xfinlab

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

Beyond the annotations (which already declare readOnly/idempotent/non-destructive/openWorld), the description discloses rich behavior: restatements are never leaked into earlier dates, missing fields go into unavailable_fields rather than being zero-filled, and source/quality/limitations are returned. It also states the data universe (US SEC filers, annual only) and that it is not investment advice.

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?

Dense but well structured: purpose, inputs, return shape, and the anti-look-ahead rationale are front-loaded, and every sentence carries information. It is on the longer side, with some redundancy between the opening clause and the later 'not investment advice' disclaimer, but nothing is 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?

With no output schema, the description carries the burden and does so: it enumerates the returned metrics (revenue, net income, diluted EPS, total assets, liabilities, operating cash flow), the temporal fields (data_time, publication_time), the unavailable_fields behavior, and the auxiliary source/quality/limitations. An agent has everything needed to call and interpret the result.

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 interpretation beyond the schema: as_of is not just a date format but the point-in-time cutoff determining which filings were public, and the symbol is constrained to US-listed companies. It leaves api_key and marketplace_license_key to the schema, which 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 gives a specific verb+resource (returns point-in-time company fundamentals) and precisely scopes it: annual SEC 10-K figures for a US-listed symbol on a given as_of date. It clearly distinguishes itself from the sibling analytics tools (market map, sentiment, technical analysis, events, feed), none of which return per-company point-in-time financials.

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?

It states the concrete use case and the condition that selects it ('for a symbol and an as_of date ... avoids look-ahead bias in backtests and research'), so an agent knows this is the correct tool when historical, as-of fundamentals are needed. It does not explicitly name alternatives or exclusions (e.g. non-US filers, quarterly data), but the 'US SEC filers, annual data only' note effectively sets boundaries.

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?

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so safety is covered. The description adds genuinely new context: the data provenance (world_bank/fred/ecb, GDELT), that sentiment is FinBERT-derived, and that output is deliberately non-analytical. It omits quota/rate-limit behavior, though the marketplace_license_key parameter hints at a daily quota.

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 paragraph that front-loads the core purpose and defers detail to the data inventory. Every clause carries information, though the parenthetical source tags make it slightly heavy for one sentence.

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?

No output schema exists, so the description must convey the return shape, and it does via an explicit inventory of the four result categories. Coverage of what the caller gets back is strong; the only missing element is guidance on how the composite relates to the sibling tools it overlaps with.

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 real value by enumerating all ten valid region keys (us, europe, japan, korea, china, hk, tw, sea, me, latam), which the schema only illustrates with 'e.g. us,hk,china'. That enumeration closes the only gap in the otherwise complete parameter docs.

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?

Specific verb+resource with full enumeration of what the snapshot contains: macro indicators with source attribution, filtered regional headlines, FinBERT sentiment, and a top-level GDELT feed. It also explicitly scopes what it is not (no AI narrative, no directional signal), so an agent can distinguish it from get_sentiment or get_intelligence_feed without opening either.

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 negative framing (no AI narrative, no directional signal, structured real data only) implies a use case, and 'Omit for all 10 regions' gives a default behavior. However, it never states when to pick this composite snapshot over the sibling tools get_sentiment, get_intelligence_feed, or get_market_events.

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?

Annotations already declare readOnly, idempotent, openWorld, non-destructive, so the safety profile is covered. The description adds real behavioral context beyond that: it is structured fact extraction rather than republished article text, and it never emits a directional trading signal or probability estimate — a meaningful expectation boundary for an agent.

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 tight sentences, front-loaded with what is returned, then scope behavior, then content boundaries. No filler or repetition of schema defaults, and each sentence carries distinct information.

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?

With no output schema, the description compensates by listing the returned fields (entity/sentiment/quant-context, narrative summary) and the cluster structure, and it defines scope behavior and content limits. Combined with a fully documented schema, an agent has everything needed 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 baseline is 3. The description adds semantic meaning by explaining the omissible ticker's runtime consequence ('otherwise returns the latest cross-market feed'), clarifying the default output scope rather than merely restating the parameter.

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 and resource ('Get AI-structured event clusters from recent news') and enumerates the payload (headline clusters, entity/sentiment/quant-context fields, AI-written narrative summary). It is clearly distinguishable in substance from market-map or fundamentals siblings, but it never names or contrasts a sibling tool explicitly, so it stops short of a 5.

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?

Gives one conditional usage rule — pass a ticker to scope, omit it for the latest cross-market feed — which is genuinely useful. However, there is no guidance on when to prefer this over get_market_events or get_sentiment, and no exclusions or prerequisites beyond the optional scope.

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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds real value beyond that: the feed is RSS-sourced and deduplicated, and critically it returns 'no article body text', which sets expectations about result depth. It still says nothing about rate limits, quota behavior, or what 'recent' means.

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 tight sentences: purpose/source first, optional filter second, return shape third. Every sentence carries information and the most decision-relevant content is 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?

With no output schema, the description compensates by listing the returned fields (title, source, kind, published_at, url) and explicitly excluding body text. The main remaining gap is that 'recent' is undefined and there is no mention of pagination or date-range behavior, though limit is covered in the schema.

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 four parameters including api_key and marketplace_license_key. The description only restates the ticker filter and omits the limit/license parameters entirely, adding no syntax or format detail beyond the schema. Baseline 3 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?

States a specific verb and resource ('Get recent market/company news headlines') and characterizes the source as XFINLAB's aggregated real-time news feed, RSS-sourced and deduplicated. It is clear on its own, but does not explicitly differentiate from the overlapping sibling get_intelligence_feed, leaving the agent to infer the distinction.

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 mentions the optional ticker filter, which implies a filtered-lookup use case, but gives no explicit when-to-use, when-not-to-use, or routing guidance versus get_intelligence_feed or get_sentiment. Usage is inferable but not 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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is lower. The description adds the return shape (per-headline label/confidence/score plus an average) and an explicit reliability claim that the output is real model inference rather than a fabricated estimate, which is useful trust context beyond the 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?

Two tight sentences, front-loaded with what the tool does and followed by what it returns. No filler; every clause carries information.

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?

With no output schema, the description correctly carries the return-value burden by naming the per-headline fields and the aggregate score. Auth and quota details live in the schema, but rate-limit or quota behavior at call time is not described, leaving a minor gap.

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 ticker, limit, api_key, and marketplace_license_key. The description adds no parameter-level meaning beyond what the schema provides (it only implies the ticker and recent-headlines scope), so the baseline 3 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?

States a specific verb+resource: FinBERT-based sentiment analysis of recent headlines for a ticker. The scope (per-headline plus averaged sentiment) is concrete and unmistakably distinct from siblings like get_fundamentals_as_of 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?

Usage is implied by the ticker-scoped headline sentiment framing, but the description never states when to reach for this tool versus the other intelligence tools, nor any exclusions or prerequisites. No explicit when/when-not guidance is present.

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?

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false), so the bar is lower. The description adds genuine context: results are computed from real OHLC price data and come from the same engine as XFINLAB's chart/ai-analysis pages, which speaks to provenance and trust. It still says nothing about latency, rate limits, or daily quota behavior that the api_key/marketplace_license_key parameters hint at.

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 tightly packed sentence that front-loads the resource and keeps the signal list scannable. The parenthetical market-structure enumeration is long but every item carries information about the return content, so little is wasted.

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?

With no output schema, the description carries the burden of describing what comes back, and it does so by enumerating the computed signals (confluence direction/confidence, trend, MACD, volume, patterns, BOS/CHOCH/liquidity sweep). It stops short of describing the shape or organization of the response, but combined with full annotation and schema coverage it is largely sufficient.

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, period, interval, api_key, and marketplace_license_key in detail; baseline 3 applies. The description only reinforces that it operates on one ticker, adding no format or syntax meaning beyond 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?

States a specific verb (get) and a well-enumerated resource: confluence direction/confidence, trend, MACD, volume, chart patterns, and named market-structure signals for a single ticker. The enumerated output set makes it easy to separate from get_sentiment or get_fundamentals_as_of in practice, but the description never explicitly names or routes against those siblings, so it falls short of a 5.

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 only says what the tool computes; it never states when to reach for it, when not to, or how it relates to alternatives such as get_sentiment (which also takes a ticker). An agent must infer the invocation condition entirely from the tool name and output list.

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