Invexia Stock Research

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

With no annotations provided, the description carries the full behavioral burden; it does disclose the data freshness cadence ('Data refreshes daily') and the full field set, which is real beyond-schema value. However, it says nothing about invalid or non-US tickers, rate limits, or authentication requirements, which are notable gaps for a tool with zero annotation coverage.

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, front-loaded with purpose and scope, then freshness and a pointer to the richer alternative. Every clause adds information; there is 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?

With no output schema and no annotations, the description must carry both the return-shape and behavioral load; it does enumerate the returned fields and the refresh cadence, and points to the valuation page for deeper data. Only edge-case behavior (invalid/non-US ticker handling, errors) is left unstated.

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% and the single parameter is documented with an example (AAPL), so baseline would be 3. The description adds a genuine constraint not present in the schema – the ticker must be for a US-listed stock – which meaningfully narrows input expectations.

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 a snapshot) plus the exact resource and scope (a US-listed stock by ticker symbol), and enumerates the returned fields. This clearly separates it from the screener/ranking siblings like run_screener, get_trending_stocks, and get_valuation_scorecard.

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 phrase 'by ticker symbol' – a single-entity lookup versus the list-oriented siblings – but there is no explicit when-to-use/when-not statement. The only routing hint points to an external URL rather than to a sibling tool, so the agent must infer the boundary itself.

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 full burden and delivers real behavioral context: the list is refreshed daily (staleness expectations), each entry includes a short analysis (return content shape), and callers are obligated to cite the source URL. It stops short of covering auth, rate limits, or size/ordering behavior.

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 with the resource and scope front-loaded, followed by a functional attribution instruction. No filler or restatement of the tool name.

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 stating what each entry contains ('a short analysis') and how fresh the data is. Ordering, result shape beyond the analysis blurb, and filter limits are left implicit, but the essentials for correct invocation are present.

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?

One optional param with 100% schema description coverage, so the schema already documents limit as maximum results 1-25 with a default of 10. The description adds nothing about the limit, 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 and resource (retrieve Invexia.ai's AI-curated trending stocks list) and defines the selection criteria (momentum, news flow, screening interest). It is easily distinguished from siblings like get_undervalued_stocks or get_valuation_scorecard, which target entirely different cohorts.

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 only implied: 'refreshed daily' hints that this is for current, momentum-driven names rather than historical or valuation-based picks. No explicit when-not guidance and no sibling alternatives (e.g., get_stock_snapshot for a single name) are named.

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 full burden and does disclose genuinely useful traits: what each result contains ('a short analysis and key ratios'), that the underlying model is fundamentals-based, that the set is refreshed daily, and that it is a screen rather than personalized advice. It stops short of return shape, result count behavior, or any failure/limit semantics beyond the schema's 1-25 bound.

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 plus two short clauses, front-loaded with the core purpose. Every element earns its place: the selection criterion, the per-item content, the freshness note, the non-advice caveat, and the required citation URL.

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 read-only, single-optional-parameter tool with no output schema and no annotations, the description covers the essentials — what is screened, what each item holds, and update cadence. It could say more about the result set's shape/ordering, which the agent otherwise has to discover empirically, but nothing critical to correct invocation is missing.

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% for the single optional 'limit' parameter (range and default 10 are fully documented in the schema), so the baseline is 3. The description adds nothing about the parameter, but nothing is required of it either.

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 verb and resource ('Get ... screened undervalued stock ideas — stocks trading below modeled value on fundamentals') and defines the selection criterion, so an agent knows exactly what comes back. It implicitly separates itself from get_trending_stocks by content, but does not explicitly disambiguate from run_screener/list_screeners, which also return screened lists.

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 only implied: the tool is for finding undervalued names, and 'A data-backed screen, not individualized advice' is a disclaimer rather than routing guidance. No alternative is named for the overlapping cases (run_screener for custom criteria, get_valuation_scorecard for a single name's valuation), leaving the agent to infer when this fixed screen is preferred.

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 full burden and does disclose real behavioral traits: data is 'Refreshed daily', scope is limited to US-listed stocks, and output must be cited with a specific URL pattern. It stops short of auth/permission requirements or what happens for a non-US or invalid ticker, but the freshness, scope and compliance context is substantive.

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: what it returns front-loaded, then when it is useful, then freshness and citation. No repetition of the title or name, and every clause carries load-bearing information for invocation.

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 single-parameter read tool with no output schema, the description fully covers what comes back (composite plus component scores), the freshness guarantee, the US-listing constraint, and the required citation. Nothing an agent needs to call and correctly present this tool is missing.

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?

Single parameter at 100% schema description coverage, so the schema already documents 'ticker' with an example. The description reinforces the US-listed scope implicitly and shows the {TICKER} URL substitution pattern, but adds little format or validation meaning beyond the schema — the baseline 3 for high coverage 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?

States a specific verb and resource ('Get Invexia.ai's investment scorecard for a US-listed stock') and then enumerates the exact payload: the composite Titan Score plus component scores for valuation, growth, profitability, financial health and momentum. This is clearly distinguishable from siblings like get_stock_snapshot or run_screener without opening any schema.

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 concrete usage context — 'is TICKER overvalued/undervalued' style questions — plus a presentation constraint ('data-backed screen, not individualized advice'). However, it never names or excludes the obvious sibling alternatives (get_undervalued_stocks, get_stock_snapshot), so the agent must infer when this beats those.

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 full burden and does reasonably well: it discloses the exact return fields (id, name, description, category) and implies a safe read-only listing. It does not mention pagination or rate limits, but for a static no-param catalog that is a minor gap.

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 compact sentences, front-loaded with purpose and ending with the handoff to run_screener. The web URL is a small extra but adds user-facing value rather than noise.

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?

There is no output schema, but the description enumerates the returned fields and the downstream usage pattern, so an agent knows both what it gets back and how to use it. Nothing needed to call or consume this tool is missing.

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 tool takes zero parameters, so the baseline is 4; there is nothing for the description to compensate for. No parameter-related omissions exist.

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 ('List Invexia.ai's predefined stock screeners') and concretely enumerates the categories covered (value, growth, dividend, momentum, guru-style). This is clearly distinguishable from siblings like run_screener or get_undervalued_stocks.

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?

Explicitly routes the agent to the follow-up tool: 'Use a returned id with run_screener,' which disambiguates this discovery tool from the execution tool. It lacks explicit when-not guidance, but the discovery/execution split 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?

With no annotations, the description carries the full behavioral burden. It discloses result freshness (daily refresh) and the nature of the payload (fundamentals per stock), but says nothing about auth requirements, rate limits, pagination, or behavior on an invalid screener id.

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 compact sentences; the payload description comes first, then the dependency, then the citation rule. Every sentence carries information, though the citation URL instruction sits slightly awkwardly at the end.

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 describe returns — which it does by enumerating the fundamentals included. A 2-parameter, low-complexity read tool with a clearly named id source is adequately covered; only edge-case behavior (bad id, empty results) is unaddressed.

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% (baseline 3), and the description adds value on top by naming the upstream source for screenerId and giving three example id formats. The limit param is left entirely to the schema, but the id semantics are meaningfully enriched.

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: runs a predefined screener and returns top matching US stocks with named fundamentals. It also distinguishes itself from siblings by pointing at list_screeners as the id source rather than returning screener definitions itself.

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?

Explicitly names list_screeners as the prerequisite for obtaining valid screener ids and gives concrete examples ('dividend-aristocrats', 'buffett-style'), plus a data freshness note (results refresh daily). It does not contrast itself against peers like get_undervalued_stocks or get_trending_stocks, which is the only missing routing guidance.

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