Skip to main content
Glama

query_knowledge_base

Search internal security policies and standards to find compliance requirements and security guidelines using natural language queries.

Instructions

Query the internal security knowledge base (policies, standards).

Args: query: The search query (e.g., "password complexity requirements"). top_k: Number of results to return.

Returns: JSON string with retrieved document chunks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool queries a knowledge base and returns JSON with document chunks, but lacks critical details like authentication requirements, rate limits, error handling, or whether it's read-only (implied but not explicit).

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

Conciseness4/5

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

The description is well-structured with clear sections (Args, Returns) and uses minimal sentences. Each part earns its place, though the 'Returns' section could be slightly more detailed given the lack of annotations.

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

Completeness3/5

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

Given the tool has an output schema (returns JSON string), the description doesn't need to detail return values. However, with no annotations and a sibling tool, it lacks guidance on usage context and behavioral traits. The parameter explanations help, but overall completeness is adequate with clear gaps.

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%, so the description must compensate. It provides meaningful context for both parameters: 'query' is explained with an example ('password complexity requirements'), and 'top_k' specifies it controls the number of results. This adds value beyond the bare schema.

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

Purpose4/5

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

The description clearly states the verb ('Query') and resource ('internal security knowledge base') with specific content scope ('policies, standards'). It distinguishes from the sibling 'assess_document' by focusing on retrieval rather than assessment. However, it doesn't explicitly contrast with the sibling tool.

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

Usage Guidelines2/5

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 the sibling 'assess_document' or any alternatives. It mentions the tool's purpose but offers no context about appropriate use cases, prerequisites, or exclusions.

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/arthurpanhku/DocSentinel'

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