karte-datahub-mcp

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 and excels. It reveals critical behavioral traits: guardrails on SQL execution, mandatory constraints for specific tables, automatic LIMIT application (1000) by default, rejection of destructive SQL operations, and the dry_run option for scanning only. This provides comprehensive insight into how the tool behaves beyond basic execution.

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: it starts with the core purpose, followed by key behavioral rules, and ends with a structured Args section. Every sentence earns its place by providing critical information 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 complexity of a SQL execution tool with 3 parameters, 0% schema coverage, no annotations, but an output schema present, the description is complete enough. It covers purpose, usage rules, behavioral constraints, and parameter semantics thoroughly. The output schema handles return values, so the description doesn't need to explain them, making this a well-rounded specification.

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 0%, so the description must compensate fully, which it does. The Args section explicitly explains each parameter's semantics: 'sql' is the SQL statement to execute, 'dry_run' confirms scan volume only when True, and 'no_limit' skips automatic LIMIT addition when True. This adds essential meaning beyond the bare schema, clarifying how parameters affect tool behavior.

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: 'カスタムSQLを実行する（ガードレール付き）' (Execute custom SQL with guardrails). It specifies the verb (execute) and resource (custom SQL) and distinguishes it from sibling tools like count_karte_events, describe_karte_events_schema, and query_karte_events by focusing on raw SQL execution rather than predefined queries or metadata operations.

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 explicit usage guidelines: it states when to use specific constraints ('krt_pockyevent_v1_* テーブルへのクエリには _TABLE_SUFFIX 制約が必須' - queries to krt_pockyevent_v1_* tables require _TABLE_SUFFIX constraint), when alternatives apply (automatic LIMIT 1000 unless no_limit is True), and exclusions ('破壊的SQL（DROP, DELETE等）は拒否される' - destructive SQL like DROP, DELETE is rejected). This clearly guides when and how to use this tool versus other approaches.

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