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imbenrabi

Financial Modeling Prep MCP Server

getTEMA

Calculate the Triple Exponential Moving Average (TEMA) for stock trend analysis. Use this tool to identify potential buy or sell signals based on historical price data.

Instructions

Calculate the Triple Exponential Moving Average (TEMA) for a stock using the FMP TEMA API. This tool helps users analyze trends and identify potential buy or sell signals based on historical price data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesStock symbol
periodLengthYesPeriod length for the indicator
timeframeYesTimeframe (1min, 5min, 15min, 30min, 1hour, 4hour, 1day)
fromNoStart date (YYYY-MM-DD)
toNoEnd date (YYYY-MM-DD)
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 tool helps analyze trends and identify signals, but does not disclose behavioral traits such as rate limits, authentication needs, data freshness, error handling, or output format. This is a significant gap for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose and followed by a usage hint, with no wasted words. However, it could be slightly more structured by explicitly listing key parameters or constraints, but it remains efficient overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and a tool with 5 parameters, the description is incomplete. It lacks details on behavioral aspects, output format, error cases, and does not compensate for the missing structured data, making it inadequate for full agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters (symbol, periodLength, timeframe, from, to) with descriptions. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints, meeting the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool calculates the Triple Exponential Moving Average (TEMA) for a stock using a specific API, which is a specific verb+resource combination. However, it does not explicitly differentiate from sibling tools like getDEMA, getEMA, getSMA, getWMA, or getWilliams, which are all technical indicators, so it misses full sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for analyzing trends and identifying buy/sell signals based on historical price data, providing some context. However, it does not explicitly state when to use this tool versus alternatives (e.g., other moving average tools in the sibling list) or any exclusions, leaving usage guidance incomplete.

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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