Toolstem MCP Server

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 effectively describes key behavioral traits: it's a read operation ('Get'), returns synthesized data from multiple sources, includes derived signals (e.g., dcf_signal), and provides human-readable formats. However, it lacks details on potential limitations like rate limits, error handling, or data freshness.

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 appropriately sized and front-loaded, starting with the core purpose and key features, followed by usage guidance. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 tool's complexity (synthesizing multiple data sources), no annotations, and the presence of an output schema (which handles return values), the description is complete enough. It covers purpose, key data points, usage context, and behavioral aspects, providing sufficient guidance for effective tool selection and invocation.

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?

The input schema has 100% description coverage, clearly documenting the single parameter 'symbol' with examples. The description does not add any additional meaning or context beyond what the schema provides, such as explaining symbol conventions or validation rules, so it meets the baseline score of 3.

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 a comprehensive stock snapshot') and resources ('for any publicly traded company'), and distinguishes it from siblings by specifying it returns 'curated, agent-ready data synthesized from multiple sources in a single call' for a 'quick overview' rather than detailed financials or comparisons.

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 for when to use this tool ('Use this when you need a quick overview of a stock before digging into financials'), but it does not explicitly state when not to use it or name specific alternatives among the sibling tools (e.g., compare_companies, get_company_metrics, screen_stocks).

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