Retrieve a single knowledge base entry by its unique ID. Returns the full entry including content, metadata, tags, and related context IDs. Use this after query_context or find_similar to get complete details for a specific entry.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The unique context ID (UUID format). Obtain IDs from query_context or find_similar results. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds return format details (content, metadata, tags, related context IDs), which goes beyond the annotations. No contradictions.
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 action verb and resource. Every sentence adds value: purpose, return content, and usage context.
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 simple retrieval tool with one parameter and no output schema, the description sufficiently covers purpose, return content, and usage context. The annotations cover the safety profile, leaving no significant gaps.
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 input schema fully documents the 'id' parameter with UUID format and source. The description's 'unique ID' adds no new meaning beyond the schema, so baseline 3 is appropriate given 100% schema description coverage.
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 'Retrieve a single knowledge base entry by its unique ID,' which is a specific verb+resource. It distinguishes from sibling tools by explaining that it returns full details and is intended for use after query_context or find_similar.
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 explicitly says 'Use this after query_context or find_similar to get complete details for a specific entry,' providing clear when-to-use guidance. It implies this is a follow-up retrieval step rather than a search tool, though it doesn't list explicit when-not-to-use scenarios.
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.
Most tools have clearly distinct purposes, such as get_balance for token balances, analyze_contract for code analysis, and init_klever_project for project scaffolding. However, there is some overlap between query_context and search_documentation, both of which search the knowledge base, which could cause confusion about which to use for specific queries.
The naming follows a consistent verb_noun pattern throughout, such as get_balance, analyze_contract, and init_klever_project. Minor deviations exist, like add_helper_scripts (verb_adjective_noun) and enhance_with_context (verb_preposition_noun), but overall, the pattern is clear and predictable.
With 16 tools, the count is slightly high but reasonable for the Klever VM domain, which covers blockchain queries, smart contract development, and knowledge base management. It provides comprehensive coverage without being overwhelmingly large, though it could be streamlined by merging overlapping tools.
The tool set offers complete coverage for Klever VM development, including project setup (init_klever_project, add_helper_scripts), contract analysis and querying (analyze_contract, query_sc), blockchain data retrieval (get_balance, get_transaction, get_block), and knowledge base access (query_context, search_documentation). No obvious gaps are present for the intended scope.
