FMP MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide excellent behavioral context (read-only, open-world, idempotent, non-destructive). The description adds valuable operational context by specifying what data is returned (current price, 52-week range, SMA indicators, performance metrics, volatility, daily closes) and clarifying the scope ('Not for raw chart data'), which helps the agent understand the tool's output format and limitations.

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

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

Perfectly structured with purpose first, output clarification second, parameter documentation third. Every sentence earns its place: the first defines scope, the second clarifies exclusions, the third documents parameters efficiently. No wasted words, excellent front-loading of critical information.

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

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

Given the rich annotations (read-only, open-world, idempotent), comprehensive parameter documentation in the description, and the presence of an output schema, this description provides complete context. It covers purpose, exclusions, parameters, and output characteristics without needing to duplicate what's in structured fields.

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

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

With 0% schema description coverage, the description fully compensates by clearly explaining both parameters: 'symbol' as a stock ticker with an example, and 'period' with all valid values and default. This provides complete semantic understanding beyond the bare schema, though it doesn't explain parameter interactions or constraints.

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

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

The description clearly states the tool's purpose with specific verbs ('Get price performance, key levels, and momentum indicators') and distinguishes it from siblings by explicitly stating what it does NOT do ('Not for raw chart data'). It identifies the resource (stock price data) and differentiates from chart-focused tools.

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

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

The description provides clear context about when to use this tool ('Get price performance, key levels, and momentum indicators') and explicitly excludes raw chart data usage. However, it doesn't specify when to choose this over specific sibling tools like 'market_overview' or 'stock_brief' that might overlap in financial data coverage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.