stockdata-mcp
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool maps to a distinct FMP data category or operational utility; even the close pairs (price_target vs analyst_estimates, key_metrics vs ratios) are clearly differentiated by their described outputs. No two tools appear to serve the same purpose.
Naming Consistency4/5The 29 fmp_* data tools follow a consistent fmp_<resource> snake_case pattern, which is highly predictable. The four utility tools (usage_status, estimate_cost, cache_clear, backend_status) break the fmp_ prefix convention and mix noun/verb styles, so the naming is not perfectly uniform.
Tool Count3/5At 33 tools, the surface is heavy and an agent has to scan many options before selecting one. The broad financial-data domain and the fmp_request escape hatch justify the scope somewhat, but the count is still above the comfortable range.
Completeness5/5The set covers the major stock-data lifecycle: search, quotes, history, technicals, fundamentals, statements, analyst opinions, insider activity, news, and screening. The generic fmp_request endpoint fills any remaining FMP API gaps, and the usage/cache utilities cover operational needs.
Average 3.1/5 across 30 of 33 tools scored. Lowest: 1.3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 6 commits 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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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
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No behavioral traits are disclosed. The description does not mention side effects, permissions, rate limits, or output format. With no annotations to supplement, the description entirely fails to inform the agent about the tool's runtime behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, which is concise, but it is too brief to be useful. It does not earn its place because it adds minimal information beyond what the tool name implies. The structure is a single phrase without any elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is incomplete relative to the tool's context. It does not mention the output schema, the nature of the data, or any usage constraints. Even with a simple schema, the description fails to provide essential information for the agent to effectively invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not explain any of the parameters (symbol, limit, period) or their meanings. Given the schema has 0% description coverage, the description should compensate by elaborating on parameter roles, but it does not, leaving the agent to guess.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Balance sheet statements' identifies the topic but does not clearly state the tool's action (e.g., retrieves, fetches, lists). It is a noun phrase rather than a clear verb-driven purpose, leaving ambiguity about what the tool actually does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does 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 (e.g., income statement or cash flow tools). It does not mention parameters, typical use cases, or any decision criteria, leaving the agent without context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description bears the full behavioral disclosure burden but adds no details: no units, sort order, default period behavior, data availability, or limitations. It is a bare label and does not disclose any operational characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
At one short phrase it is not wasteful, but it is underspecified rather than concise; a useful description still needs a verb and context. It fails to earn its place as the sole descriptive text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The input schema and output schema make the invocation structure recoverable (required symbol, optional annual/quarter period and limit), so the tool is not completely opaque. However, without annotations or usage/behavior context, the definition still lacks enough context for confident selection among sibling financial-statement tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description never mentions symbol, period, or limit. The schema's titles and defaults carry all parameter semantics; the description adds no meaning or guidance about these inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5Does the description clearly state what the tool does and how it differs from similar tools?
The description is just the noun phrase 'Cash flow statements,' which restates the tool name 'fmp_cash_flow' and lacks a verb such as 'retrieve' or 'list.' It names the report subject but does not clearly state an action or differentiate the tool from sibling financial statement tools beyond the label.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided: the description does not say when to use this tool versus fmp_income_statement, fmp_balance_sheet, or other siblings, and it gives no exclusions or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose any behavioral traits: no mention that it only returns indicator series, no indication of data freshness, no mention of limitations (e.g., only supports specific indicators, or that it may require premium API access), no note on how errors or empty results are handled. Even the presence of an output schema is not leveraged. This is nearly absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one short sentence, which is concise, but it is under-specified and conveys minimal content. It is not rambling, but it does not earn its place because it lacks substance. Given that conciseness should reward efficiency without sacrificing information, this is average at best.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 6 parameters, an output schema, and no annotations, the description is incomplete. It provides no context on how the indicator list maps to output, how to select the right indicator for a task, or what data range to expect. It is not devoid, but it is far from complete for a complex tool requiring parameter selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, but it does not. It only says 'technical indicator series', giving no clue about how to choose the indicator, set timeframes, or interpret period_length. The enum parameters are self-explanatory, but the description adds no semantics for any of the 6 parameters. Baseline 3 is not met because no compensation exists.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'A technical indicator series — useful for backtesting signal ideas.' uses vague nouns ('series') and lacks a clear verb ('get', 'retrieve', 'calculate'). It does not distinguish from siblings like fmp_quote or fmp_historical_prices, and the phrase 'useful for backtesting' is a purpose, not a function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool vs alternatives. The description vaguely implies backtesting utility, but does not state when to prefer it over fmp_historical_prices or fmp_quote, nor mention any exclusions or prerequisites. With 0% schema description coverage, this gap is significant.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description discloses no behavioral traits whatsoever. It doesn't mention the effect of the 'historical' flag, whether the output is current or archival, or any read-only/mutation implications, leaving the full burden unmet.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short, but that brevity is under-specification rather than conciseness. The single noun phrase omits essential context and has no front-loaded actionable structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given 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 needed context: no explanation of the historical parameter, no usage scenario, and no distinction between the two possible output forms. It is incomplete for a tool with even simple parameters given the absence of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to either parameter. 'symbol' and 'historical' are left entirely to the schema, so the agent gets no help understanding acceptable values or how the boolean changes behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description names the specific resource (analyst grades) and indicates two data forms (upgrades/downgrades or consensus distribution), which is clear enough to understand what is returned. However, it uses a noun phrase without an explicit verb and the 'or' creates ambiguity about modes, and it doesn't differentiate from related siblings like fmp_analyst_estimates or fmp_price_target.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. The description is a single sentence with no mention of use cases, prerequisites, or exclusions, so the agent receives no selection 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only adds the word 'forward' to indicate a future-looking scope and does not mention data source, update frequency, symbol coverage, pagination, or limitations. This leaves most behavioral traits undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single 12-word sentence with no filler, making it concise and front-loaded. However, it is so sparse that it reads as a label rather than an instruction, and the missing verb is a structural weakness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has three parameters, no annotations, and an output schema, but the description provides almost no parameter or usage context. It omits period/limit behavior, alternatives, and prerequisites, making it insufficient for confident selection and invocation despite the output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does 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 the required 'symbol' parameter or the 'period' and 'limit' parameters. It names output metrics but says nothing about how inputs affect the result, forcing agents to infer meaning solely from schema names and enum values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the resource (forward analyst estimates) and lists specific metrics (revenue, EPS, EBITDA, net income), but it lacks an explicit verb such as 'get' or 'returns,' making it a noun phrase rather than a clear action. It partially differentiates from siblings by the forward-estimates focus, but it does not clearly distinguish the tool from alternatives like fmp_price_target or fmp_grades.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 such as fmp_earnings or fmp_price_target. The description implies usage when forward estimates for these metrics are needed, but it does not state prerequisites, exclusions, or alternatives.
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 behaviors but only mentions that limit controls the number of periods returned. It does not state default behaviors, ordering, or read-only nature. This is minimal information that leaves safety and side-effect questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two short sentences with no fluff. Front-loading is good: the resource is stated first. However, it is under-specified, but conciseness itself is well-executed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description is too sparse for a tool with three parameters. It doesn't mention the required symbol, the default period of 'annual,' or any behavior about data ordering or period selection. A new agent would have to infer everything from parameter names, which is insufficient for safe invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description should compensate for all parameters. It only explains the limit parameter ('control how many periods come back'), but symbol and period are not described at all. Period's enum is visible in the schema, but its semantics (e.g., that it toggles between annual and quarterly data) are implicit. This partial compensation earns a low score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly names the resource: 'Income statements.' This unmistakably identifies the tool as returning income statement data, and the sibling tools (balance sheet, cash flow) are easily differentiated. However, it lacks an explicit verb like 'get' or 'retrieve,' making it slightly less direct than ideal, though still unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does 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 doesn't mention contexts, prerequisites, or contrast with siblings such as fmp_balance_sheet or fmp_cash_flow. The only hint is the limit parameter, but no strategic usage advice is 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 carries the full burden. It only says 'as identified by FMP,' but does not disclose whether the operation is read-only, if it has rate limits, or what the output exactly represents. No behavioral details beyond the basic purpose are given.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, but it is under-specified rather than properly concise. It lacks essential context that should be present given the absence of annotations and schema descriptions. It is brief but not efficiently informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and an output schema, the description is minimal. It does not mention what the output contains beyond 'comparable companies,' nor does it clarify any edge cases or response structure. The completeness is inadequate given the lack of annotations and schema commentary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter is 'symbol' with zero schema description coverage. The description does not explain the expected format, such as whether it requires a ticker, exchange suffix, or other details. The tool name and parameter name provide some hint, but no additional semantic clarity is added.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool returns comparable companies for a symbol, which is a specific verb+resource. It clearly identifies the primary function but does not explicitly distinguish it from other FMP data tools, such as quotes or profiles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 like fmp_profile or fmp_quote. The description gives no context about typical use cases, selection criteria, or prerequisites.
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 carry the full burden of disclosing behavior. It only lists data contents and does not mention request behavior, default period/limit implications, data availability caveats, ordering, or pagination. 'Over time' hints at a time series but is insufficiently descriptive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence that is efficient and front-loaded with the core data attributes. It wastes no words, but its brevity comes at the cost of informative content, so it does not earn a 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, three parameters, and a large sibling tool set, this description is too sparse. The output schema covers return structure, but usage context, parameter effects, and behavioral expectations remain unexplained, making it inadequate for reliable tool selection in a complex domain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds nothing about symbol, limit, or period. Although parameter names are somewhat self-explanatory, the description provides no additional semantics or guidance on how they affect the returned data, leaving a significant gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the specific resource (enterprise value, market cap, net debt) and temporal scope ('over time'), which clearly conveys what data the tool returns. However, it lacks an explicit verb and does not differentiate from sibling metric tools like fmp_key_metrics or fmp_quote.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives, nor any exclusions, prerequisites, or typical use cases. The bare data-list statement leaves tool selection entirely to the agent's inference from the name and siblings.
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 reveals the tool has two behavioral modes (transactions vs. statistics) but omits critical details such as whether the operation is read-only, output format specifics, pagination (beyond the 'limit' parameter), rate limits, or authentication requirements. Significant gaps remain 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that conveys the core purpose without fluff. However, it is perhaps too terse, missing valuable details that would not add excessive length. It is front-loaded and easy to parse, scoring high on efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema, the description need not explain return values, but it still lacks essential context about the two modes, the meaning of 'limit', and typical use cases. The description is too sparse to fully guide an agent in selecting and invoking the tool correctly, especially with no annotation support and low schema coverage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must explain all parameters. It implicitly suggests that 'symbol' identifies the company and 'statistics' toggles aggregate stats, but it does not explain 'limit' at all. It also fails to clarify the exact meaning of each mode or the expected data structure. The description adds minimal value beyond the parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves insider transactions for a symbol or aggregate buy/sell statistics. It uses the specific verb 'insider transactions' and identifies the resource (symbol) and the two modes. While it's not exhaustive, it distinguishes this from sibling financial data tools like dividends or earnings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions two usage modes ('for a symbol' vs 'aggregate insider buy/sell statistics') but provides no guidance on when to choose this tool over alternatives, nor any exclusions or prerequisites. It does not explain when 'statistics' is appropriate or how it differs from other financial data endpoints.
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 of behavioral disclosure. It does not mention that this is a read-only operation, any authentication needs, rate limits, or behavior with invalid symbols. It only lists ratio types, failing to communicate important behavioral aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, terse sentence that immediately conveys the tool's purpose without any fluff. It is front-loaded with the core idea and earns its place with zero wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 4 parameters, an output schema (though unknown contents), and no annotations. A complete description would need to explain parameter semantics, usage context, and possibly output structure. This description only lists ratio categories, leaving the agent to guess about the rest, making it insufficient for reliable tool selection and correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the schema provides no parameter details. The description also fails to explain any of the four parameters (symbol, ttm, limit, period). It doesn't clarify what ttm stands for, how limit affects the output, or what period options mean. The description adds no value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Financial ratios' and lists specific categories (margins, liquidity, leverage, returns, payout), clearly indicating the tool provides ratio-based financial metrics. This differentiates it from raw financial statements like fmp_income_statement or fmp_balance_sheet, though it lacks a verb like 'get' or 'list' and could be more explicit about being for a specific company.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 alternative financial data tools. It doesn't mention prerequisites, exclusions, or how it differs from fmp_key_metrics or fmp_financial_growth. The description is purely descriptive with no use-case 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?
There are no annotations, and the description only lists data fields; it does not disclose side effects, permissions, operational constraints, or output behavior meaning.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
One clear sentence adding compact field list to the description; no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema is present, and the tool is a simple lookup/profile tool, but the description lacks behavioral detail such as what exactly is returned, error conditions, or where input comes from.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The JSON schema fully defines the single property ('symbol' string). The description does not restate its meaning, but with only one param and schema coverage at 100% this is acceptable.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
Description names the domain (company profile) and enumerates contained fields, but uses a noun phrase with no fetch/retrieval verb and does not differentiate this historical/fundamental data tool from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided for when to use this tool versus sibling fundamental data tools, nor any exclusions or alternative 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?
With no annotations, the description carries the full burden of disclosing behavior. It adds minimal context beyond purpose, mentioning 'across the three statements' but not explaining how the data is structured, whether it's read-only, any rate limits, or potential pitfalls. This is insufficient for a tool operating without structured safety hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no fluff or repetition. It efficiently communicates the core purpose, making it appropriately sized for a straightforward data-retrieval tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with three parameters and an output schema, so the description doesn't need to explain return values. However, it lacks any behavioral notes, usage scenarios, or caveats that would aid selection among many closely related financial data tools. It is minimally complete but leaves gaps in context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not compensate by explaining the parameters. It hints at the 'period' parameter through 'period-over-period' but omits details about 'limit' and how 'period' values (annual/quarter) affect the output. The schema provides defaults and enums but lacks semantic meaning, leaving the agent to guess at parameter intent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's function as providing period-over-period growth rates across the three financial statements. Although it lacks an explicit verb like 'retrieves' or 'returns', the noun phrase conveys the output and distinguishes it from sibling tools that focus on raw statements, ratios, or other metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 does, with no mention of suitable scenarios, exclusions, or differences from related tools like fmp_income_statement or fmp_ratios. This leaves the agent without explicit selection criteria.
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 carry the full burden of behavioral disclosure. It fails to mention that this is a read-only operation, whether any special permissions are needed, or what the response format looks like (e.g., returns historical metrics for a period). The description only lists metric types and one parameter behavior, leaving much behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loads the metric list, but it includes a docstring-like 'Args:' section that is redundant with the input schema. While not overly verbose, the 'Args:' section adds little value and could be trimmed. The core description is concise enough but has minor non-essential structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema, the description need not explain return values in detail. However, with no annotations and low schema coverage, it should provide more context about the tool's scope (e.g., that it returns historical metrics for a given symbol, not just current values). The description is adequate for a simple metrics tool but lacks depth to be fully complete, such as noting that it returns time-series data or that 'ttm' is a special mode.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description needs to compensate. It explains only the 'ttm' parameter ('Return trailing-twelve-month values instead of discrete periods'), which adds value beyond the schema. However, it omits details about 'period', 'limit', and 'symbol' meanings, which are inferable from their schema definitions but could benefit from clarification (e.g., 'limit' likely controls number of periods returned). Thus it partially compensates but not fully.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides 'Per-share and valuation metrics' such as P/E, EV/EBITDA, ROIC, and FCF per share. This distinguishes it from sibling tools like fmp_ratios or fmp_enterprise_values, though not explicitly naming them, the focus on per-share and valuation metrics differentiates it sufficiently.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. It does not mention any prerequisites, exclusions, or comparisons to sibling tools. The only usage hint is the ttm parameter for trailing-twelve-month values, which is a functional detail rather than usage guidance.
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 explains the 'levered' parameter's effect (post-debt DCF) but does not mention any potential side effects, permissions, rate limits, or the nature of the response. As a read-only data retrieval tool, it implicitly seems safe, but the description does not explicitly confirm this or discuss any 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extraordinarily concise: two sentences that cover the tool's purpose and the key parameter. It is front-loaded with the core action and contains zero filler. Every sentence earns its place, and the formatting with 'Args:' neatly separates parameter explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, an output schema exists), the description is adequately complete for the core functionality. It explains the purpose and the levered parameter, and the output schema presumably covers return values. However, it lacks usage guidance and any behavioral caveats, which might be expected in a more comprehensive description for an agent to make an informed choice. Since the output schema exists, the description doesn't need to explain returns, but the missing contextual aspects reduce completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It explicitly explains the 'levered' parameter's meaning (use the post-debt DCF), which adds value beyond the schema's simple boolean title. However, it does not elaborate on 'symbol' beyond the phrase 'for a symbol,' leaving it to the schema's 'Symbol' title, which is clear enough. The description partially compensates for the lack of schema descriptions but could be more thorough.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a discounted cash flow valuation for a given symbol, which is a specific and distinct purpose among the sibling tools (e.g., dividends, earnings, historical prices). It uses a specific verb ('valuation') and resource ('symbol'), making the purpose discernible. However, it doesn't elaborate on the output format or differentiate strongly from alternatives that might also return valuation-like data, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 any conditions or exclusions. It only states what the tool does, leaving the agent to infer that it is for DCF calculations. There is no mention of when not to use it or alternative tools for similar financial metrics.
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 full burden. It adds some behavioral context by noting 'current' rating snapshot and 'summary and consensus', implying aggregated point-in-time data. However, it does not disclose limitations, data source, or error behavior, leaving gaps for a no-annotation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that directly states the core output. It is concise, front-loaded, and contains no filler or redundant information, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a simple one-parameter tool with an output schema present, the description provides a basic understanding of the tool's purpose. However, it lacks usage guidance, parameter explanation, and any behavioral caveats, making it minimally adequate but not fully complete for an agent to select confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter (symbol) with 0% description coverage. The tool description does not mention the parameter at all, nor does it clarify format (e.g., ticker case, exchange suffix). Since schema coverage is low, the description should compensate but fails to do so.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource: analyst price-target summary and consensus, plus current rating snapshot. It is specific enough to differentiate from some siblings like fmp_dividends or fmp_news, though it lacks a clear verb and could be confused with fmp_analyst_estimates or fmp_grades.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description implies it provides price-target data, but among many related siblings (fmp_analyst_estimates, fmp_grades, fmp_quote), there is no direction on how to choose between them.
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 behavioral disclosure. The description is minimal and does not disclose any behavioral traits such as data source, update frequency, or potential limitations (e.g., only available for certain companies). It doesn't mention what the output contains beyond the segmentation, leaving the agent uncertain about the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, a single sentence, and front-loaded with the key purpose. It is efficient and to the point, though it could be slightly more informative without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema and only 2 parameters, the description is minimal. It does not explain the return structure, any limitations, or how to interpret the data. For a financial data tool, more context is needed, such as whether the data is quarterly or annual, or if it requires specific permissions. The description is incomplete for a tool that likely returns complex segmented data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 mentions 'product line or by geography' which maps to the 'by' parameter's enum values, but it does not explain the 'symbol' parameter beyond what the schema provides. The description adds some meaning for 'by' but not for 'symbol', and with only 2 parameters, the coverage is partial.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Revenue split by product line or by geography' clearly states the tool's purpose: it provides revenue data segmented by product or geography. It distinguishes itself from sibling tools like fmp_income_statement or fmp_quote by focusing on segmentation, though it doesn't 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 Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for revenue segmentation but does not provide explicit guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites or context, such as needing a valid symbol or that it's for fundamental analysis. The 'by' parameter default is clear from the schema, but the description doesn't elaborate on use cases.
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 adds only the expected date format and point-in-time nature, but does not disclose the effect of the `pe` flag, default date behavior, or output characteristics beyond what the output schema already covers.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single, focused sentence with no filler; every phrase earns its place by identifying the resource, optional mode, and date format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With only two optional parameters and an output schema present, the description is minimally sufficient for basic invocation and purpose identification. It lacks parameter behavior details and usage context that would make it fully self-contained, but the tool's simplicity lowers the requirement.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description hints that the `pe` boolean toggles between performance and P/E ratios and provides the date format, offering partial meaning beyond the bare input schema. However, it does not explicitly map these meanings to the parameter names, so it incompletely compensates for the 0% schema description coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource ('sector performance, or sector P/E ratios') and the temporal scope ('as of a date (YYYY-MM-DD)'), distinguishing it from sibling FMP data endpoints. It lacks an explicit verb, but the intent is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 fmp_market_movers or fmp_screener. The description simply states what it returns, leaving the agent to infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description alone carries the transparency burden. It discloses that the tool returns actual vs estimated EPS/revenue and covers history and upcoming dates, which is useful behavioral info. However, it omits any details about rate limits, data freshness, or side effects (though it's likely read-only), so it's 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with a colon-separated list of contents. It's immediately clear and free of filler, earning full marks for efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params, 1 required, no nested objects) and the presence of an output schema, the description covers the essential purpose and data coverage. It doesn't explain edge cases or default behavior, but for a straightforward earnings data tool, this is nearly complete. The only reason it's not a 5 is a slight lack of detail on what exactly 'upcoming dates' means (e.g., next quarter only or a range).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate by explaining parameters. It doesn't mention 'symbol' or 'limit' at all. The description's mention of 'actual vs estimated EPS and revenue' describes the output data, not parameter semantics. This is insufficient for an agent to understand what 'limit' controls (e.g., number of periods) without additional inference.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the resource (earnings data) and key fields (actual vs estimated EPS and revenue) and distinguishes from similar financial tools by mentioning both history and upcoming dates. It doesn't explicitly exclude any sibling behavior, but the specific field list makes it more than vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for earnings history and upcoming dates but gives no when-to-use guidance or comparison to siblings like fmp_earnings_calendar or fmp_analyst_estimates. There are no exclusions or alternative suggestions provided, offering minimal decision support for an agent.
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 for behavioral transparency. It only states the function and date format, but does not disclose anything about safety (read-only), side effects, output structure, rate limits, or any other behavioral traits. For a simple read operation it is minimally transparent but lacks depth beyond the basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the core purpose and date format. There is no redundant or extraneous information, making it highly efficient and well-structured for agent comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with only two parameters and an output schema exists, so the description need not explain return values. However, it lacks specifics such as the market(s) covered (e.g., US only), whether the dates are inclusive, or any pagination behavior. While the output schema likely covers some details, the description alone feels incomplete for a production agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It does provide the date format (YYYY-MM-DD) which clarifies the expected format for from_date and to_date, adding value over the schema's bare string type. However, it does not explain inclusive/exclusive boundaries, required vs optional, or semantics beyond the obvious start/end. This is adequate but minimal.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides a 'Market-wide earnings calendar over a date range', which is specific about scope and data type. It distinguishes from likely sibling tools like 'fmp_earnings' by implying market-wide rather than per-ticker, though it doesn't explicitly name alternatives. The verb is implied ('calendar' as a noun), but the meaning is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description includes a clear context (market-wide earnings over a date range) which implies when to use it, but it does not provide explicit guidance on when not to use it or mention alternatives. There is no exclusion or contrast with other earnings-related tools, so usage indication is only 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?
With no annotations, the description carries the burden. It only states the data returned, but does not disclose whether the operation is read-only, potential errors, rate limits, or any side effects. Likely a read-only query, but not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
One concise sentence that directly states what the tool does without any fluff or redundancy. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lists the two scores returned, but does not mention the output format (e.g., a JSON object with separate fields, historical vs current values), or any additional context like whether the scores are computed from the latest financial statements. Given the absence of an output schema, the description could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'symbol' is mentioned in the description as 'for a symbol', implying it is a ticker, but no further explanation is given about accepted formats (e.g., uppercase vs lowercase, exchange prefix). The schema itself only names it 'symbol', so the description adds minimal semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states that the tool returns Altman Z-score and Piotroski F-score for a given symbol. The verb is implicit but the resource is specific, and it distinguishes from sibling financial data tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus other financial metrics like fmp_ratios or fmp_dcf. Does not mention scenarios where these scores are useful or when a user should prefer them.
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. It explains the return content (dates and amounts) but omits other behavioral traits such as pagination, ordering, or any limitations. The description is not misleading but adds minimal context beyond the purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It efficiently conveys the core purpose and output fields.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple and the output schema exists, so the description need not explain return structure. However, the lack of parameter semantics and usage guidance makes it only minimally complete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does 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 clarifies the 'symbol' parameter indirectly ('for a symbol') but does not mention the 'limit' parameter or its default behavior. This leaves a key parameter under-explained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: retrieving dividend history for a symbol, with specific data points (declaration, record, payment dates, amounts). This distinguishes it from sibling tools like fmp_earnings or fmp_historical_prices.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 any prerequisites, exclusions, or comparison to related tools like fmp_earnings or fmp_historical_prices.
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 transparency burden. 'Screen' implies a read-only query operation, but it does not explicitly disclose safety, rate limits, pagination, or how filters are combined. It is adequate but has noticeable gaps in behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose, and includes a conditional usage hint. Every sentence earns its place with no filler or redundant detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given 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 18 parameters and a minimal description. It does not explain how filters interact, whether limit caps results, or what numeric units are expected. This is a high-complexity tool, and the description is too sparse to fully guide correct use without external knowledge.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 18 parameters with 0% description coverage, and the description offers almost no parameter-level meaning. It only advises using fmp_reference to get exact sector/industry values, which helps just two parameters. Units for numeric filters (price, market cap, beta, etc.) and filter combination logic are left unspecified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool screens for stocks based on fundamental and market filters. This distinguishes it from sibling tools like fmp_quote, fmp_profile, and fmp_market_movers by highlighting its screening nature, though it does not enumerate the specific filter dimensions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear usage context: 'Screen for stocks matching fundamental and market filters.' It also provides a practical prerequisite—calling fmp_reference for exact sector/industry spellings. However, it does not explicitly mention when not to use the tool or compare it to alternative screening approaches.
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 must convey behavioral context. It clearly indicates the tool returns market mover data, but provides no detail on data freshness, pagination, or market coverage. For a read-only data retrieval tool, this is acceptable but not outstanding. The description doesn't contradict any annotations, but it also doesn't add much beyond what the name and schema suggest.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with 12 words, perfectly concise and front-loaded. It uses a list format that maps directly to the enum values, making it highly skimmable. Every word earns its place with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with a single optional parameter and no required fields, the description is largely sufficient. It covers the main use case, and the enum values are self-explanatory. However, the output schema exists but is not examined, and the description could have noted that this tool is for real-time market data as opposed to historical (which is covered by fmp_historical_prices). Overall, it's adequate but not exhaustive in guiding an agent on all edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the description provides no parameter-level details. However, the sole parameter 'kind' has an enum with self-explanatory values ('gainers', 'losers', 'actives') that are clearly listed in the schema. The description's phrase 'biggest gainers, biggest losers, or most actively traded' exactly mirrors the enum values, adding no extra meaning beyond the schema. Given that the schema alone fully explains the parameter, the description adds minimal semantic value beyond what's already present.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Today's biggest gainers, biggest losers, or most actively traded names' clearly states the tool's purpose with specific verbs and resource types (market movers). It differentiates from siblings like fmp_screener or fmp_quote, though it doesn't explicitly name alternatives. The description is concise and informative, though it could have been more tied to the exact enum values.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (to get market movers), and the parameter enum values ('gainers', 'losers', 'actives') are self-explanatory. However, no explicit when-to-use vs. alternatives is provided. Since this is a simple tool with obvious use cases, the implied guidance is adequate, but it doesn't explicitly state when not to use it or suggest alternatives.
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, and the description does not disclose any behavioral aspects such as whether the operation is read-only, potential rate limits, or pagination behavior. This leaves the agent without essential information about side effects or constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no redundant words, efficiently conveying the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema likely exists, the description adequately covers the input parameters and purpose. It could be slightly more specific about the nature of the news (e.g., source, date range), but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description explains that 'symbols' are comma-separated, adding meaning beyond the schema. However, the 'limit' parameter is not described, though its default value is given. Partial semantic coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves recent news articles for given symbols, distinguishing it from other financial data tools that focus on prices, dividends, or fundamentals.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving news, but does not explicitly state when to use it over alternatives or provide any exclusions. It relies on the tool name and context to convey the purpose.
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 disclose behavioral traits. It explains the 'short' parameter's effect (returning only price/volume), but does not mention any side effects, auth requirements, rate limits, or read-only nature. It adds some transparency about the response shape but is otherwise minimal for a tool that presumably performs a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: one sentence for purpose and a short arg list. Every word serves a purpose normalization coverage is high, no fluff. Front-loaded purpose statement followed by param explanations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is a simple query with two params, both explained. An output schema exists (though not shown), so return format may be self-documenting. However, without annotations and no mention of what fields a 'full quote' includes, the description leaves some uncertainty. It is adequate for a straightforward read tool but lacks explicit completeness about response contents or any usage caveats.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does 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 clearly explains both parameters: 'symbols' accepts one or multiple comma-separated tickers, and 'short' changes the response to only price/volume. This adds meaningful semantic detail beyond the schema's basic type information. Slight deduction because it doesn't specify the exact format for symbols (e.g., case sensitivity) or the full response structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Current quote for one or more symbols,' with a specific verb (quote) and resource (symbols). It distinguishes from siblings like fmp_historical_prices and fmp_screener by focusing on current data. The optional 'short' parameter nuance is also explained, adding specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fetching real-time quotes but doesn't explicitly state when to use this tool vs alternatives (e.g., when historical prices are needed, use fmp_historical_prices). No exclusions or alternative tools are mentioned, so guidance is limited to implied context.
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. The verb 'look up' indicates a read-only operation, and the tool's purpose suggests no side effects. However, the description does not mention any potential rate limits, authentication requirements, or specifics about the response format, leaving some behavioral aspects undisclosed. It's not misleading, but it's minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that immediately states the tool's function. There is zero fluff, every word contributes to clarity, and it is front-loaded with the key action and object. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one enum parameter and has an output schema (though not shown), so the description does not need to elaborate on return values. The description captures the two main use cases (screener filters and index constituents) and is adequately complete for its simplicity. However, it could add a note about relation to the screener tool (e.g., 'use these values when calling fmp_screener'), which would make the context more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter ('kind') with a full enum, but no description strings (0% schema description coverage). The tool description adds a categorical distinction between screener filter values (sectors, industries, exchanges, countries) and index constituents (sp500, nasdaq, dowjones), which provides some added semantics. However, it doesn't explain each enum value's meaning beyond that rough grouping, so the description only partially compensates for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to look up valid values for screener filters or index constituents. It uses a specific verb ('look up') and resources ('valid values for screener filters, or an index's constituents'), and it distinguishes from sibling financial data tools by focusing on reference data rather than raw market data or metrics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for prefilling screener filters or obtaining index membership, but it does not explicitly state when to use this tool versus alternatives like fmp_screener or fmp_index_constituents (none exist). It lacks explicit 'use this when you need filter values' guidance or exclusionary language, so the usage context is implied rather than instructed.
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. It mentions the API key is added automatically and that it's an escape hatch, but it does not disclose potential side effects, rate limits, error handling, or what happens when the endpoint is invalid. For a generic request tool, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise but includes a multi-line docstring with args. It front-loads the main purpose and then adds parameter notes. Some redundancy in the args block, but overall compact and readable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 2 parameters, a permissive schema, and an output schema present, the description is mostly adequate. It provides a broad list of use cases and parameter examples. However, it could mention response format or typical errors, but the output schema may cover return values. Given the generic nature, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does 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 explains the endpoint format with an example ('etf/holdings') and parameter format with example, plus notes the API key is auto-added. The params schema is permissive (additionalProperties), so examples are crucial. Two parameters well-documented via examples, though not exhaustive.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it calls any FMP stable endpoint as an escape hatch for the ~250 endpoints without a dedicated tool, listing examples. This distinguishes it from siblings which target specific endpoints like fmp_quote or fmp_earnings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly frames the tool as an escape hatch for endpoints without a dedicated tool, implying when to use it (when no sibling matches). However, it does not explicitly say when NOT to use it, e.g., for endpoints that already have dedicated 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?
With no annotations, the description carries the full burden and does well — it discloses that cache hits are counted separately as 'saved', that the count reflects total spend across all clients on the machine, and that it is not session-specific. It does not explicitly state that the operation is read-only, but that is strongly implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three tight sentences, each adding distinct value: what it reports, what it excludes (cache hits), and the scope (shared across clients). It is front-loaded with the core purpose and contains no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no output schema, the description is quite complete — it explains the semantic content of the result (spent, remaining, reset time) and clarifies edge cases (cache hits, cross-client sharing). It stops short of describing the exact output format, but that is unnecessary given no output schema exists.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so there is nothing to describe — this is the baseline 4 for a parameterless tool. Schema coverage is trivially complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool provides (requests spent, remaining, budget reset time) and distinguishes it from the financial-data siblings by being a meta-tool about API usage. It lacks an explicit verb like 'Returns' or 'Shows', but the meaning is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly signals when to use it (when checking budget/usage) by clarifying what it counts (requests that left the machine, excluding cache hits) and its shared scope. However, it never explicitly states when to use this over alternatives like estimate_cost or cache_clear, leaving the guidance to inference.
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 alone must convey behavior. It states the tool reports configuration, implying a read-only action, but doesn't explicitly disclose side effects, permission needs, or any other behavioral traits. It's adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that fully conveys the tool's function without extraneous words. It's concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no parameters and an output schema (present, though not detailed here). The description adequately covers the tool's purpose. Slight lack of detail about what 'backends' means or whether it's read-only, but given its simplicity)Skip it's complete enough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so the schema is trivially complete. Baseline for 0 params is 4; the description doesn't add parameter details, but none are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's verb ('Report') and resource ('which backends are configured and how this server is set up'). It distinguishes itself from sibling tools (all financial data tools) by focusing on backend/configuration status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives context on what it reports but does not explicitly state when to use it over alternatives or any usage exclusions. Given the tool's purpose (server status), usage is implied, but no direct guidance on when not to use it.
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 transparency burden. It discloses a key behavior: omitting from_date returns the full history, which is 'large'. However, it does not mention rate limits, pagination, error handling, or output size limits. The warning adds value but leaves other potential side-effects undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a compact docstring with a one-line summary and a clean Args list. Each parameter gets a single line with essential details. No redundant sentences; every clause provides value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description does not need to detail return values. It covers core behavior (OHLCV, intervals, symbol types) and the full-flag variation. It lacks edge-case guidance (e.g., date range validation) but is adequate for a data-retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description explains all 5 parameters: symbol Types, date format, interval enumeration, and the full flag's effect on columns. It adds meaningful context (e.g., from_date warning) beyond the bare schema titles, compensating well for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Historical OHLCV data' with specific resource types (ticker, index, crypto, FX pair). It distinguishes from siblings like fmp_dividends and fmp_earnings by focusing on price data. The verb is implicit ('retrieve') but the intent is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains symbol types and interval options but does not explicitly state when to use this tool versus alternatives like fmp_quote for current prices or fmp_technical_indicator for derived data. There is no exclusion or alternative naming, 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.
- 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. It discloses the tool's behavior (search by fragment or name) and mentions optional filters (exchange, limit). However, it doesn't describe what happens with no results, case sensitivity, or whether it returns multiple matches. The description is adequate but not rich in behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured. It starts with a one-sentence purpose, then lists parameters with clear examples. No wasted words; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (3 params, 1 required) and has an output schema, so the description doesn't need to explain return values. It covers the essential usage. The only missing piece is a note on behavior when no match is found or when multiple matches exist, but given the simplicity and output schema, this is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does 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 does: it explains 'query' as 'A symbol fragment ("AAP") or company name ("Apple")', 'limit' as 'Max results', and 'exchange' as 'Optional exchange filter, e.g. "NASDAQ"'. This adds meaning beyond the schema's bare type definitions. The only minor gap is not specifying the default behavior for limit (though schema has default 10) or exchange (default null).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Find a ticker symbol by symbol fragment or company name.' It uses a specific verb ('Find') and resource ('ticker symbol'), and distinguishes itself from siblings like fmp_quote or fmp_profile by focusing on symbol lookup rather than data retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool: when you need to resolve a symbol fragment or company name to a ticker. It doesn't explicitly state when not to use it or name alternatives, but the context of sibling tools (e.g., fmp_quote for quotes, fmp_profile for company info) makes the usage clear. A slight deduction for not explicitly mentioning 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?
With no annotations provided, the description carries the full transparency burden and does well: it explains that expired-mode is safe, that clearing live entries means paying for requests again, and that price bars never expire by design. It does not mention authorization or rate-limit implications, but it covers the core behavioral consequences for a cache-clearing tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded, with the core purpose in the first sentence. Every subsequent sentence adds needed behavioral context about safety, cost, and the immutability of historical price bars, with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one optional parameter and an output schema, the description covers the main usage context well: when to use it, what the safe default is, and the cost tradeoff of clearing live entries. The main missing piece is a fuller explanation of the backend enum values, but overall the description is sufficiently complete for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 for the single 'backend' parameter. It usefully explains what 'expired' does and that clearing live entries is costly, but it does not clarify what the 'all', 'fmp', and 'qualtrim' values specifically target. This leaves a meaningful gap in understanding the full parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The opening sentence 'Drop cached responses' states the exact verb and resource, making the tool's purpose immediately clear. It also distinguishes cache_clear from its data-retrieval and status sibling tools, none of which manage cached data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says to use this when data looks stale despite its TTL, giving a clear trigger condition. It also provides important usage nuance: the 'expired' default is safe, while clearing live entries has a cost implication. The note about price bars never expiring further guides appropriate 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?
With no annotations, the description carries full disclosure burden. It reveals non-obvious behavior: non-1:1 request ratio (specific counts for fmp_search, fmp_price_target, fmp_quote), cached legs cost nothing, worst-case estimation, and returns budget context. This goes beyond a basic purpose statement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured, starting with the core purpose, followed by usage rationale, an example, and a summary of returns. Every sentence adds value, and the format makes it easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite a single parameter, the tool needs explanation of request ratios, caching, budget, and returns. The description covers all these aspects, and the presence of an output schema reduces the need to describe return format in detail. It's complete for the tool's purpose and usefulness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no field descriptions (0% coverage). The description compensates fully with a precise example of the 'calls' array, including the tool and args structure, making the parameter semantics unambiguous.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'Say what a set of tool calls will cost upstream before running them.' This clearly distinguishes it from data-fetching siblings like fmp_profile or fmp_quote, as it is a cost-estimation meta-tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit context: 'Use this before a multi-step analysis, or whenever a plan touches many symbols.' It also explains caching and request ratios, providing rationale. However, it doesn't explicitly state when not to use it or alternatives, just a clear 'when to use'.
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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