MCP Presidio

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 the tool analyzes for PII detection and returns JSON results, but lacks details on permissions, rate limits, error handling, or what 'analyze' entails (e.g., detection only, no modification). For a batch processing tool with zero annotation coverage, this is insufficient to inform safe and effective use.

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: the first sentence states the purpose clearly, followed by structured sections for 'Args' and 'Returns' that are easy to parse. Every sentence adds value without redundancy, making it efficient and well-organized.

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 moderate complexity (batch analysis with 4 parameters), no annotations, and an output schema present (implied by 'Returns' note), the description is reasonably complete. It covers purpose, parameters, and return format, but lacks behavioral context like error handling or performance considerations, which would be beneficial for a batch tool. The output schema reduces the need to detail return values.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'texts' as 'List of texts to analyze', 'language' as 'Language code', 'entities' as 'List of entity types to detect', and 'score_threshold' as 'Minimum confidence score', including defaults. This compensates well for the schema's lack of descriptions, though it doesn't detail entity types or language code formats.

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: 'Analyze multiple texts in batch for PII detection.' It specifies the verb ('analyze'), resource ('multiple texts'), and domain ('PII detection'), which is specific and informative. However, it doesn't explicitly differentiate from sibling tools like 'analyze_text' or 'batch_anonymize', which would require mentioning batch vs single or analysis vs anonymization.

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 sibling tools like 'analyze_text' (for single texts) or 'batch_anonymize' (for batch anonymization), nor does it specify prerequisites, exclusions, or optimal use cases. Usage is implied by the name and purpose but not explicitly stated.

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