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Kirachon

Context Engine MCP Server

by Kirachon

semantic_search

Find relevant code snippets by searching your codebase with natural language queries to locate functions, classes, or implementations.

Instructions

Perform semantic search across the codebase to find relevant code snippets.

Use this tool when you need to:

  • Find specific functions, classes, or implementations

  • Locate code that handles a particular concept

  • Quickly explore what exists in the codebase

For comprehensive context with file summaries and related files, use get_context_for_prompt instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language description of what you're looking for (e.g., "user authentication", "database connection", "API error handling")
top_kNoNumber of results to return (default: 10, max: 50)
modeNoSearch mode: "fast" (default) uses cached results and moderate expansion; "deep" increases expansion/budget for better recall at higher latency.fast
bypass_cacheNoWhen true, bypass caches for this call (useful for benchmarking or ensuring freshest results).
timeout_msNoMax time to spend on the retrieval pipeline in milliseconds. 0/undefined means no timeout.
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'quickly explore' (implying speed) and references 'cached results' in the parameter descriptions, but doesn't explicitly discuss rate limits, authentication needs, error conditions, or what the return format looks like. It adds some behavioral context but leaves significant gaps for a tool with 5 parameters and no output schema.

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 perfectly structured and concise - a clear purpose statement followed by bulleted usage guidelines and an explicit alternative tool mention. Every sentence earns its place, with no wasted words or redundant information.

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's complexity (semantic search with 5 parameters), no annotations, and no output schema, the description does a good job but has some gaps. It clearly explains purpose and usage, but doesn't describe what the search results look like or potential limitations. The parameter schema is well-documented, but the description could better compensate for the lack of output schema and annotations.

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?

The schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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's purpose with a specific verb ('perform semantic search') and resource ('across the codebase'), distinguishing it from siblings like 'get_context_for_prompt' which provides comprehensive context. It explicitly differentiates from that sibling tool, making the purpose unambiguous.

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

Usage Guidelines5/5

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

The description provides explicit usage guidelines with bullet points listing specific scenarios (e.g., 'Find specific functions', 'Locate code that handles a particular concept') and explicitly names an alternative tool ('get_context_for_prompt') for when comprehensive context is needed. This gives clear when-to-use and when-not-to-use guidance.

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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