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mcp-klever-vm

by io.github.klever-io

enhance_with_context

Read-onlyIdempotent

Augment a natural-language query with relevant Klever VM knowledge base context. Extracts Klever-specific keywords, finds matching entries, and returns the original query combined with relevant code examples and documentation in markdown. Use this to enrich a user prompt before answering Klever development questions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe user's natural-language question or prompt to enhance (e.g. "How do I handle KLV payments in my contract?").
autoIncludeNoWhen true (default), automatically appends the most relevant knowledge base entries to the response. Set to false to only return metadata without injecting context.

TDQS

A4.2/5.0
Behavior4/5

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

The description goes beyond the annotations by explaining the internal process (keyword extraction, matching entries) and the output format (markdown combining original query with examples/documentation). Since annotations already declare read-only, idempotent, and non-destructive behavior, this added context is valuable and does not contradict them.

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 two sentences long, immediately states the core purpose, and includes a practical usage hint. Every sentence contributes meaning without redundancy.

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 the tool has full schema coverage and no output schema, the description sufficiently conveys the output format (markdown), the processing steps, and the intended use case. It does not over-explain, and the provided context is adequate for an agent to select and invoke the tool correctly.

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%, so both parameters ('query' and 'autoInclude') are already well-documented in the schema. The description reiterates the behavior (combining original query with context) but adds no new parameter-specific semantics beyond what the schema provides.

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 a specific action ('Augment a natural-language query') and a specific resource ('with relevant Klever VM knowledge base context'). It further elaborates on the process (extracting keywords, finding entries, returning combined markdown) and distinguishes itself from sibling search tools by focusing on enriching a prompt for downstream use.

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 a clear usage context: 'Use this to enrich a user prompt before answering Klever development questions.' This implies a distinct stage (pre-processing) compared to sibling tools like query_context or search_documentation, but it does not explicitly mention when not to use it or name alternative tools.

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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TDQS

A4.2/5.0
Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count4/5

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.

Completeness5/5

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.

Resources