FMP MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Most tools have distinct purposes targeting specific financial analysis aspects like earnings, valuation, or market data, with clear boundaries. However, some overlap exists between company_overview and stock_brief, and between market_overview and market_context, which could cause minor confusion but descriptions help differentiate them.
Naming Consistency5/5
Tool names follow a consistent snake_case pattern with clear verb_noun or noun_verb structures throughout, such as analyst_consensus, company_overview, and earnings_setup. There are no deviations in naming conventions, making the set predictable and readable.
Tool Count3/5
With 21 tools, the count is borderline high for a financial data server, potentially overwhelming for agents. While it covers a broad domain, some tools like stock_brief might overlap with others, suggesting the set could be streamlined without losing functionality.
Completeness5/5
The tool surface provides comprehensive coverage for stock analysis, including company data, earnings, valuation, market context, and search capabilities. It supports full CRUD-like workflows for financial research with no obvious gaps, enabling agents to perform detailed analysis from overview to deep dives.
Average 4.4/5 across 21 of 21 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior, covering safety and reliability. The description adds useful context by specifying the types of dividend data returned (e.g., yield, growth rates, ex-date) and mentions stock split history, which enhances understanding beyond the annotations without contradicting them.
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 front-loaded with key information in the first sentence, followed by details in a bullet-like format, and ends with parameter explanation. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple parameter schema, rich annotations, and presence of an output schema, the description is largely complete. It clearly states what data is returned, though it could briefly hint at output structure or limitations. The tool's complexity is low, so the description adequately covers its scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description compensates by explaining the 'symbol' parameter as a stock ticker with an example ('AAPL'), adding meaningful semantics. With only one parameter, this is sufficient to clarify usage, though it could note format constraints like uppercase or validity.
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 specific action ('Get') and resources ('dividend history, yield, growth rates, and stock split history'), distinguishing it from sibling tools like 'price_history' or 'financial_statements' that focus on different financial data. It provides a comprehensive list of what information is retrieved, making the purpose explicit and distinct.
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 does not provide guidance on when to use this tool versus alternatives. It lists what data is returned but offers no context on scenarios where dividend info is preferred over other tools like 'company_overview' or 'stock_brief', nor does it mention prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide excellent behavioral coverage (readOnlyHint=true, destructiveHint=false, openWorldHint=true, idempotentHint=true). The description adds valuable context by specifying the return format ('top 10 holders with % ownership, quarter-over-quarter position changes, and institutional vs float ratio'), which helps the agent understand what data to expect. No contradiction with annotations exists.
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 perfectly structured and concise: a clear purpose statement, specific return details, and parameter explanation in just three lines. Every sentence earns its place, with no redundant information. The front-loaded purpose statement immediately communicates the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (covering safety and behavior), and the existence of an output schema (which handles return value documentation), the description is complete enough. It explains the purpose, return data format, and parameter usage, providing all necessary context for the agent to use the tool effectively without over-explaining.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining the single parameter's purpose and format ('Stock ticker symbol (e.g. "AAPL")'). This adds essential meaning beyond the bare schema, making the parameter's usage clear despite the schema's lack of description.
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 with specific verbs ('Get institutional ownership breakdown and position changes') and identifies the resource (stock ownership data). It distinguishes from siblings by focusing on institutional ownership rather than earnings, dividends, or other financial metrics. However, it doesn't explicitly contrast with similar tools like 'insider_activity' or 'market_overview'.
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 context through the parameter description (stock ticker symbol) and the mention of specific data returned. However, it provides no explicit guidance on when to use this tool versus alternatives like 'insider_activity' for ownership changes or 'company_overview' for broader company data. The agent must infer appropriate usage from the tool's name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds valuable behavioral context by specifying what the tool returns (segment percentages, growth identification, concentration risk flags), which goes beyond the annotations and helps the agent understand the output behavior.
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 efficiently structured with a purpose statement, output details, and parameter explanation in three concise sentences. Every sentence adds value, and it's front-loaded with the core functionality, making it easy to parse without wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (single parameter, read-only operation) and the presence of annotations and an output schema, the description is largely complete. It explains the purpose, output behavior, and parameter, though it could benefit from more usage guidance relative to siblings to achieve full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by clearly explaining the single parameter 'symbol' as a stock ticker symbol with an example ('AAPL'). It adds meaning beyond the bare schema, though it doesn't detail format constraints like length or valid characters.
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 specific action ('Get revenue breakdown') and resource ('by product/service and geographic region'), distinguishing it from siblings like 'financial_statements' or 'company_overview' which provide different financial data. It specifies the exact type of analysis performed, making 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for analyzing revenue segmentation, but provides no explicit guidance on when to use this tool versus alternatives like 'financial_statements' or 'peer_comparison'. It doesn't mention prerequisites, exclusions, or specific scenarios where this tool is preferred over others in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it explains merging of news sources, deduplication by title similarity, sorting by date, and event type flagging. This clarifies the tool's processing behavior, though it doesn't detail rate limits or authentication needs.
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 front-loaded with the core purpose, followed by behavioral details and parameter explanations in a structured format. Every sentence adds value: the first states the goal, the second describes processing steps, and the third defines parameters with examples. No wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, processing logic) and rich annotations, the description is mostly complete. It explains the tool's behavior and parameters well, and since an output schema exists, return values need not be detailed. However, it lacks explicit usage guidelines compared to siblings, which is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description carries full burden. It clearly explains both parameters: 'symbol' as the stock ticker with an example ('AAPL'), and 'limit' as the max items with a default value (20). This adds essential meaning beyond the bare schema, though it could specify format constraints like uppercase or valid ticker symbols.
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 purpose with specific verbs ('Get recent news articles and press releases for a stock') and distinguishes it from siblings by focusing on news aggregation rather than financial metrics, earnings, or other data types. It explicitly mentions merging, deduplication, sorting, and event flagging, which differentiates it from simple news retrieval tools.
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 for retrieving news about a specific stock, but does not explicitly state when to use this tool versus alternatives like 'market_overview' or 'stock_brief'. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool's name and description alone.
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?
Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds useful context about the dual functionality (simple lookup vs. filtered screening) and default limit behavior, but doesn't disclose rate limits, authentication needs, or pagination details beyond the limit parameter.
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 perfectly structured and front-loaded: first sentence states core purpose, second explains usage patterns, then a clean Args section documents all parameters. Every sentence earns its place with zero wasted words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (6 parameters, screening functionality), rich annotations, and presence of an output schema, the description is nearly complete. It explains all parameters and usage patterns well. The only minor gap is not mentioning what type of results are returned, but the output schema handles that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description carries full burden for parameter documentation. It successfully explains all 6 parameters with clear semantics: query purpose, exchange/sector filtering examples, market cap range meaning, and limit default. The only minor gap is not specifying units for market cap (implied as dollars).
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 purpose with specific verbs ('find stocks by name/ticker' and 'screen by criteria') and identifies the resource ('stocks'). It distinguishes this search/screening tool from sibling tools like 'price_history', 'financial_statements', or 'stock_news' which serve different analytical purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
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 different parameter combinations ('For simple name/ticker lookup, just pass query. For filtered screening, use the optional parameters'). However, it doesn't explicitly mention when NOT to use this tool or name specific alternative tools from the sibling list for different types of stock data needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds useful context by specifying the return data structure (analyst count, price targets, ratings breakdown, and FMP's own rating), which enhances understanding beyond annotations. No contradictions with annotations are present.
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 front-loaded with the core purpose, followed by a bullet-point-style list of return values and a clear parameter explanation. Every sentence adds value without redundancy, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (covering safety and idempotency), and the presence of an output schema (which handles return value documentation), the description is complete. It effectively explains the purpose, parameter, and return data without needing to duplicate structured information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description compensates by explaining the 'symbol' parameter as 'Stock ticker symbol (e.g. "AAPL")', providing clear semantics and an example. Since there is only one parameter, this is sufficient to achieve a high score, though it doesn't detail format constraints beyond the example.
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 specific action ('Get Wall Street analyst consensus') and resource ('price targets, ratings, and buy/sell distribution'), distinguishing it from siblings like 'fair_value_estimate' or 'stock_brief' by focusing exclusively on analyst data rather than intrinsic valuation or general summaries.
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 for analyst consensus data but does not explicitly state when to use this tool versus alternatives like 'fair_value_estimate' (which might provide different valuation metrics) or 'peer_comparison' (which could include analyst data in a broader context). No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context by detailing the multi-method approach (PE-based, PS-based, simplified DCF, analyst target) and peer comparison, which goes beyond annotations to explain how the tool operates and what it returns.
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 well-structured and front-loaded with the core purpose, followed by method details and return values. Every sentence adds value without redundancy, and the parameter explanation is concise and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multi-method valuation), rich annotations, and the presence of an output schema, the description is complete enough. It explains the calculation methods, peer context, and return types, leaving output details to the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates by explaining the 'symbol' parameter as a stock ticker with an example ('AAPL'), adding meaning beyond the bare schema. However, it doesn't detail format constraints or validation rules.
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 purpose with specific verbs ('calculates', 'compares', 'returns') and resources (fair value using multiple methods, peer medians, blended fair value, upside/downside, valuation rating). It distinguishes from siblings by focusing on valuation estimation rather than consensus, overview, or other financial data.
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 for valuation analysis but doesn't explicitly state when to use this tool versus alternatives like 'analyst_consensus' or 'peer_comparison'. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool's name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide key behavioral hints (readOnlyHint: true, destructiveHint: false, idempotentHint: true, openWorldHint: true), but the description adds valuable context by detailing what specific data is returned (e.g., net buy/sell over 30/90 days, cluster buying signals). It doesn't contradict annotations and enhances understanding of the tool's output scope beyond the basic safety profile.
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 front-loaded with the core purpose, followed by specific return details and parameter explanation in a structured 'Args:' section. Every sentence adds value without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations, and the presence of an output schema, the description is complete enough. It clearly states what the tool does, what it returns, and the required parameter, leaving detailed output structure to the schema. No critical gaps exist for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description compensates by explaining the 'symbol' parameter with an example ('AAPL') and clarifying it's a stock ticker symbol. This adds meaningful semantics beyond the bare schema, though it doesn't detail format constraints or validation rules.
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 purpose with specific verbs ('Get insider trading activity, statistics, and ownership context') and distinguishes it from siblings by focusing on insider trading data rather than analyst consensus, earnings, or other financial metrics. It explicitly lists the types of data returned (net buy/sell, cluster buying signals, CEO/CFO action highlights, ownership percentage).
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 context by specifying it returns insider trading data for a stock symbol, but it doesn't explicitly state when to use this tool versus alternatives like 'institutional_ownership' or 'stock_brief'. No exclusions or clear alternatives are mentioned, leaving some ambiguity about optimal use cases.
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?
Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds useful context about what the tool does (compares specific metrics, shows premium/discount and rank) and mentions 'FMP-identified peers' as the source, but doesn't disclose rate limits, authentication needs, or detailed behavioral traits 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by specific metrics and output details. The Args section is brief and directly relevant. Every sentence adds value without redundancy, making it efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (peer comparison with multiple metrics), rich annotations (covering safety and idempotency), and the presence of an output schema (which handles return values), the description is complete enough. It explains the purpose, parameters, and what to expect in results, leaving no significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage and only one parameter, the description compensates by explaining the 'symbol' parameter as 'Stock ticker symbol (e.g., "AAPL")' and providing an example. This adds clear meaning beyond the bare schema, though it doesn't detail format constraints or validation rules.
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 specific action ('Compare a stock's valuation and growth metrics against its peers') and identifies the resource ('stock'). It distinguishes from siblings by focusing on peer comparison rather than individual company analysis (e.g., company_overview) or market-wide data (e.g., market_overview).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying it compares against 'FMP-identified peers' and focuses on valuation/growth metrics, suggesting it's for relative analysis. However, it lacks explicit guidance on when to use this tool versus alternatives like analyst_consensus or fair_value_estimate, and doesn't state any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations cover safety (readOnlyHint=true, destructiveHint=false) and idempotency, but the description adds valuable context by specifying the data returned ('latest yields', 'yield curve slope', 'inversion flag', 'DCF-ready inputs'), which helps the agent understand the output format and use cases. It does not contradict 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 front-loaded with the core purpose in the first sentence, followed by specific return details. Every sentence adds essential information without waste, making it highly efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (financial data retrieval), rich annotations (e.g., readOnlyHint, idempotentHint), and the presence of an output schema, the description is complete. It explains what data is returned and its purpose (e.g., 'DCF-ready inputs'), which complements the structured fields adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema coverage, so no parameter documentation is needed. The description compensates by detailing the output content, which adds semantic value beyond the empty input schema, earning a baseline score above 3.
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 purpose with specific verbs ('Get current US Treasury yield curve and equity risk premium') and resources ('yields across maturities', 'yield curve slope', 'inversion flag', 'DCF-ready inputs'). It distinguishes itself from sibling tools by focusing on Treasury rates rather than company-specific or broader market data.
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 context by specifying the data returned (e.g., 'DCF-ready inputs'), suggesting it's for financial analysis. However, it does not explicitly state when to use this tool versus alternatives like 'market_context' or 'economic_calendar', nor does it provide exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds valuable context beyond this by specifying the data scope (upcoming estimates and recent annual history) and return structure, which helps the agent understand what to expect without contradicting 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 front-loaded with the core purpose in the first sentence, followed by specific data details and parameter explanation. Every sentence earns its place by adding clarity without redundancy, making it efficient and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (covering safety and behavior), and the presence of an output schema (which handles return values), the description is complete enough. It explains the purpose, data scope, and parameter semantics, leaving no critical gaps for agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining the 'symbol' parameter's meaning ('Stock ticker symbol') and providing an example ('e.g. "AAPL"'). This adds essential semantics beyond the bare schema, though it doesn't detail format constraints (e.g., case sensitivity).
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 purpose with specific verbs ('Get analyst earnings estimates and income statement history') and resource ('for a stock'), distinguishing it from siblings like 'analyst_consensus' (which lacks historical context) and 'financial_statements' (which is broader). It precisely identifies what data is retrieved: upcoming quarterly estimates (EPS/revenue) and recent annual income data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying it provides 'upcoming quarterly estimates' and 'recent annual income data for trend context,' suggesting it's for earnings analysis. However, it doesn't explicitly state when to use this tool versus alternatives like 'earnings_postmortem' (likely for past earnings) or 'financial_statements' (comprehensive data), leaving some ambiguity in sibling differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations: it specifies the tool analyzes 'the most recent (or specified) earnings report' and details the return data (e.g., 'post-earnings price reaction', 'guidance tone from transcript'). Annotations cover safety (readOnlyHint, non-destructive) and idempotency, but the description enriches this with operational scope and output specifics, without contradicting 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 appropriately sized and front-loaded: the first sentence succinctly lists key analysis areas, followed by a clear purpose statement and detailed return values. The parameter explanations are concise and necessary. Every sentence adds value without redundancy, making it easy to scan and understand.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (analyzing earnings reports with multiple facets), the description is complete: it covers purpose, usage, parameters, and behavioral traits. With annotations providing safety hints and an output schema presumably detailing return values, the description fills gaps effectively, ensuring the agent has sufficient context without over-explaining.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description compensates well by explaining parameter semantics: 'symbol' is defined as 'Stock ticker symbol', 'quarter' as 'Specific quarter (1-4) to analyze' with omission rules, and 'year' similarly. It adds meaning beyond the bare schema, though it could detail format constraints (e.g., symbol case-sensitivity) to reach a perfect score.
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 purpose with specific verbs ('analyzes', 'returns') and resources ('earnings report'), listing the exact components it examines (EPS/revenue surprise, YoY/QoQ comparisons, price reaction, analyst rating changes, guidance tone). It distinguishes itself from siblings like 'earnings_info' or 'earnings_transcript' by focusing on post-earnings synthesis rather than raw data or transcripts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
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 this tool ('Analyzes the most recent (or specified) earnings report'), but does not explicitly state when not to use it or name alternatives among siblings. It implies usage for post-earnings analysis rather than pre-earnings or general financial data, which is helpful but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds useful context about filtering for high-impact events and the default/limit for days_ahead, enhancing understanding beyond annotations without contradiction.
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 front-loaded with the core purpose, followed by filtering details and parameter explanation in a structured format. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter), rich annotations, and presence of an output schema, the description is complete enough. It covers purpose, filtering, sorting, and parameter details, leaving return values to the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by explaining the 'days_ahead' parameter's purpose, default value (14), and maximum (90), adding meaningful semantics beyond the basic 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 clearly states the specific action ('Get upcoming high-impact macro-economic events') and resource ('macro-economic events'), distinguishing it from sibling tools like stock_search or treasury_rates by focusing on calendar events rather than stock data or rates.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about filtering for important events (Fed, CPI, NFP, GDP) and sorting by date, which helps identify when to use this tool. However, it does not explicitly state when not to use it or name alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations by specifying what calculations are performed (yield curve spread, rotation signal, breadth signal, regime classification) and what data elements are included. While annotations cover safety (readOnly, non-destructive) and idempotency, the description provides operational details about the tool's aggregation and computation behavior.
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 efficiently structured with two sentences: the first lists the comprehensive data elements included, and the second explains the aggregation and calculations performed. Every phrase adds value without redundancy, and the information is front-loaded with the most important details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (aggregating multiple data sources with calculations), the description provides complete context about what the tool does, what it includes, and what it computes. With annotations covering safety aspects and an output schema presumably detailing the return structure, the description focuses appropriately on the tool's purpose and behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters and 100% schema description coverage, the baseline would be 4. The description appropriately explains that this tool requires no inputs and provides a comprehensive market snapshot, which aligns perfectly with the empty input 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 explicitly states the tool's purpose: to provide a comprehensive market environment snapshot combining multiple data elements (rates, yield curve, sector rotation, breadth, movers, economic calendar). It clearly distinguishes from siblings by focusing on aggregated market context rather than specific components like treasury_rates or economic_calendar individually.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by stating it 'combines' multiple data sources into 'a single environment snapshot,' suggesting this tool should be used when needing holistic market context rather than isolated data points. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds valuable context about the scope ('today's market snapshot') and return content ('sector rankings, top 5 gainers/losers, most actively traded names'), enhancing behavioral understanding without contradicting 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 front-loaded with the core purpose in the first sentence and details the return values in the second. Both sentences earn their place by providing essential information without redundancy, making it highly efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters), rich annotations covering key behavioral traits, and the presence of an output schema (which handles return value documentation), the description is complete. It effectively communicates what the tool does and what it returns, leaving no significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters and 100% schema description coverage, the baseline is 4. The description appropriately does not discuss parameters, as none exist, and focuses on the tool's function and output, which is sufficient given the empty input 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 clearly states the tool's purpose with specific verbs ('Get today's market snapshot') and resources ('sector performance, biggest movers, and most active stocks'), distinguishing it from siblings like 'market_context' or 'price_history' by focusing on a comprehensive daily overview rather than specific aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('today's market snapshot') for obtaining a broad market overview, but does not explicitly state when to use this tool versus alternatives like 'market_context' or 'stock_search'. It provides clear context but lacks explicit exclusions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide excellent behavioral context (read-only, open-world, idempotent, non-destructive). The description adds valuable operational context by specifying what data is returned (current price, 52-week range, SMA indicators, performance metrics, volatility, daily closes) and clarifying the scope ('Not for raw chart data'), which helps the agent understand the tool's output format and limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Perfectly structured with purpose first, output clarification second, parameter documentation third. Every sentence earns its place: the first defines scope, the second clarifies exclusions, the third documents parameters efficiently. No wasted words, excellent front-loading of critical information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the rich annotations (read-only, open-world, idempotent), comprehensive parameter documentation in the description, and the presence of an output schema, this description provides complete context. It covers purpose, exclusions, parameters, and output characteristics without needing to duplicate what's in structured fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by clearly explaining both parameters: 'symbol' as a stock ticker with an example, and 'period' with all valid values and default. This provides complete semantic understanding beyond the bare schema, though it doesn't explain parameter interactions or constraints.
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 purpose with specific verbs ('Get price performance, key levels, and momentum indicators') and distinguishes it from siblings by explicitly stating what it does NOT do ('Not for raw chart data'). It identifies the resource (stock price data) and differentiates from chart-focused tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('Get price performance, key levels, and momentum indicators') and explicitly excludes raw chart data usage. However, it doesn't specify when to choose this over specific sibling tools like 'market_overview' or 'stock_brief' that might overlap in financial data coverage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable context beyond annotations by specifying the comprehensive nature of the snapshot and listing example data fields (name, sector, market cap, etc.). While annotations cover safety (readOnlyHint, non-destructive) and reliability (idempotent, openWorld), the description enhances understanding of what information will be returned, though it doesn't mention rate limits or authentication needs.
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 efficiently structured: first sentence states the purpose, second provides usage guidance, third lists return fields, and fourth explains the parameter. Every sentence adds value without redundancy, and key information is front-loaded. The Args section is appropriately brief and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (readOnlyHint, idempotentHint, etc.), and the presence of an output schema, the description is complete. It explains the tool's role, when to use it, what data it returns, and parameter semantics, providing sufficient context for an agent to invoke it correctly without needing to detail return values (handled by output schema).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining the 'symbol' parameter as 'Stock ticker symbol (e.g. "AAPL", "MSFT")'. This provides clear semantics and examples, making the parameter meaning explicit despite the schema lacking descriptions. The description effectively handles the single parameter's purpose.
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 purpose with specific verbs ('Get a comprehensive company snapshot') and resources ('company snapshot including profile, valuation ratios, and current quote'). It distinguishes itself from siblings by providing a broad overview rather than specialized data like earnings or dividends, making it immediately clear what this tool does differently.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states 'Use this FIRST for any stock analysis query,' providing clear guidance on when to use this tool versus alternatives. This directive helps the agent prioritize this tool for initial company assessments over more specialized siblings like analyst_consensus or financial_statements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds valuable behavioral context beyond annotations by revealing it 'Orchestrates 8 endpoints' and returns multiple data points (days until earnings, consensus EPS/revenue, etc.). While annotations cover read-only, non-destructive, idempotent, and open-world aspects, the description enhances understanding of the tool's comprehensive data aggregation behavior without contradicting 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 efficiently structured with a clear purpose statement upfront, followed by implementation details and parameter explanation. Every sentence adds value: the first defines scope, the second explains orchestration and outputs, and the third clarifies the parameter. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (orchestrating 8 endpoints), rich annotations, and the presence of an output schema, the description is complete. It explains the tool's purpose, usage context, behavioral scope, and parameter meaning without needing to detail return values (handled by output schema) or repeat annotation information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage for the single parameter 'symbol', the description compensates by providing a clear example ('e.g. "AAPL", "DDOG"') and context that it's a stock ticker symbol for earnings analysis. This adds meaningful semantics beyond the bare schema, though it doesn't detail format constraints like length or valid characters.
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 purpose with specific verbs ('Pre-earnings positioning analysis') and resources ('consensus estimates, historical beat/miss rate, analyst momentum, price drift, and insider signals'), distinguishing it from siblings like earnings_info or earnings_postmortem by focusing on pre-earnings setup analysis rather than general earnings data or post-event analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: to answer 'should I play this earnings?' This provides clear context for pre-earnings decision-making and implicitly distinguishes it from alternatives like earnings_info (general earnings data) or earnings_postmortem (post-event analysis), though it doesn't name specific exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds useful context about returning simplified per-period financials and 3-year CAGRs, which clarifies the output format and scope beyond what annotations provide, though it doesn't detail rate limits or auth needs.
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 front-loaded with core functionality, followed by usage guidance and parameter details. Every sentence adds value: the first states what it does, the second clarifies output and usage, and the parameter explanations are concise and necessary given low schema coverage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (financial data retrieval with growth metrics), the description is complete: it explains purpose, usage context, parameters, and output characteristics (simplified per-period data and CAGRs). With annotations covering safety and an output schema existing, no further details on return values or behavioral risks are needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description carries full burden. It explains each parameter's purpose: symbol as stock ticker, period as 'annual' or 'quarter' with default, and limit as number of periods with default. This adds meaningful semantics beyond the bare schema, though it could specify format constraints (e.g., symbol casing).
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 retrieves specific financial statements (income statement, balance sheet, cash flow data) with growth rates and CAGRs. It distinguishes from siblings by specifying this is for deeper analysis after company_overview, making the purpose specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool: 'Use after company_overview for deeper analysis.' This provides clear context and distinguishes it from alternatives like company_overview, earnings_info, or stock_brief, which serve different analytical purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, openWorldHint=true, and idempotentHint=true, covering safety and idempotency. The description adds valuable behavioral context beyond this: it explains the tool returns 'momentum across timeframes, SMA positioning, valuation multiples, analyst consensus + upside, insider signal, and a heuristic quick-take signal,' which helps the agent understand the comprehensive nature of the response. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded: the first sentence gives the core purpose, followed by consolidation benefits and detailed return components, then the parameter explanation. Every sentence adds value with no redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (consolidating multiple functions), rich annotations, and the presence of an output schema, the description is complete enough. It explains the tool's purpose, usage context, return components, and parameter, without needing to detail output values (handled by output schema). This provides sufficient context for an agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
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. It provides the 'Args' section that explains the single parameter 'symbol' as a 'Stock ticker symbol (e.g. "AAPL")', adding clear semantics beyond the bare schema. This adequately documents the parameter, though it could mention format constraints (e.g., uppercase).
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 provides a 'quick comprehensive stock snapshot' with specific components listed (profile, price action, valuation, analyst consensus, insider signals, headlines). It explicitly distinguishes from siblings by stating it 'replaces the 4-tool chain (overview + price + news + consensus) with a single call,' making its purpose specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives: it states it 'replaces the 4-tool chain' and names specific sibling tools (overview, price, news, consensus) that it consolidates. This gives clear context for choosing this tool over individual specialized tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds valuable behavioral context: it describes pagination behavior ('Returns paginated transcript content with line-boundary snapping'), default fetching logic, and how to continue pagination ('Use next_offset from previous response to continue'), which annotations don't cover.
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 front-loaded with the core purpose, followed by usage rules and detailed parameter explanations. Every sentence adds value: the first states the purpose, the second explains default behavior, the third describes pagination, and the Args section efficiently documents all parameters without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, pagination logic) and the presence of an output schema (which handles return values), the description is complete. It covers purpose, usage rules, behavioral details like pagination, and full parameter semantics, leaving no gaps for the agent to operate effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining all 5 parameters in the Args section. It provides clear semantics: symbol as 'Stock ticker symbol', year/quarter for fiscal period selection with omission logic, max_chars as 'Max characters to return per call', and offset for pagination with continuation guidance.
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 specific action ('Get an earnings call transcript') and resource ('for a company'), distinguishing it from siblings like earnings_info (which likely provides summary data) or stock_news (general news). It precisely defines what the tool retrieves.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to omit year/quarter ('When year/quarter are not specified, fetches the most recent available transcript') and provides pagination guidance ('Use offset + max_chars to paginate through long transcripts'). This clearly differentiates it from tools like earnings_info or financial_statements.
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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