cash_flowFinancial Modeling Prep cash-flow statement for a US-listed ticker: operating, investing, financing activities, free cash flow, capex, net change in cash. Annual (period=annual) or quarterly. Use for fundamental analysis, DCF inputs, cash-flow valuation.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| period | No | ||
| symbol | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and idempotentHint annotations, the description adds valuable behavioral context: it specifies the data source (Financial Modeling Prep), restricts to US-listed tickers, names the available periods (annual/quarterly), and enumerates the exact data fields returned. This gives agents a rich understanding of the tool's scope and output without needing to invoke it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact, front-loads the core purpose, and delivers detail efficiently. It avoids redundant phrasing and every sentence contributes meaningful information, making it highly scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the schema (3 params, no output schema), the description covers the main purpose, key data fields, period options, and use cases. It omits clarification of the 'limit' parameter, but overall it is sufficiently complete for an agent to select and invoke the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero schema description coverage, the description compensates for two of three parameters: 'symbol' is implicitly defined as a US-listed ticker, and 'period' is explicitly stated to accept annual or quarterly. However, 'limit' remains unexplained, leaving a gap in parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a cash-flow statement from Financial Modeling Prep, enumerates specific financial metrics (operating, investing, financing activities, free cash flow, capex, net change in cash), and notes it targets US-listed tickers. This specificity distinguishes it from sibling tools like balance_sheet and income_statement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit use cases ('Use for fundamental analysis, DCF inputs, cash-flow valuation'), giving clear contexts for when to apply the tool. However, it does not explicitly mention alternatives or exclusions, so it misses the 'when-not-to-use' aspect of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools overlap in purpose: quote/quote_short/historical_price/intraday for price data; balance_sheet/income_statement/cash_flow for financials; search_symbol/search_name/discover_tools for lookup; and a cluster of Pipeworx routers (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim) with unclear boundaries. Agents will frequently select the wrong tool.
All tool names use consistent snake_case, but naming conventions vary widely: noun phrases (balance_sheet, entity_profile), bare verbs (forget, subscribe), verb+noun (compare_entities, resolve_entity), and adjective+noun (historical_price, recent_alerts). No single pattern dominates, making it harder to guess tool names.
55 tools is excessive for a server labeled 'Fmp'. The core financial data tools are perhaps 20-25, while the rest are unrelated: memory utilities, prediction market analyzers, web scraping, and meta-routing tools. This bloated set dilutes the server's purpose and burdens the agent with irrelevant options.
For the declared domain (FMP financials), the set covers the main statements but lacks tools like segment data, insider trades (listed as paid), or ownership details (also paid). Conversely, it includes many tools for prediction markets and general data retrieval that don't belong here, creating a mismatch between server name and actual capability.