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

The annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds useful context about the sources (company site and LinkedIn) and the narrative format, but it does not disclose output shape, potential source bias, or limitations. No contradiction with 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 one tight, front-loaded sentence with no filler. Every clause adds meaning: what it produces, what it contains, and where the information comes from.

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 covers the tool's purpose and sources, and annotations cover read-only/open-world behavior. However, with no output schema and nested optional parameters, an agent is left to infer the return shape and how fields like sector, country, and listed should be used together. It is adequate for a simple read-only overview but not fully 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 0% for the top-level context parameter, and the description does not explain any of the nested fields such as company, companyDomain, country, listed, sector, or date. The required company field is inferable from the tool name, but the description does not compensate for the low 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 a specific verb ('Produces') and a concrete deliverable ('narrative business overview') with content details (what the company does, products/services, target market) and sources. However, it does not explicitly distinguish itself from the sibling company_profile tool, 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?

No guidance is given about when to use this tool versus alternatives like company_profile, industry_overview, or market_size. The source qualifier is informative, but it does not tell an agent when to choose this tool or when to avoid it.

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?

The annotation already marks the tool as readOnly. The description adds that the tool searches multiple external sources, which is a behavioral detail beyond the annotation. However, it does not describe the return format, potential errors, or any pagination/limitations, so it provides only partial transparency.

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 concise sentence with no redundant words. It efficiently conveys the core function and sources without any fluff, making it easy to parse and remember.

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 complexity (one nested context object with multiple fields) and lack of an output schema, the description is too minimal to be considered complete. It does not explain how to construct the input or what to expect in response, leaving significant gaps for an agent to fill.

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 description provides zero explanation of the input parameters (e.g., company, country, listed, sector, companyDomain). The schema itself has some inline descriptions for certain fields, but the description does not compensate for the low schema coverage (0%). An agent would have no guidance on how to map the purpose to the required context parameters.

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's function: retrieving registry-grade company profile facts (CIN, incorporation date, registered office) and identifies the specific data sources (MCA/Tofler/Zauba/OpenCorporates and the company's own site). It is specific and unambiguous, distinguishing it from sibling tools that focus on other aspects like financials or news.

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 does not explicitly state when to use this tool versus alternatives such as company_overview or search_company. It implies a use case for registry facts but lacks direct comparison or conditions, leaving the agent to infer the appropriate context.

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 mark the tool read-only and idempotent, so the description adds valuable behavioral context: it is deterministic, silently omits a multiple type if inputs are incomplete, never guesses missing inputs, and bridges enterprise value to equity via netDebt. This goes well beyond the annotation hints and helps the agent anticipate edge cases.

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 three dense sentences with the primary behavior front-loaded, followed by constraints and edge-case behavior. The parentheticals add length but every clause carries meaningful information; it is concise relative to the complexity it explains, though not as tight as a two-sentence definition.

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 nested objects and no output schema, the description explains inputs, outputs, methodology, and failure behavior well. It mentions `issues` for silent omissions, but because there is no output schema, the agent still lacks exact return-value structure. Overall it is complete enough for 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?

With schema description coverage only at 33%, the description compensates by explaining the core meaning of `peers` (real peer multiples, e.g. from listed_peer_comparison), `target` (the company's own financial metrics), and `netDebt` (used to bridge enterprise-value bands to equity). It does not detail every parameter such as `companyName` or `sharesOutstanding`, but the most important semantic distinctions are covered.

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 ('Applies'), a precise resource (peer trading multiples to target financial metrics), and the exact outputs (low/median/high valuation bands plus a blended equity range). It also differentiates itself from sibling valuation tools like dcf_valuation and listed_peer_comparison by stating it 'picks no peers and invents no multiples.'

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 tool says to supply real peer figures 'e.g. from listed_peer_comparison' and clarifies it performs only banding/blending arithmetic, not peer selection. It implies the right condition for use — when peer multiples and target metrics are already available — but does not explicitly contrast with DCF or other valuation 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?

Beyond the readOnly and idempotent hints, the description discloses that the tool never forecasts, guesses, or defaults assumptions; echoes all assumptions for auditability; reports structural issues like wacc <= terminalGrowthRate in an `issues` field; and outputs calculations rather than a verdict. This is exactly the kind of contextual behavior annotations cannot convey.

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 long but information-dense, with each sentence contributing either scope, prerequisites, issue handling, or output interpretation. It could be more scannable with structure, but there is no filler or tautology.

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, the description covers most of the contract: explicit assumptions in, auditable output, fairValuePerShare, issues reporting on structural problems, and non-advisory framing. It references metadata.kind and generate_report, giving an agent enough context to use the result responsibly; only the full result shape and the optional companyName/sharesOutstanding semantics remain implicit.

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?

Despite 0% schema-description coverage at the top level, the description names most assumption inputs: revenue growth path, EBITDA margin path, D&A/capex/NWC percentages, tax rate, WACC, terminal growth rate, and net debt. It omits the optional companyName and sharesOutstanding parameters, so it does not fully compensate for the schema gap, but it adds substantial semantic meaning beyond the raw field names.

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 ('Runs') and a concrete operation: a discounted-cash-flow valuation from caller-supplied assumptions. It clearly distinguishes this from report-generation or advisory tools, though it does not explicitly differentiate among DCF siblings like multi_stage_dcf_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?

The description gives strong usage context: reason about assumptions from financial_statements, ratio_analysis, and sector context before calling, and pair the output with an ai_interpretation section rather than treating fairValuePerShare as advice. It does not explicitly state when not to use this versus multi-stage, SOTP, or comparables, but the computation-vs-verdict framing provides clear orientation.

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?

The description aligns with the readOnlyHint and openWorldHint annotations—it searches external sources and returns results without side effects. It additionally explains the internal methodology (text-pattern heuristics, mention frequency, listed-company signal) and the deterministic top-5 selection, going beyond the annotations to provide insight into how the tool behaves.

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 sentence, but it packs in multiple steps (search, extract, rank, return) and a caveat about deterministic selection. While it is somewhat long, it remains focused and avoids unnecessary fluff, earning a high score for conciseness with minor structural complexity.

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 clarifies that the tool returns a top-5 list and explains the ranking logic, covering the output aspect despite no output schema. However, it does not address how to specify the search target (company vs sector) or other contextual fields, leaving some ambiguity in how to pass the 'context' parameter. Overall, it is reasonably complete for the tool's core purpose but misses parameter-level context.

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 description does not reference any of the parameters in the 'context' nested object, nor does it explain how to populate them (e.g., when to provide 'company' vs 'sector'). The schema itself has descriptions for only two fields (sector, companyDomain), leaving others undefined, and the tool description adds no clarification. This leaves the agent guessing about required inputs for a nested structure.

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 explicitly states the tool's function: searching industry-analyst and news sources for competitors, extracting candidates, ranking them, and returning a top-5. The verb 'searches' and specific details about the process make the purpose unmistakable, and the deterministic selection note distinguishes it from other tools that might involve model choice.

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 the tool is for discovering competitors but does not explicitly state when to use it over alternatives. It mentions 'instead of leaving selection to the calling model,' which hints at deterministic behavior, but there is no direct guidance on when to invoke this tool versus others like search_company or comparables_valuation. The context is clear enough for an agent to infer, but explicit guidance is lacking.

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 readOnlyHint annotation, the description transparently reveals the multi-source fallback strategy and guarantees never returning bare nulls, instead returning a structured not_available status with the checked sources. This is valuable behavioral detail not fully captured by annotations alone, though it does not mention every edge case.

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 structured and information-dense, with each sentence adding meaningful detail about the source order, output format, computed metrics, and fallback behavior. It avoids repetition and fluff, making it efficient for an agent to parse.

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 nested context object and no output schema, the description provides sufficient operational context: source priority, default ratio computation, and non-null fallback behavior. It does not explicitly address ambiguous combinations like both 'company' and 'sector' being provided, but the schema enum and descriptions cover most necessary input semantics.

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 descriptions cover about 50% of parameters, and the tool description adds context about output and fallback behavior but not comprehensive per-parameter semantics. It clarifies that 'sector' is for sector-level research and that includeRatios controls inline computation, but parameters like 'company', 'country', and 'listed' lack explicit semantic expansion in the description.

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 retrieves financial statements and details the source waterfall (screener.in, filing PDFs, HTML, text). It also specifies the output shape (FinancialStatement[] consumed by ratio_analysis) and default inline computation of ratios and CAGR, leaving no ambiguity about the tool's core function.

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 explains what the tool does and its fallback behavior, but it does not explicitly state when to prefer this tool over sibling tools such as ratio_analysis or company_profile. It mentions the same output shape as ratio_analysis but lacks direct guidance on use cases versus 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?

The annotations readOnlyHint=true and openWorldHint=true adequately cover the read-only nature and possibility of incomplete data. The description adds that it 'extracts candidate facts,' which transparently indicates the output may be provisional. No behavior is hidden or contradicted.

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, well-structured sentence that lists the data sources and the type of information retrieved. It is concise, without unnecessary words, and directly conveys the tool's 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?

The description gives a good high-level overview but omits details about output format, potential limitations, or how the 'context' parameter should be used. Given the tool's multi-source nature and the absence of an output schema, a bit more context about expected results or edge cases would be beneficial.

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 top-level parameter 'context' has no description in the schema, and the tool description does not explain any of its nested properties (company, country, sector, etc.). While the schema provides descriptions for a few nested fields, the overall parameter semantics are poorly communicated, especially for a required object 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?

The description clearly states the tool's function: it searches multiple named sources for funding rounds, investors, and valuation mentions, and extracts candidate facts. This distinguishes it from other tools that might provide general company profiles or financial statements.

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 does not provide any guidance on when to use this tool versus alternatives such as search_company or company_profile. There is no mention of prerequisites, scenarios, or comparison to sibling tools, leaving the agent to infer usage from the name alone.

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?

The annotations only carry readOnlyHint=false, openWorldHint=true, idempotentHint=false, leaving the description to carry the behavioral burden, and it does so thoroughly. It discloses the sector fallback, the report's closing section telling the calling model exactly which analyst-judgment sections to add next (SWOT, bull/bear, valuation with metadata.kind='ai_interpretation'), the risk screen's evidence checklist of which sources were checked, output rendering behavior, and the required writing convention with concrete examples of direct versus hedged phrasing.

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 unusually long for a tool definition at roughly 200 words, but nearly every clause earns its place: the verb+scope statement is front-loaded, the consolidation directive over nine sibling tools is placed early, and the trailing writing-style instruction, while lengthy, dictates the model's output voice and is essential to downstream quality. It is dense but not padded.

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 complex 7-parameter aggregator with no output schema, the description covers a great deal: the report's section composition, sector fallback, output formats, sibling alternatives to avoid, and the expectation that the calling model adds analyst-judgment sections. What is missing is the response shape (no output schema exists, and the description only says 'rendered in the requested output formats') and the behavioral meaning of the three enum parameters, both of which matter for a correct call.

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 57%: company, sector, companyDomain, and outputFormats already have schema descriptions, and the tool description adds no new per-parameter meaning — the sector fallback repeats the schema text and outputFormats merely lists the same formats. The three enum parameters (listed, country, reportType) remain undocumented in both schema and description, so an agent has no way to know what reportType=debt_raising changes or what listed=unlisted affects behaviorally. Since coverage sits in the mid range and the description does not compensate for the enum gaps, 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 opening clause 'Generates a complete institutional research report for a company in one call' pairs a specific verb with a clear resource, and the description then enumerates the report's contents (company profile, financial snapshot, macro/industry overview, competitor list, funding history, risk screen, news). It differentiates itself from siblings by explicitly naming the nine tools it consolidates, so an agent can select it over search_company or company_profile without ambiguity.

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 contains an explicit directive: 'Use this instead of calling search_company, company_profile, financial_statements, discover_competitors, litigation_history, promoter_background, negative_news, latest_news, and generate_pdf separately,' which clearly states when this tool is the right choice and names the alternatives. It also explains the sector fallback behavior. It stops short of a full 5 because it never states when NOT to use it — e.g., when only a single section like financial statements is needed — but the consolidation guidance 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?

Annotations already mark the tool idempotent; the description adds that the operation renders/transforms rather than mutating, and enumerates output components. It does not disclose whether the result is a returned Markdown string or a written file, which would be valuable since no output schema exists. No contradiction with 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?

One sentence with the action front-loaded and the output structure compressed into a list of meaningful deliverables. The parenthetical schema pointer is efficient and avoids repeating the nested 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?

The description captures the core rendering behavior and output features, but with no output schema it should state whether the result is a Markdown string or a file path, and ideally indicate the intended pipeline from generate_report. It is adequate but not fully complete for a 10-parameter tool.

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 documents half the parameters, and the description reinforces the semantics of 'sections' by mentioning per-section confidence/sources and consolidated citations. It does not add meaning for tags, subtitle, generatedAt, or other cover-page fields, but those are relatively self-evident and the input schema carries the detail.

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 action ('Renders'), a clear input ('structured report ... generate_report's schema'), and a concrete output ('GitHub-flavored Markdown document with table of contents, per-section confidence/sources, and a consolidated citation list'). It is unambiguous about what the tool produces, though it does not explicitly contrast with generate_pdf or generate_report.

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 'see generate_report's schema' reference implies it is meant to consume output produced by generate_report, and the output type differentiates it from PDF generation. However, it never explicitly states when to choose this over generate_pdf or when not to use it, nor does it describe the intended pipeline.

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?

It discloses important runtime behavior beyond the generic annotations: the PDF is embedded directly as a base64 resource, a downloadUrl appears only over httpStream, and no filesystem access is required. This gives an agent a clear model of how output is delivered, though it does not mention error cases or potential server-side side effects.

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 tightly packed sentences with no filler. The first sentence front-loads the core purpose and output format; the second explains delivery behavior. 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 10-parameter tool with no output schema, the description covers the input source, the output format, transport behavior, and the main layout characteristics. Minor gaps remain around defaults and error behavior, but an agent has enough information to select and invoke the 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?

With schema description coverage at 50%, the description adds meaning by referencing generate_report's schema and explaining that the input is a structured report with rendering-specific layout features. It does not individually explain every optional parameter, but the external schema reference plus self-explanatory field names covers most practical invocation needs.

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 action ('Renders a structured report'), a concrete output ('institutional-layout PDF'), and details the layout features. It also points to generate_report's schema, tying the tool's input contract to a sibling and clearly distinguishing this PDF-rendering tool from markdown or structured-report siblings.

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 clearly indicates this is for rendering a generate_report-style structure into a PDF and returning it inline for remote clients. It does not explicitly say 'use generate_markdown instead when Markdown is needed' or list excluded alternatives, so it stops short of full when-to-use/when-not-to-use 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 provide idempotentHint=true and readOnlyHint=false, so the description does not need to restate safety. It adds useful behavioral context beyond the schema: the renderer visually distinguishes AI-interpretation sections, always attaches a 'not advice' disclaimer, and the tool enforces a specific analyst voice. This enriches the agent's mental model of what invoking the tool produces.

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 front-loaded with the core purpose and then adds only high-value operational guidance. The length is justified by the need to communicate report content standards, AI-interpretation handling, and voice requirements; there is no filler or repetition of schema details.

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 covers when to call, what it does, how to mark interpretive sections, and the required writing style. For a tool with no output schema, it could more explicitly state what the call returns (e.g., a rendered report or a report object), but the phrase 'assembles ... into the standard report envelope' implies the output sufficiently for an agent.

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 50%, so the description must add parameter meaning. It clarifies that sections carry 'summary, tables, citations, and confidence' and gives precise semantics for metadata.kind = 'ai_interpretation', which the schema leaves open as additionalProperties. It does not cover every undocumented field (e.g., generatedAt, companyName), but it adds real value beyond the schema on the most behaviorally important parameters.

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 ('Assembles'), a specific resource ('a structured research report from ResearchSections ... into the standard report envelope'), and explicitly differentiates it from research-gathering tools: 'this tool does no research of its own.' It is immediately distinguishable from fact-finding siblings like financial_statements or latest_news.

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 explicit when-to-use guidance: 'Use after gathering facts with other tools.' It also rules out using it as a research tool, and explains exactly how to handle interpretive content by setting metadata.kind = 'ai_interpretation'. This is strong routing guidance relative to the many sibling tools.

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, and openWorldHint false. The description adds useful behavioral context by specifying what is assessed (config, Redis, Postgres, registry), going beyond the annotations. It doesn't mention response format or auth, but for a read-only diagnostic this is sufficient.

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 entire description is one front-loaded sentence with no filler. Every phrase adds concrete information about what the health check reports.

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 read-only health check, the description covers the important operational aspects: the subsystems checked and the diagnostic scope. It doesn't specify the response schema, but no output schema exists and the listed components give an agent enough context to invoke and interpret basic results.

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 and the schema is fully described, so there are no parameter semantics to clarify. Per the rubric, zero params warrants a baseline 4.

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 ('Reports') with a clear resource ('server health') and enumerates the exact subsystems checked: config validity, Redis cache connectivity, Postgres configuration status, and the capability registry. This clearly distinguishes it from all sibling tools, which focus on company/valuation data.

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 operational use obvious: call this when server health status is needed. It doesn't explicitly name alternatives or exclusions, but none of the siblings serve a health-check purpose, so the context is clear enough without them.

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?

The readOnlyHint annotation already covers the read-only nature, and the description uses 'retrieves' consistently. It adds some behavioral context by listing sources and fallback behavior, but does not disclose details like return format or network/external access beyond what openWorldHint suggests.

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: purpose and content are stated first, source list is compact, and usage guidance is separated cleanly. No redundant wording or unnecessary details.

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 research-retrieval tool, the description gives a strong sense of what will be returned by enumerating content areas and sources. There is no output schema, so the exact response format is not specified, but the intended use and behavior are adequately 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?

The description adds value by explaining the relationship between context.sector and context.company and the fallback behavior. The schema already provides descriptions for date, sector, company, and companyDomain, and the enum fields are self-explanatory, so the key usage semantics are covered.

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 retrieves macro/industry-level research and specifies the content areas (market structure, key players, growth drivers, TAM/market size) and source types. It distinguishes this from company-specific research, making its purpose unambiguous.

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 gives practical guidance to pass context.sector when known and explains the fallback behavior of searching by context.company's industry. It does not explicitly name sibling alternatives or say when not to use it, but the 'company-specific report should sit inside' framing communicates its intended context.

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 and openWorldHint=true, so safety is covered. The description adds useful behavioral details: the list of sources and the sort order. However, it does not disclose result limits, pagination, or how the 'open world' aspect might affect output. Given the annotations, this is adequate.

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, front-loaded sentence with no wasted words. It states the primary action and key attributes efficiently, making it easy to parse quickly.

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 has a complex context object with many optional fields and no output schema. The description fails to explain how these parameters affect behavior (e.g., sector vs company, date, daysBack), nor does it mention any return format or limitations. This is incomplete for an agent to use correctly, especially given the existence of the negative_news sibling.

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 0% and the description makes no mention of any parameters. It fails to explain the required 'context' object, the 'daysBack' field, or the various optional fields like 'sector', 'country', and 'companyDomain'. The description adds no semantic value 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 ('Retrieves') and a clear resource ('recent news coverage of a company') and names five distinct sources, while noting the sorting by publish date. This clearly distinguishes it from siblings like negative_news or search_company, so an agent can tell when to invoke it.

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 this is the tool for recent news coverage, but it gives no explicit guidance on when to use it versus alternatives such as negative_news or search_company. There is no mention of exclusions or context where a different tool would be more appropriate.

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?

The description states the data sources and the read-only nature of retrieval, which aligns with the readOnlyHint annotation. However, it does not mention potential limitations like missing data for unlisted companies or possible format variations, leaving some behavioral aspects unspecified.

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, well-structured sentence that efficiently conveys the tool's purpose, data sources, and usage context. There is no redundant information, and the dash separates the core function from the intended usage pattern.

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 explains what the tool does and why it is used, but it does not specify the output format or structure. Given there is no output schema, this is a minor gap, but the description is sufficient for understanding the tool's role within the peer comparison workflow.

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 provides descriptions for date, sector, country, and companyDomain, but company lacks a description. The tool description adds context that the company should be listed, which indirectly clarifies the 'listed' parameter, but it does not elaborate on the purpose of other fields like sector or country beyond what the schema already states.

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's function: retrieving a financial snapshot (market cap, P/E, shareholding pattern) for a single listed company. It distinguishes itself from sibling tools like discover_competitors by focusing on the per-company data retrieval needed for peer comparison.

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 says it is 'meant to be run once per company (the target and each peer discover_competitors identifies)' and that results are assembled into a peer-comparison table. This provides clear guidance on when to use this tool relative to competitors discovery and other financial analysis tools.

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 and openWorldHint, and the description adds concrete behavioral specifics: the exact sources searched (SEBI, NCLT, IndianKanoon, LiveLaw, Bar & Bench) and record categories (litigation, penalties, insolvency, disqualification). This goes beyond annotations by detailing the scope of data, which is valuable for an agent deciding if this tool fits.

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 zero filler. The first sentence packs the core function and sources, and the second gives a precise contrast with negative_news. Information is front-loaded and every clause 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?

For a read-only research tool with one nested parameter and no output schema, the description covers the essential purpose, sources, record types, and a key sibling distinction. It lacks details about return format or limitations, but openWorldHint already implies non-exhaustiveness. The description is sufficient for an agent to decide whether to invoke it and what input to provide, though it could mention the required company field.

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 0%, meaning the description must compensate for parameter meaning, but it does not. The only parameter, context, is a nested object with required 'company' and optional fields (date, listed, sector, country, companyDomain). The description never mentions how to specify the subject (e.g., company name in context.company) or what the optional fields do, leaving the agent to infer from the schema alone. Given low coverage, this is a notable gap.

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 screens specific legal/regulatory sources (SEBI, NCLT, IndianKanoon, etc.) for litigation, penalties, insolvency, and disqualification records tied to a company/promoter. It also explicitly differentiates from negative_news, making its purpose unambiguous and distinct from siblings.

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 a clear usage context by naming the sources and record types, and explicitly distinguishes this tool from negative_news for hard legal/regulatory records. However, it doesn't mention when to use it over other relevant siblings like red_flag_screen or promoter_background, leaving some ambiguity for alternative selection.

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?

The description adds context beyond the readOnlyHint by disclosing the method (pattern matching) and the sources (IMARC, Statista, etc.), aligning with openWorldHint. No contradictions with 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?

A single, well-structured sentence that front-loads the purpose and mentions sources and method. 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?

The tool is complex due to a nested context object with several properties, but the description does not explain the input structure or any prerequisites. Without an output schema, the description should at least guide on required fields like sector, but it does not. This is a significant omission.

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 has zero description coverage for the top-level 'context' parameter, and the description provides no information about how to populate it. The required 'sector' field is undocumented, and optional fields like company, country, and date are not explained. The description does not compensate for the schema gap.

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's purpose: finding market size and CAGR figures for an industry, and lists specific research sources. It distinguishes from broader tools like industry_overview by focusing on quantitative metrics, though it doesn't explicitly compare to that sibling.

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?

No explicit guidance on when to use this tool versus alternatives like industry_overview. The description implies usage for market size queries but does not state exclusions or conditions that would favor a different tool.

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 and openWorldHint=true, covering safety and scope. The description adds value by specifying the exact sources (Glassdoor, Reddit) and the categories of adverse media (fraud, layoffs, defaults, controversy), which are behavioral traits beyond the annotations. However, it doesn't describe return format or pagination, so it doesn't fully exhaust the 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 sentences with zero waste. The purpose is front-loaded, and the alternative is given in a single clear clause. No 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?

The tool has a nested object parameter with multiple fields and no output schema, so the description must guide input construction. It does not mention required fields (like company), defaults (like date or country), or what the tool returns. The only useful guidance is the purpose and the sibling routing, but not enough for 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 0%, so the description must compensate by explaining the context parameter. It does not mention that context requires a company name or any other field. The description only explains the tool's purpose, not how to fill its input. This is a critical gap for an agent to correctly invoke the tool.

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 ('screens') and resource ('news and public-sentiment sources' - Glassdoor, Reddit) and clearly distinguishes from litigation_history for hard legal records. The purpose is precise and unambiguous.

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 the alternative tool (litigation_history) and the condition that selects it ('hard regulatory/legal records'). This is direct routing guidance with no inference needed.

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?

The readOnlyHint annotation already indicates the tool is non-destructive. The description adds that it queries external regulatory sources (SEBI/MCA/registry), but does not elaborate on rate limits, authentication, or other behavioral aspects. Given the annotation covers the key safety trait, a score of 3 is appropriate.

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, focused sentence that front-loads the action and scope. It avoids unnecessary details and is easy to parse, making it highly efficient for an agent to understand quickly.

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 only one parameter (context) and a nested object, the description adequately covers the tool's purpose and usage. It does not include an output schema, but that is not required for a screening tool, and the description provides enough context for an agent to decide when 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?

The schema description for the context object already explains that context.company should be the promoter/director's name or company name for leadership screening. The tool description reiterates this, ensuring the parameter's meaning is unambiguous. This adds clarity beyond the raw schema fields.

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 action (screens) and the specific resources (SEBI/MCA/registry sources) and the purpose (disqualification, debarment, or regulatory penalty records). It also clarifies that it can take a company name to screen leadership, which distinguishes it from general company search 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 explicitly instructs the user to pass the individual's name (or company name for leadership screening) as context.company, which provides direct usage guidance. It does not mention when not to use it or alternatives, but the instruction is clear and sufficient for the primary use case.

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 mark readOnlyHint, idempotentHint, and openWorldHint, and the description adds 'deterministic' and 'Pure calculation — no search, no LLM tokens', reinforcing and extending the behavioral profile. It does not contradict annotations. It could add edge-case or output-format details, but the annotations carry part of the burden.

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 the primary purpose, with no redundant filler. The behavioral qualifier 'Pure calculation — no search, no LLM tokens' is compact and 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 calculation tool with a detailed input schema, the description covers the key inputs, the computation scope, and the output themes. There is no output schema, so a little more detail about the returned structure would improve completeness, but the description is still sufficient for selection and 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?

The schema describes the main 'statements' parameter with ordering and source context, and the description adds the notion of multi-period CAGR and the financial statement categories. However, optional fields like companyName and the exact required financial metrics (revenue, netProfit) are not explained beyond the schema, and schema description coverage is only 50%.

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?

Description uses a specific verb ('performs') with a clear resource ('financial ratio analysis over FinancialStatement objects') and lists the exact ratio categories and CAGR trend. The phrase 'Pure calculation — no search, no LLM tokens' also helps distinguish it from sibling tools that generate narratives or search data.

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 for when to use it: when deterministic financial ratio analysis or multi-period CAGR is needed, not when search or LLM-driven output is required. It does not explicitly name sibling alternatives or state when-not-to-use conditions, so it stops short of a 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?

Beyond the readOnly and idempotent annotations, the description adds important behavioral context: the tool only aggregates supplied evidence, never fabricates new evidence, and does not render an investment verdict. It also explains the optionality behavior, making the tool's operational boundaries clear.

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 but every sentence earns its place: it defines the output, names the evidence sources, clarifies non-invention and non-verdict behavior, and explains optionality. It is front-loaded with the core purpose and avoids redundant phrasing.

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 covers the aggregation purpose, severity buckets, overall severity, source traceability, and optional inputs. Since there is no output schema, the description does most of the work, though it stops short of describing the exact flag object structure or threshold logic; monthsSinceLastFunding is also 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?

The description usefully maps several parameters to their upstream tool outputs, such as litigationCases from litigation_history and negativeNewsCount from negative_news. However, monthsSinceLastFunding and companyName receive no semantic explanation in either the schema or the description, leaving a noticeable gap for those parameters.

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: it aggregates already-gathered evidence into a severity-bucketed flag list. It names the upstream tools and explicitly clarifies that it invents no new evidence and renders no investment verdict, which clearly distinguishes it from other research and report-generation siblings.

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 context for when to use the tool: after evidence has been collected from litigation_history, negative_news, ratio_analysis/financial_statements, and promoter_background. It also states that all inputs are optional and that omitted categories contribute no flags, which is practical selection guidance, though it does not explicitly name exclusions or 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?

Annotations already declare readOnlyHint, openWorldHint=false, and idempotentHint. The description adds meaningful behavior beyond these: it reports each case's validity issues rather than presenting a distorted number, invents no assumptions of its own, and performs no search. This is consistent with the annotations and gives an agent useful non-obvious expectations.

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 dense sentences with no filler: the first states the core behavior, the second explains input ownership and validity handling, and the third gives the tool-type constraint. The length is justified by the tool's complexity and every sentence adds useful guidance.

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 complex nested-schema tool with no output schema, the description covers execution modes, input ownership, validity behavior, and the no-search constraint. It could be more explicit about the exact shape of returned fair-value outputs, but 'fair-value outcomes' and per-case validity reporting give adequate 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?

Schema description coverage is low (~20%), so the description does meaningful compensation: it explains bull/bear deltas with a concrete example (+3% revenue growth, -1% WACC), clarifies that the base case is 'as given', and notes sensitivity grid values are typically WACC x terminal growth rate. It does not enumerate the required baseAssumptions fields, but those are standard DCF inputs largely inferable from property names.

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 ('Runs'), resource ('mechanical DCF'), and precise behavior: three cases (base, bull, bear) plus an optional 2D sensitivity grid. It also clearly distinguishes the tool from the sibling dcf_valuation by noting it runs the same DCF across scenarios rather than a single 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?

The description positions the tool relative to dcf_valuation, explains that the caller must supply deltas/grid values from their own analysis, and explicitly says it is 'pure calculation — no search'. It provides clear context but does not enumerate exclusions or name alternatives like multi_stage_dcf_valuation or sotp_valuation.

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 provide readOnlyHint=true and openWorldHint=true, covering the non-mutating and open-ended nature of the search. The description adds the 'domain-restricted Exa search' mechanism and the output of 'authoritative source URLs,' which is useful but does not disclose pagination, rate limits, or edge cases. Given annotation coverage, the description adds moderate behavioral 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 concise sentences with no filler. The primary action and purpose are front-loaded in the first sentence, and the usage guidance is in the second. Every word 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?

There is no output schema, so the description must explain what the tool returns. It mentions 'authoritative source URLs' but does not describe the response structure, whether it returns a single URL or multiple, or how to handle a not-found case. It also lacks any guidance on parameter usage. For a tool meant to be the entry point, this is incomplete and could cause an agent to call it incorrectly or misinterpret results.

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 0% per the signal, though the schema itself includes some inline descriptions for date, sector, and companyDomain. The tool description does not reference any parameter or explain how to provide the company name or other fields. It says 'resolve a company name' but does not indicate that the name belongs in the 'company' field of the context object. This is a significant gap since the description should compensate for the low 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 states a specific verb ('Discover'), a resource ('a company's official website, LinkedIn, and registry presence'), and a method ('domain-restricted Exa search'). It also explicitly frames its role as the first step to resolve a company name to authoritative source URLs, clearly distinguishing it from sibling tools that operate on already-resolved companies.

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 instructs 'Use this first to resolve a company name to authoritative source URLs before calling other company tools.' This gives a clear when-to-use directive and implies the correct sequencing, making it unambiguous which tool to invoke initially and why.

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