analyze_text

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 mentions 'estadísticas y patrones' (statistics and patterns), which hints at output behavior, but doesn't specify what statistics (e.g., word count, frequency) or patterns (e.g., regex matches, sentiment) are included. It also omits details like performance characteristics, error handling, or any limitations, leaving significant gaps in understanding how the tool behaves.

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 a single, concise sentence in Spanish: 'Análisis completo de texto con estadísticas y patrones'. It's front-loaded with the core purpose and avoids unnecessary words. However, it could be more structured by explicitly separating the analysis types or adding brief examples, but it's efficient and to the point.

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 complexity of text analysis, no annotations, and no output schema, the description is incomplete. It vaguely mentions statistics and patterns but doesn't detail what these entail, making it insufficient for an AI agent to understand the full context or expected results. For a tool with potential rich output, more explanation is needed to compensate for the lack of structured data.

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, with the parameter 'text' documented as 'Texto a analizar'. The description doesn't add any meaning beyond this, such as constraints on text length, encoding, or language. Since the schema already fully describes the parameter, the baseline score of 3 is appropriate, as no additional value is provided by the description.

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 'Análisis completo de texto con estadísticas y patrones' states the tool performs text analysis with statistics and patterns, which is a clear purpose. However, it's somewhat vague about the specific type of analysis (e.g., sentiment, grammar, keyword extraction) and doesn't distinguish it from sibling tools like 'count_words' or 'split_text' that also process text. The purpose is understandable but lacks specificity for differentiation.

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 no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, such as when to choose 'analyze_text' over 'count_words' for word counting or 'extract_emails' for email extraction. Without such guidance, users must infer usage based on the vague purpose alone.

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