FinResearch MCP

Server Quality Checklist

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Latest release: v0.2.0
Disambiguation4/5
Most tools have distinct purposes (e.g., calculate_cagr vs calculate_dcf_valuation), but some overlap exists: get_financial_fact, get_company_financials, and get_sec_company_facts all retrieve SEC financial data, and analyze_company, ai_analyze_company, and get_dashboard_data produce overlapping analytical outputs. Descriptions help but boundaries could be clearer.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (calculate_*, get_*, run_*, analyze_*, compare_*, index_*, search_*, export_*). There are minor prefixes like 'ai_' and suffixes like '_rag', but they are applied consistently and do not break the overall convention.
Tool Count4/5
With 24 tools, the server is on the high end of the typical range, but the count is justified by the comprehensive scope covering financial data retrieval, ratio analysis, valuation models, portfolio analytics, market data, reporting, and RAG-based search. Each tool addresses a distinct need, though the set could be slightly consolidated.
Completeness4/5
The tool surface covers the full financial research workflow: fetching SEC data, calculating metrics and valuations, running sensitivity analysis, comparing companies, analyzing portfolios, retrieving market data, and exporting reports. Missing a direct tool for listing filings or comparing against industry benchmarks, but these are minor gaps that can be worked around with existing tools.
Average 3/5 across 24 of 24 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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{
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  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'attributable' and 'AI-ready', which hint at output characteristics, but it does not disclose whether the operation is read-only, whether prior indexing is required, any rate limits, or potential failures when filings are not indexed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded, immediately conveying the tool's core purpose. However, it lacks any structural breakdown (e.g., separate sections for usage, parameters, or caveats), which would improve scannability without much added length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, no annotations, 0% schema coverage), the single-sentence description is insufficient for an agent to select and invoke it reliably. The output schema exists, but it does not cover usage context, prerequisites, or behavior, making the overall description incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage for its 3 parameters, and the description does not mention any of them. It fails to explain what 'cik', 'filing_query', or 'contact_email' mean or how they influence the tool's behavior, leaving the agent without essential parameter guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates an 'attributable AI-ready research brief' from financials and indexed filings, which is a specific verb+resource combination. It distinguishes the tool's unique output form (AI-ready, attributable) from sibling tools that simply retrieve data, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for producing a research brief from financials and indexed filings, but it gives no explicit guidance on when to use this tool versus alternatives like analyze_company or get_company_financials. No exclusions or alternative recommendations are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does not mention output format, data sources, authentication requirements (despite the contact_email parameter), or any limitations. The described behavior is limited to the comparison action itself.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence that front-loads the verb and lists specific metrics without wasted words. It is appropriately concise, though it sacrifices informative detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite an output schema being present, the description is too sparse for a tool with three required parameters and a complex sibling landscape. It omits parameter explanations, usage context, and any caveats, making it incomplete for reliable agent selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no explanation of the three parameters (first_cik, second_cik, contact_email). The agent receives no help understanding what a 'cik' is or why an email is required, leaving critical semantics undefined.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Compare' and lists concrete financial metrics (revenue, margins, returns, etc.), making the tool's core function clear. It implies comparing companies, which distinguishes it from tools that retrieve single-company data, though it does not explicitly mention 'two companies' or name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus sibling tools like get_company_financials or get_financial_ratios. The description only states what it does, not the context or prerequisites for using it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the behavioral disclosure burden. It only states that a file is created at a relative project path; it does not disclose whether an existing file is overwritten, what data the report includes, or any side effects or permissions required.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The one-sentence description is concise and front-loaded with the core action and deliverable, containing no filler. However, the brevity sacrifices necessary parameter and behavior details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the tool has three parameters with zero semantic coverage and no annotations. The description is far too thin to guide an agent in setting required fields like cik and contact_email or understanding the report generation behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain the parameters, but it does not mention cik, contact_email, or output_path. The only parameter-related hint is 'relative project path,' which does not clarify the meaning or format of any argument.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Create') and names the deliverable ('Markdown company research report') with a location ('relative project path'). It is distinguishable from the sibling export_company_report_pdf by the output format, though it does not mention the underlying data source or report contents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when a Markdown report is needed but provides no explicit when-to-use guidance or comparison with siblings like export_company_report_pdf or analyze_company. It does not state prerequisites such as why cik and contact_email are required.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'deterministic' as a behavioral trait, but it does not disclose whether the tool has side effects, requires specific permissions, or how it handles data. For an analysis tool, this is a significant gap, especially given the absence of read-only hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-formed sentence that conveys the core function without extraneous words. It is appropriately concise and front-loaded, earning full credit for efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, no annotations, and the presence of many related siblings), the description is too minimal to be complete. It does not explain the scope of the 'comprehensive' analysis, the meaning of parameters, or how this tool fits into the broader financial analysis workflow. While an output schema exists, it does not compensate for the lack of contextual guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description fails to explain any of the three parameters (cik, years, contact_email). It does not compensate for the lack of schema detail, leaving users without clarity on what these parameters mean or how to use them.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: generating a comprehensive financial-health analysis from SEC data. It uses a specific verb ('Generate') and identifies the resource ('financial-health analysis'), making the core purpose clear. However, it does not distinguish itself from sibling tools like get_financial_ratios or ai_analyze_company, so it loses a point.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives. It does not mention conditions, exclusions, or alternative tools. The intended use case is only vaguely implied by the phrase 'financial-health analysis,' but no concrete decision criteria are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits but only mentions 'supported' metrics and 'plot-ready' outputs, hinting at limitations without specifics. It does not explain contact_email requirements, data sources, rate limits, or error behavior for unsupported companies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that is front-loaded with the primary action. It contains no fluff or redundant information, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (financial domain, 3 parameters, 0% schema coverage) and the presence of many sibling analysis tools, the description is incomplete. It omits which metrics are supported, what 'plot-ready' implies, and how this tool fits with other financial data tools, leaving significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain cik, contact_email, or years. The word 'annual' only tangentially relates to the years parameter, so the description fails to compensate for the schema's lack of parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns 'supported annual financial metrics and plot-ready histories for a company,' using a specific verb and resource. However, it does not explicitly differentiate from siblings like get_financial_ratios or get_company_chart_data, so it misses a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description only states what it returns, without mention of use cases, prerequisites, or exclusions, leaving the agent to guess based on tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It does not disclose whether this is a read-only operation, that SEC rate limits apply, why a contact email is needed, or any constraints on the calculation. The description adds minimal behavioral context beyond the basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that directly states the tool's purpose. It is concise with no wasted words, making it easy to parse and understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (which covers return values), the description is incomplete for a tool with two required parameters. It does not mention the need for a SEC-compliant contact email, how the company is identified, or any caveats about data availability. The tool is a financial calculation utility, and the description lacks essential context for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the tool description provides zero additional meaning for the parameters 'cik' and 'contact_email'. It does not explain what a CIK is, the format expected, or the purpose of the contact email. The schema only gives field titles, which are not elaborated by the description, so the description fails to compensate for the lack of coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Calculate') and a specific resource ('profitability, liquidity, leverage, and efficiency ratios') sourced from SEC data. It is not a tautology and the specific ratio categories help distinguish it from sibling tools like get_company_financials, though it does not explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It does not mention prerequisites, such as the requirement for a contact email or that the user should have a specific CIK. The description merely states what it does, leaving usage context entirely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description is the sole source of behavioral disclosure. It indicates a read-only operation ('Retrieve') but does not mention rate limits, authentication requirements, the need for contact_email, or any limitations regarding the metadata returned. This is minimal disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It efficiently states the tool's purpose, which is appropriately sized for a simple retrieval tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has two required parameters and an output schema, but the description provides no guidance on parameter usage, when to choose this tool over siblings, or any behavioral expectations. It is too sparse to be complete, even for a straightforward retrieval operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain either parameter (cik, contact_email). The description adds no meaning beyond the raw schema, leaving the agent to infer the purpose and format of the parameters without additional context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Retrieve') and identifies the resource ('XBRL company facts metadata from SEC EDGAR'), making the core purpose clear. However, it does not explicitly distinguish itself from sibling tools like get_financial_fact or get_company_financials, which also retrieve company data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. The only implicit usage context is that it retrieves metadata, but there is no direction on choosing this over sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only states the calculation type without explaining assumption constraints (e.g., growth rate vs discount rate) or what the output represents. This leaves significant behavioral uncertainty.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, grammatically clean sentence with no redundant words. It is appropriately concise for the purpose, though it lacks supporting detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 5-parameter financial calculation with an output schema, this description is severely under-specified. It does not explain the model's assumptions, parameter relationships, or return format, leaving the agent without enough context to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no property descriptions (0% coverage), so the description should compensate. However, the description does not mention free_cash_flow, growth_rate, discount_rate, terminal_growth_rate, or projection_years, offering no semantic guidance for parameter meanings.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it calculates enterprise value using a constant-growth DCF model, which is a specific verb, resource, and method. It distinguishes from sibling tools like calculate_enterprise_value by specifying the model type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool over alternatives like calculate_enterprise_value or run_dcf_sensitivity_analysis. The description does not offer any context for choosing this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the data returned but does not mention the data source, the required contact_email, or any potential rate limits or failure modes. This is a thin description for a data-retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one concise, front-loaded sentence that clearly states the primary output. It is not verbose, though it does sacrifice necessary detail for brevity. The structure is clean and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (which reduces the need to explain return values), the description lacks essential context: usage guidance, parameter semantics, and behavioral notes. For a tool with three parameters and a broad financial data scope, this is insufficient for reliable tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain any of the three parameters (cik, years, contact_email). It does not even hint at how years affects the data range or why contact_email is required, leaving the agent to guess parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Return' with a clear resource: structured revenue, income, cash-flow, debt, and growth data for plotting. It distinguishes itself from sibling tools like get_company_financials (broader financials) and get_financial_fact (single fact) by explicitly targeting chart-ready data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool compared to alternatives. It does not mention exclusions, prerequisites, or scenarios where other tools would be more appropriate, so the agent is left without contextual decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits but only states 'Retrieve', implying a read operation. It does not mention the required contact_email (likely for SEC rate limits), the meaning of the 'years' parameter, or any other behavioral nuances.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no filler or redundancy. It front-loads the core action and source, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 5 parameters and no annotations, the description offers minimal context. It does not explain how 'years' affects the history length, what the 'concept' parameter refers to, or clarify the output, leaving significant gaps for an agent to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any parameters. Terms like 'cik', 'concept', and 'years' are left undefined, forcing the agent to infer meaning from names alone, which is insufficient for a financial domain.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action ('Retrieve') and resource ('annual US-GAAP financial history from SEC EDGAR'), making the purpose understandable. However, it does not distinguish itself from sibling tools like get_sec_company_facts, and the term 'financial history' is somewhat generic without mentioning the specific 'concept' parameter.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as get_sec_company_facts or get_company_financials. The description simply states what the tool does without contextualizing usage or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavioral traits. It mentions the output is a percentage, which is a useful detail, but it does not state input validity requirements (e.g., beginning_value must be positive), edge-case handling (e.g., zero or negative years), or rounding behavior. The disclosure is minimal and leaves much to be inferred.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that immediately states the tool's purpose and output format. Every word contributes value: 'calculate' identifies the action, 'compound annual growth rate (CAGR)' names the target metric, and 'as a percentage' specifies the output. It is appropriately sized for a simple calculation tool and perfectly front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool lacks annotations, has a schema with no parameter descriptions, and the description is just one sentence, the overall context is incomplete. The description does not cover input prerequisites, edge cases, or usage context. Although an output schema likely exists, it does not fill the gaps left by the sparse description and schema. For a tool that requires three parameters, this is insufficient for confident use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no explanation of the three parameters. The parameter names (beginning_value, ending_value, years) are somewhat self-explanatory, but the description does not compensate for the missing schema descriptions by explaining units, valid ranges, or relationships (e.g., beginning_value must be greater than zero for CAGR to be defined). The agent is left entirely to guess parameter semantics from names alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: calculating compound annual growth rate (CAGR) and returning it as a percentage. It uses a specific verb ('calculate') and a specific resource ('compound annual growth rate'), which distinguishes it from sibling tools like CAPM cost of equity or WACC rate. The metric name alone makes the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. It does not mention any context such as 'for historical financial growth analysis' or exclude cases like 'not for simple growth rates'. There is no indication of prerequisites or scenarios where this tool should or should not be invoked.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It doesn't disclose side effects (e.g., file overwriting), permissions, or error conditions. It only states the creation action, which is a mutation but lacks security/impact details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no unnecessary words, earning high marks for brevity. However, it's slightly under-specified, which limits its usefulness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description fails to provide context on query purpose, required inputs (cik, contact_email), or how the path is used. It's minimally informative for a tool with 3 parameters and no annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain cik, output_path, or contact_email. The phrase 'relative project path' hints at output_path but doesn't map to the parameter. No semantics added for the required parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Create'), names the resource ('PDF company research report'), and adds a location constraint ('relative project path'). It clearly differentiates from the sibling tool export_company_report_markdown by specifying the PDF format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives like export_company_report_markdown. It doesn't mention use cases, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It discloses that the search is local and attribution is provided, but it does not clarify whether the operation is read-only, how attribution is represented, or any limitations. The behavioral insight is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient sentence of 10 words, front-loaded with the action. No filler or redundant content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
While the output schema may define return values, the description omits essential usage context such as the meaning of 'cik', how 'limit' behaves, and what 'attributable lexical retrieval' entails. For a 3-parameter tool with no annotations, this is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters (cik, limit, query) have no descriptions in the schema (0% coverage). The description does not mention any of them or their semantics, so the agent has no additional meaning beyond the parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Search', the resource 'locally indexed SEC filing chunks', and the method 'attributable lexical retrieval'. This distinguishes it from sibling tools like index_sec_filing (which indexes) and get_sec_company_facts (which retrieves facts).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to prefer this over other search/retrieval tools. Only a statement of function exists; there is no mention of prerequisites (e.g., needing to run index_sec_filing first) or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of disclosure. It accurately describes a pure calculation with no side effects, but offers no information about edge cases, assumptions, or numerical handling. For a straightforward calculator, this is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one short sentence with no filler, making it highly concise. It is appropriately sized for a simple calculation tool, though it could include more detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with two parameters and an output schema, and the description covers the core purpose. However, it lacks usage guidance and parameter semantics, making it minimally complete rather than fully self-sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the tool description must compensate. The phrase 'per diluted share' hints at dividing by shares, but it does not clarify the meaning of 'equity_value' or 'diluted_shares_outstanding' beyond their names, nor does it explain the exact computation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Calculate intrinsic value per diluted share.' It uses a specific verb and resource, and the 'per diluted share' phrasing distinguishes it from sibling tools like enterprise value or DCF valuation. However, it does not explicitly name alternatives, so it doesn't fully differentiate itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus others, no prerequisites, and no exclusions. The description simply states what the tool does without any context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states a broad aggregating behavior and does not reveal authentication requirements (e.g., why contact_email is needed), potential rate limits, trigger side effects, or whether it performs multiple underlying calls.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the verb and key content with no wasted words. It is appropriately concise, though the wording 'aggregate company analysis' remains slightly vague.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Even though an output schema exists, the aggregating nature, four parameters, and lack of annotations call for more context. The description does not clarify what the dashboard data contains, how parameters affect the response, or any prerequisites, making it insufficient for reliable invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the tool description does not explain any parameter. The phrase 'optional market data' vaguely hints at 'ticker', but the required 'cik' and especially the non-obvious 'contact_email' are left completely unexplained, leaving the agent without critical invocation details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'aggregate' and identifies distinct resource domains (company analysis, financial trends, optional market data) plus a clear dashboard context. This distinguishes it from sibling tools that focus on individual financial metrics or specific reports.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for a dashboard' implies a use case, but there is no explicit guidance on when to choose this tool over alternatives like get_company_financials or get_market_snapshot. The description gives no exclusions or decision rules, leaving usage context implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It only says 'return a structured grid' without explaining the calculation methodology, input requirements, or output format details. The grid structure and how results are computed remain opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler, but it is too brief to provide substantive guidance. Still, it earns its place by stating the core output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Has output schema (so return values are specified elsewhere), but the tool has two array parameters whose interaction defines the sensitivity grid, and this is not explained. With no annotations and no parameter discussion, the description is insufficient for a complex tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not mention any parameters. It fails to explain the meaning of discount_rates, terminal_growth_rates, or the relationship between them that defines the grid.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns a structured DCF sensitivity grid for charting or tabulation, using a specific verb and resource. This distinguishes it from siblings like calculate_dcf_valuation which likely returns a single value.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when a sensitivity grid is needed for charting or tabulation, but it does not explicitly compare to alternatives or state when not to use it. Given the many sibling financial tools, this is a gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It only lists the analysis metrics but does not explicitly state that the operation is read-only, whether authentication is required, or any potential side effects. The agent must infer safety from the word 'analyze'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, extremely concise and front-loaded with the core purpose. It contains no filler or redundant information, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks critical context for correct invocation: it does not explain what 'positions' should contain, how the metrics are computed, or how this tool differs from similar analysis tools like analyze_company. The presence of an output schema helps, but the input side remains under-specified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has a single 'positions' parameter with no description and schema coverage is 0%. The description adds some meaning by implying the positions must contain data to compute the listed metrics, but it does not specify required fields or data structure, leaving the agent to guess what the array items should look like.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool analyzes portfolio positions, listing specific metrics (weights, market value, unrealized return, concentration). This distinguishes it from sibling tools like analyze_company or get_dashboard_data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as calculate_cagr or get_financial_ratios. There is no mention of prerequisites, exclusions, or scenarios where this tool is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description must carry the full burden of disclosing behavioral traits. It reveals that data is 'delayed' and 'plot-ready,' which is some context, but it omits other important limitations such as supported markets, data adjustment policies, rate limits, or error handling. This leaves significant gaps for an agent relying on this description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, succinct sentence that is front-loaded with the action and resource. Every word contributes meaning, and there is no redundancy or filler, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with three parameters and an output schema, so the description does not need to explain return values. However, the absence of usage guidance and parameter semantics makes the tool only minimally viable for an agent to select and invoke correctly, despite its clear purpose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not elaborate on the range or interval parameters. The mention of 'for a ticker' ties to the ticker parameter, but the description fails to explain acceptable values or formats for range (like '1y') and interval (like '1d'), which are only present as defaults in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Return' and clearly identifies the resource as 'market OHLCV history' with qualifiers 'delayed' and 'plot-ready.' This distinguishes it from sibling tools focused on financial statements or valuations, making the tool's purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool instead of alternatives such as get_market_snapshot or get_company_chart_data. It lacks any mention of preferred scenarios, prerequisites, or exclusions, leaving the agent to infer usage solely from the tool name and sibling context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations are present, the description carries the full burden of disclosure. It mentioned the output format (decimal rate), but does not specify whether input costs are percentages or decimals, nor does it clarify edge-case behavior or side effects, leaving significant ambiguity for a financial calculation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that efficiently conveys the tool's purpose without redundancy. It is appropriately sized and front-loaded with the key action and output format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description omits critical assumptions about WACC calculation, such as whether cost of debt is pre- or post-tax and the expected input units. This makes the tool ambiguous for an AI agent without additional documentation, especially given the lack of parameter descriptions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With a 0% schema description coverage, the description must compensate for the meaning of five parameters. It provides only a general hint about market-value capital weights, but does not explain the expected format or role of each parameter (e.g., whether cost_of_debt is pre-tax), leaving the agent to rely on assumptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool calculates WACC as a decimal rate using market-value weights. It is unambiguous and distinguishes itself from sibling financial calculation tools like CAPM or DCF by clearly identifying its specific formula.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for WACC computation, but it does not provide explicit guidance on when to prefer it over alternatives such as CAPM or DCF, nor does it mention any exclusions. The intended context is clear from the tool name, but the description lacks direct comparison or conditions for use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does disclose the high-level process (fetch, chunk, index), which is more than many mutation tools, but it omits consequences such as whether indexing overwrites existing entries, idempotency, authentication requirements, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that names the operation and resource without wasted words. It is appropriately concise for the level of detail provided.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 7 parameters, 6 required, with no annotations and zero schema descriptions. The description does not cover the meaning of required fields, the indexing workflow, failure modes, or what the output represents. An output schema exists, but it cannot compensate for missing parameter and operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no explanation of the 7 parameters. Parameter names like cik, accession_number, and document_name are self-evident, but the purpose of contact_email and the behavior of the optional section parameter are completely undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action sequence ('Fetch, chunk, and index') with a clear resource ('a known SEC filing document') and purpose ('for RAG retrieval'). This distinguishes it from sibling tools like search_sec_filing_rag, which handles retrieval rather than indexing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'known SEC filing document' implies the tool is for already-existing filings with identifiers, and 'for RAG retrieval' implies it prepares data for later retrieval. However, it does not explicitly state when to use this tool versus alternatives like search_sec_filing_rag, nor does it mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden. It discloses only that the result is a decimal rate, but omits input unit requirements (e.g., decimal vs percentage), the specific CAPM formula, and any error handling or edge cases. This is insufficient for a tool with no structured annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 9 words, front-loading the key verb and resource. No extraneous information is present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple calculation tool with an output schema, so the description doesn't need to explain return values. However, the lack of parameter semantics and usage guidance leaves the description minimally viable but incomplete for an agent operating among similar financial tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only parameter names without descriptions, and the description adds no parameter-level details. With 0% schema description coverage, the description was expected to compensate but does not; 'beta' and 'expected_market_return' remain ambiguous in terms of units and expected format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Calculate' and clearly identifies the resource as 'CAPM cost of equity', with the additional clarification 'as a decimal rate' that distinguishes it from other valuation metrics like WACC. This provides a clear, unambiguous purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage (when CAPM cost of equity is needed) but provides no explicit guidance on when not to use this tool or which alternative to choose. Given the sibling tool calculate_wacc_rate, the lack of differentiation is a gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral transparency. It clearly states the tool computes a value, implying a read-only arithmetic operation. However, it omits the explicit formula (equity value = enterprise value + cash - debt) and any assumptions about units or sign conventions, leaving some ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence with no filler. It front-loads the action and states inputs in a clear order. Every word contributes to conveying the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple three-parameter calculator, this description is minimally adequate. The output schema likely documents the return format, and the inputs are self-explanatory. However, it lacks usage guidelines and formula details, making it complete only in the narrowest sense.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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. The description merely re-lists the three parameter names in a sentence without adding meaning beyond their titles. It does not explain the role of each parameter in the formula, units, or potential edge cases.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Calculate') and clearly identifies the resource ('equity value') and source inputs ('from enterprise value, cash, and debt'). This directly distinguishes it from the sibling tool calculate_enterprise_value, which performs the opposite calculation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool vs alternatives. There is no mention of exclusions or context such as 'use when you have EV and need equity value' versus the reverse tool. The appropriate scenario is only implied by the tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It states the tool's purpose but does not disclose the formula (EV = market cap + debt - cash) or any assumptions about the inputs (e.g., debt vs. net debt). This is a pure calculation, so side-effect risks are low, but the missing formula is a notable gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no unnecessary words. It front-loads the primary action and clearly states the inputs, achieving high efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple calculation tool, the description is adequate but not complete. It does not state the exact formula or handling of edge cases (e.g., negative values). An output schema exists, so return values are covered, but the description could benefit from explicitly mentioning the formula to fully contextualize the calculation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It mentions market capitalization, debt, and cash, which maps directly to the parameter names, but does not explain the relationship (e.g., that cash is subtracted) or units. The description adds minimal meaning beyond the schema titles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool calculates enterprise value from specific inputs, using a specific verb and resource. However, it does not explicitly distinguish itself from sibling tools like calculate_equity_value_from_enterprise_value, so it lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool (when you need enterprise value from market cap, debt, and cash) but does not provide explicit when-to-use/when-not-to-use guidance or mention alternatives. The context is clear but no exclusions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosure. It adds context by saying 'delayed' and 'public', indicating data is not real-time and likely requires no authentication. However, it does not address error behavior, rate limits, or the exact metadata fields, though an output schema exists to fill some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is front-loaded with the action and contains no filler. Every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with an output schema, the description is adequate but not rich. It could clarify what 'metadata' includes and explicitly differentiate from get_market_price_history, though the tool name 'snapshot' helps. The output schema reduces the need to explain return values.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has only one parameter 'ticker' with no description, and schema description coverage is 0%. The description merely repeats 'ticker' without adding format examples, acceptable values, or any clarifying details. Since the description must compensate for low schema coverage, this is a clear gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Return' and clearly identifies the resource as 'delayed public market-price metadata' for a 'ticker'. This distinguishes it from sibling tools like get_market_price_history, which focuses on historical prices, by emphasizing a snapshot of metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating it returns data for a ticker, but it does not explicitly state when to use this tool over alternatives. There is no mention of exclusions or comparisons to sibling tools, so the guidance is implied rather than direct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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