latest cross-market feed. However, it does not explicitly say when to choose this tool over the sibling tools, especially get_market_events, which could plausibly overlap in the events/news space. The differentiation is implied rather than stated.

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 carries the behavioral disclosure burden, and it does so well: it discloses the RSS-sourced, deduplicated, real-time nature of the feed, and explicitly lists return fields while stating that article body text is not included. It does not cover auth/rate limits, but for a read-only news fetch this is a reasonably transparent contract.

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 the main action front-loaded, followed by the filter option and return contract. Every clause carries useful information—the parenthetical 'RSS-sourced, deduplicated' adds provenance without wordiness. No filler or redundancy.

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?

Given no output schema and no annotations, the description compensates by specifying the exact return fields and the absence of article body text. It does not mention ordering, pagination, or auth mechanics, but the api_key parameter is documented in the schema. Overall it is sufficient for an agent to invoke the tool correctly, with only minor gaps around operational details.

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 little meaning beyond what the schema provides, only restating the optional ticker/company name filtering. Since the schema handles parameter semantics fully, a 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 ('recent market/company news headlines from XFINLAB's aggregated real-time news feed'), and adds useful return-shape detail ('title, source, kind, published_at, url') and a key limitation ('no article body text'). It is clear and distinct from most siblings, though it does not explicitly differentiate itself from the potentially overlapping get_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?

There is no explicit guidance on when to use this tool versus alternatives such as get_intelligence_feed. The only usage hint ('Optionally filter by ticker/company name') concerns parameter behavior, not tool selection. The description implies a news-headline use case but provides no exclusions or routing information.

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 carries the behavioral burden. It discloses the return shape (per-headline label/confidence/score plus average) and the key fact that this is real model inference rather than a fabricated estimate, which is useful context.

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 compact sentences, no filler, with the primary purpose and return behavior front-loaded.

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?

Given there is no output schema, the description appropriately explains the return shape. It omits edge-case behavior or auth details, but the tool is simple and the essentials are covered.

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 explains ticker, limit, and api_key. The description adds no parameter-specific meaning beyond what the schema provides.

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?

States a specific verb (get), resource (FinBERT-based sentiment analysis of recent headlines), and target (ticker). Clearly distinguishes from sibling tools like get_technical_analysis 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 use when sentiment of recent ticker headlines is needed, and the sibling list contains no other sentiment tool. However, it does not state explicit when-to-use or when-not-to-use guidance or name alternatives.

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 transparency burden and does substantial work: it discloses that results are 'computed from real OHLC price data' and that it uses the same engine powering XFINLAB's chart-analysis and ai-analysis pages. It does not mention error behavior or rate limits, but the read-only 'Get' framing plus data-source disclosure is strong for a non-mutating analysis 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?

A single dense sentence with no filler; the resource and scope are front-loaded and the trailing engine-context clause adds credibility. The parenthetical list of market-structure signals is long but informative, making it slightly heavy rather than wasteful.

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?

Given no output schema and no annotations, the description compensates by enumerating the signal categories the tool returns and explicitly bounding the input to one ticker. The schema fills in parameter details. It doesn't describe output structure, but the signal enumeration gives an agent enough to understand what will come back and how to invoke it.

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 five parameters and their defaults. The description adds context that the analysis is per-ticker and OHLC-driven, but it doesn't add semantic detail for lang, period, interval, or api_key beyond 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 opens with a specific verb ('Get') and names an unusually specific resource: confluence direction/confidence, trend, MACD, volume, chart patterns, and market-structure signals. It explicitly scopes to 'one ticker', which cleanly differentiates it from the market-level siblings (get_global_market_map, get_sentiment, etc.).

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 it by emphasizing single-ticker technical analysis, distinguishing it from sibling tools that cover market maps, feeds, events, and sentiment. It does not state an explicit when-not-to-use or name an alternative, but the 'one ticker' scope provides clear usage context.

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