Yargı MCP

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

Annotations cover read-only, open-world, and idempotent traits. The description adds valuable context beyond annotations: it specifies the return format ('ChatGPT Deep Research compatible format'), search syntax support (+, -, quotes, OR), and language requirement ('Turkish keywords for best results'), enhancing behavioral understanding without contradiction.

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 front-loaded with key information (purpose and context), uses efficient sentences, and avoids redundancy. Every sentence adds value, such as specifying the return format and search syntax, making it appropriately sized and well-structured.

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 (search across multiple databases), rich annotations (readOnlyHint, openWorldHint, idempotentHint), and the presence of an output schema, the description is complete. It covers purpose, usage context, behavioral details (format, syntax, language), and compensates well for any gaps.

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?

Schema description coverage is 100% for the single parameter 'query', which is documented as 'Turkish search query'. The description adds minimal semantics by reiterating 'Turkish search query' and mentioning 'Turkish keywords for best results', but does not provide significant additional meaning beyond the schema.

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 ('search across all Turkish legal databases') and resources ('Turkish legal databases'), distinguishing it from siblings by emphasizing its cross-database scope versus sibling tools that target specific databases (e.g., search_anayasa_unified).

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?

It provides explicit usage guidance: 'Only for ChatGPT Deep Research' sets the context, 'Use this when searching across all Turkish legal databases in a single query' specifies when to use it, and the list of sibling tools implies alternatives for targeted searches, though not explicitly named.

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