Zyberno stock valuation

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

Annotations already provide readOnlyHint/idempotentHint/destructiveHint, so the bar is lower. The description adds meaningful behavioral context: that the tool 'computes your assumptions, it does not endorse them,' that output is arithmetic only, and that DCF results are highly sensitive to discount and terminal rates. It communicates the tool's non-authoritative, calculation-only nature well.

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 tightly organized into three paragraphs: what it does, when to use it (with sibling differentiation), and a caveat paragraph. Every sentence earns its place — no fluff, no repetition of schema fields. It's front-loaded with the core purpose.

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 6-param computational tool with 100% schema coverage and no output schema, the description is complete. It explains the calculation model (two-stage, Gordon Growth terminal), the purpose of each input relationship (discount rate above terminal rate is already in schema), and what outputs to expect. The sensitivity caveat is appropriate context for a tool whose results are assumption-dependent.

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 all six parameters are documented in the schema. The description adds framing beyond the schema: it explains how cash flow grows (stage-one years then forever at terminal rate via Gordon Growth Model) and notes the output uses the supplied price for margin of safety. This supplements the schema rather than repeating it, though the schema already carries a heavy load.

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 the tool as running a two-stage DCF on user-supplied numbers, returning fair value per share, margin of safety, and terminal-value share. It names the specific verb ('Runs'), the resource (DCF), and the substantive outputs, which clearly distinguishes it from sibling valuation 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 is exemplary here. It explicitly states when to use ('when the user wants to value a company under their own assumptions, test sensitivity, or check arithmetic') and explicitly names the alternative (get_stock_valuation) with a clear when-not-to-use and rationale for why the two won't agree. This is the best kind of usage guidance.

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?

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses that records are stored, refresh on a rolling ~2-week cycle, may lag the newest filing, and that each response states the computation date. This gives the agent a clear picture of data freshness and limitations.

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?

Though longer than many descriptions, every sentence carries useful content: return fields, use cases, exclusions, and data freshness. It is well-structured, front-loaded with the core purpose, and avoids redundancy or 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 the tool's moderate complexity, no output schema, and rich annotation set, the description provides a complete picture: return payload, usage guidance, exclusions, data lag caveat, and response date guarantee. The agent has enough context to invoke the tool correctly and set expectations.

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 input schema already fully covers the single parameter 'symbol' with format, case-insensitivity, and coverage limitations (100% schema coverage). The description reinforces the US-listed scope but does not add significant new parameter-level 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?

The description uses a specific verb ('Returns') and names the exact resource ('Zyberno's stored valuation record for a single US-listed company') plus the key fields included. It clearly distinguishes this tool from siblings like screen_stocks and list_flagged_stocks by scope and output.

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 explicitly states when to use the tool ('Use when the user names a company or ticker and wants to know how it is valued'), provides alternative phrasings, and explicitly lists when not to use it with named alternatives (screen_stocks, list_flagged_stocks) and exclusions like price quotes, news, and non-US listings.

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=true, destructiveHint=false, and idempotentHint=true, establishing it as a safe read operation. The description adds meaningful behavioral context beyond this: the score is 'a single 0 to 100 reading' that measures present valuation against history, that it is 'not a forecast and not a market timing signal.' This disclosure of what the tool does NOT do adds genuine transparency value.

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 paragraphs: what it returns, when to use it, and what it is not. Every sentence earns its place—the methodology sentence names the four indicators compactly, and the exclusions and disclaimers are each single sentences. No wasted words.

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 zero-parameter, no-output-schema read-only tool, the description is complete: it defines the output (0-100 score), names its components, states scope/exclusions, and disclaims forecasting/timing. Combined with generous annotations (readOnly, idempotent, non-destructive), there's nothing materially missing for an agent to select and invoke this tool 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?

The tool has zero parameters, so there is no schema burden to compensate. Per the rubric, 0 params gives a baseline of 4. The description explains what the returned reading represents, which is the relevant semantic content given there's no input to parameterize.

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 states the tool returns the Zyberno Market Valuation Score, a specific 0-100 reading of US equity market expensiveness relative to history. It explicitly names the four constituent indicators (Buffett Indicator, Shiller PE, Treasury curve, Baa spread) and distinguishes it from individual company/sector/index tools like get_stock_valuation and list_flagged_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?

The description explicitly states when to use it ('questions about the market as a whole, overall valuation levels, whether US stocks broadly look expensive or cheap') and provides clear exclusions ('Do not use it for any individual company, sector, or index price level'). This maps well to the sibling set where get_stock_valuation, calculate_dcf_value, and screen_stocks handle narrower scopes.

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=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful context beyond annotations: lists are fixed and immutable ('thresholds, sector exclusions and ranking are fixed'), output is 'screen output, not recommendations,' and notably 'neither label forecasts a return.' This last warning about the value trap label not being reliable guidance is valuable.

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 well-structured paragraphs, each with a distinct purpose: output definition, usage guidance, and behavioral caveat. The description front-loads the core purpose in the first sentence and moves to usage in the second paragraph. Every sentence earns its place with substantive content; 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?

For a parameterless tool with strong annotations (readOnly, idempotent, openWorld) and no output schema, the description is remarkably complete. It explains what the lists contain, how they're defined, when to use them, when not to, and even caveats about their interpretive limits. The only minor omission might be that no output schema exists, but with zero parameters and a fixed, published output, the description covers everything an agent needs.

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 and there are no parameters to describe, so the baseline of 4 applies. The description adds the meaningful context that the tool is parameterless and that all thresholds/criteria are fixed to the published page, which clarifies why no parameters 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?

The description clearly names the specific resource ('two published monthly Brina Matrix lists'), names both list labels (Double Discount, value trap), and explains the distinct screening criteria for each. It distinguishes itself from siblings by explicitly noting it 'takes no parameters' and covers only the fixed published screen, which separates it from screen_stocks and get_stock_valuation.

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?

Excellent usage guidance: 'Use when the user asks what the framework currently flags, or asks about the Double Discount or value trap lists by name.' It also gives explicit exclusions with named alternatives: 'Do not use it for custom criteria (use screen_stocks) or for one named company (use get_stock_valuation).' This is exemplary when/when-not usage guidance.

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, openWorld, idempotent, and non-destructive hints. The description adds substantial non-obvious behavior beyond these hints: it returns at most 25 companies plus a total match count, ranks by a selectable field, and behaves breadth-reporting on too-broad/narrow queries. This is valuable transitive context not captured in 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?

The description is concise and well-structured: first sentence states the operation, second paragraph gives usage guidance with exclusions, third explains return behavior. Each sentence contributes distinct value, and the most important information 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?

For a tool with 40 parameters and no output schema, the description covers the essential context: universe size, data source, return cap, total count, and sibling differentiation. It falls slightly short of a 5 by not describing the exact format of the returned companies (e.g., ticker list vs. full entity), but this is a minor gap given the schema richness.

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 input schema covers 100% of parameters with thorough descriptions, so no compensation is needed from the tool description. The description itself does not add parameter-level semantics beyond the schema, which is acceptable at the baseline for complete schema coverage.

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 states the tool searches a database of ~10,000 US-listed companies by numeric criteria and ranks results by a chosen field. It distinguishes itself from siblings by explicitly naming alternatives (get_stock_valuation for single ticker, list_flagged_stocks for curated monthly lists), making its purpose unmistakable.

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 says exactly when to use it ('when the user wants to find, filter, rank, or count companies by their characteristics') and when not to, with named alternative tools. It also explains filter-omission behavior and the consequence of broad queries, giving clear decision rules for the agent.

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?

Despite annotations declaring readOnlyHint and idempotentHint, the description adds material behavioral context beyond them: the assumption-propagation caveat, the normal-distribution understating tail risk, and the explicit statement that output is a property of inputs rather than a prediction. This is rich transparency that complements, not contradicts, 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?

Three paragraphs in logical order: what it returns, when to use vs not use, and a behavioral caveat. No wasted words, and the most decision-relevant information (purpose and outputs) 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?

For a 9-parameter tool with 100% schema coverage and no output schema, the description fully specifies behavior, usage constraints, and limitations. The distributional assumptions and their implications are disclosed. Nothing material is left unexplained.

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 schema already documents all 9 parameters well. The description adds slight value by clarifying the return-distribution assumption (normal, using expected return and volatility), which reinforces parameter intent. However, it does not add per-parameter detail beyond the schema, so it stays near the baseline.

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 plus resource ('Runs a Monte Carlo simulation over a portfolio balance') and enumerates the outputs (percentiles, mean, median, goal-reach share), clearly distinguishing it from the sibling valuation tools. The 'odds of reaching a target' and 'volatility widens the spread' examples make scope explicit.

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?

Provides an explicit 'Use when' clause (range/probability questions) and an explicit 'Do not use' list naming alternatives (calculate_dcf_value, get_stock_valuation) and excluded domains (retirement withdrawal, tax, drawdown-sequencing). This is exemplary when-to-use/when-not guidance.

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