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MCP Server for Splunk

MCP Server for Splunk

by deslicer
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get Kvstore Data

get_kvstore_data

Retrieve documents from a KV Store collection using MongoDB-style query filters to fetch lookup or configuration data and narrow results by field values.

Instructions

Get documents from a KV Store collection with optional MongoDB-style query filtering. Use this to fetch lookup/configuration data or narrow results by field values.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "collection": "<collection>"
    },
    "name": "get_kvstore_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
appNoApp where the collection resides (defaults to current/app context)
queryNoMongoDB-style filter object (e.g., {"status": "active"})
collectionYesCollection name

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.6.1
    • addedInput schema / properties / app / description
      Added value: +"App where the collection resides (defaults to current/app context)"
    • addedInput schema / properties / collection / description
      Added value: +"Collection name"
    • addedInput schema / properties / query / description
      Added value: +"MongoDB-style filter object (e.g., {\"status\": \"active\"})"
  2. Changed1 schema field changedv0.5.1
    • addedInput schema / additionalProperties
      Added value: +false
  3. First observedv0.5.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. 'Get documents' and 'fetch' imply a read-only operation, and optional query filtering is disclosed, but there is no mention of default limits, pagination, empty-result behavior, or other side effects.

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 concise sentences: the first front-loads the core operation, and the second adds practical usage context. No unnecessary detail or repetition.

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 simple parameter set and complete schema descriptions, the description is reasonably adequate for invocation. However, there is no output schema and no mention of return shape, document limits, or error behavior, leaving moderate uncertainty for an agent.

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 input schema already provides 100% parameter coverage with descriptions for collection, app defaulting to context, and the query example. The description mostly restates the MongoDB-style query filtering and adds the 'narrow results by field values' use case, adding minimal semantic value beyond the schema.

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 says 'Get documents from a KV Store collection' with optional 'MongoDB-style query filtering', clearly identifying the operation, resource, and scope. It differentiates from sibling tools like list_kvstore_collections by focusing on retrieval of documents rather than collection listing.

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 explicitly says 'Use this to fetch lookup/configuration data or narrow results by field values', providing clear context for when to invoke the tool. It does not explicitly name alternatives to avoid, so it falls short of a full when-to-use vs when-not-to-use guide.

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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