Skip to main content
Glama

owasp_llm_classify

Map security observations to OWASP LLM Top 10 categories using keyword and regex pattern matching. Returns top matching categories with evidence and confidence scores.

Instructions

Map a finding or observation to OWASP LLM Top 10 (2025) categories.

Pure rule-based: keyword and regex patterns with weights per category. Returns the top top_n matching categories with the matched evidence snippets and a confidence score.

Args: observation: Free-form text describing a finding, scan result, bug report, threat model entry, or security observation. top_n: Number of matches to return (default 3).

Returns: ClassifyReport with ranked matches. If nothing matches, unmatched is True and matches is empty.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNo
observationYes
Behavior4/5

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

With no annotations, the description fully explains the behavioral traits: it is rule-based using keyword and regex patterns with weights, and returns top matches with evidence snippets and confidence. It also clarifies the return format when no matches are found.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (9 lines) and front-loaded with the core purpose. Every sentence adds value, with no redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately explains the return type (ClassifyReport with matches and unmatched flag). It covers input constraints and expected output, though additional details on confidence scoring could be useful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description compensates by explaining both parameters: 'observation' as free-form text describing findings, and 'top_n' as the number of matches with a default of 3. This adds meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool maps findings to OWASP LLM Top 10 categories, with a specific verb and resource. It distinguishes itself from sibling tools, which are unrelated security audit tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool (mapping observations to OWASP categories) and lists example inputs. It does not explicitly state when not to use it, but the sibling tools cover different tasks, making the intent clear.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/actions-marketplace-validations/x0base_mcp-security-toolkit'

If you have feedback or need assistance with the MCP directory API, please join our Discord server