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klever-io
by klever-io

add_context

Add knowledge entries like code examples and best practices to the Klever VM context store for later retrieval via query_context or search_documentation.

Instructions

Add a new knowledge entry to the Klever VM context store. Use this to save code examples, best practices, security tips, or documentation that can later be retrieved via query_context or search_documentation. Returns the generated ID of the new entry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesThe category of this knowledge entry. Choose the most specific type: "code_example" for Rust snippets, "best_practice" for recommended patterns, "security_tip" for vulnerability guidance, "error_pattern" for known error solutions.
contentYesThe main content body — typically Rust source code, a CLI command, or a detailed explanation. For code, include the full working snippet.
metadataYesEntry metadata including title, tags, and categorization. At minimum, provide a title.
Behavior4/5

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

Annotations (readOnlyHint=false) indicate write operation; description confirms it adds an entry and returns an ID. Adds context about storage for later retrieval. Does not contradict annotations. Could mention potential limits or failure modes, but sufficient.

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?

Two concise sentences: first states action and resource, second gives usage context and return value. No fluff, well-structured.

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?

Covers purpose, usage, return value, and param details via schema. Lacks error handling or storage constraints, but for a simple add operation with complete schema, it's nearly complete.

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

Parameters3/5

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

Schema description coverage is 100% with detailed param docs (type enum, metadata nested fields). Description adds only return value info. Baseline score of 3 applies as schema handles semantics adequately.

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?

Description clearly states the verb ('add'), resource ('knowledge entry to Klever VM context store'), and purpose ('save code examples, best practices...'). Differentiates from sibling tools like query_context and search_documentation by noting retrieval. No tautology.

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?

Provides explicit use cases (saving various knowledge types) and hints at alternatives for retrieval. Lacks explicit when-not-to-use or distinction from siblings like add_helper_scripts, but context is clear enough.

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

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