Analyze GraphQL query complexity. Returns depth, field count, list multipliers, and a risk level (LOW/MEDIUM/HIGH/CRITICAL). CRITICAL means the query should be rejected.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | GraphQL query string to analyze |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the return values (depth, field count, list multipliers, risk level) and interprets CRITICAL as 'should be rejected', which is valuable behavioral context. It doesn't mention side effects, but 'Analyze' implies a non-destructive operation, and the return description is sufficient for this simple tool.
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 two sentences, front-loaded with the purpose, then the outputs. Every sentence earns its place, with no filler or redundancy. Ideal size for a single-parameter tool.
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?
For a tool with one parameter, no output schema, and no annotations, the description is complete. It states what it does, what it returns, and the meaning of the risk level. There is no significant missing context.
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 schema already provides a clear description for the sole parameter 'query' (100% coverage), so the description adds little beyond schema. It doesn't elaborate on formatting, size limits, or edge cases, but the baseline of 3 is appropriate when the schema fully documents the parameter.
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 uses a specific verb ('Analyze') and resource ('GraphQL query complexity'), clearly distinguishing this tool from siblings like query_rewriter (which rewrites) and rate_limiter (which rate limits). It also details the specific outputs (depth, field count, list multipliers, risk level).
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 context is clear: this tool analyzes GraphQL query complexity, and the CRITICAL risk level implies rejection decisions. However, it does not explicitly state when to use this vs. alternatives or mention any exclusions, so it lacks the explicit alternatives guidance seen in higher-scoring examples.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct concern: complexity analysis, query rewriting, and rate limiting. There is no overlap in purpose or behavior.
All tool names follow the same snake_case pattern with a noun+verb style (complexity_meter, query_rewriter, rate_limiter). Consistent and predictable.
Three tools cover the core aspects of a GraphQL DoS shield (analysis, mitigation, and client limiting). The count feels appropriate for the narrow scope.
The surface covers the main stages of DoS protection: analyze complexity, rewrite problematic queries, and rate-limit clients. A minor gap is the lack of a manual reset or configuration tool, but the core workflow is complete.