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fastmcp-me

Agent Knowledge MCP

by fastmcp-me

index_document

Index a document into Elasticsearch with duplicate prevention and automatic ID generation.

Instructions

Index a document into Elasticsearch with smart duplicate prevention and intelligent document ID generation. 💡 RECOMMENDED: Use 'create_document_template' tool first to generate a proper document structure and avoid validation errors.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
indexYesName of the Elasticsearch index to store the document
documentYesDocument data to index as JSON object. 💡 RECOMMENDED: Use 'create_document_template' tool first to generate proper document format.
doc_idNoOptional document ID - if not provided, smart ID will be generated
validate_schemaNoWhether to validate document structure for knowledge base format
check_duplicatesNoCheck for existing documents with similar title before indexing
force_indexNoForce indexing even if potential duplicates are found. 💡 TIP: Set to True if content is genuinely new and not in knowledge base to avoid multiple tool calls
use_ai_similarityNoUse AI to analyze content similarity and provide intelligent recommendations

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 burden. It discloses duplicate prevention and AI similarity features but does not discuss side effects like overwrite behavior, asynchronous indexing, or required permissions.

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, front-loaded with the main purpose and a key recommendation. It is concise with no redundant words.

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 7 parameters and an output schema, the description covers the main use case and provides a helpful recommendation. It lacks context on return value or when to use batch vs single indexing, but is otherwise 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?

The input schema has 100% description coverage, with each parameter already well-described. The tool description adds little extra parameter meaning beyond the schema's own descriptions, meriting a baseline score.

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 indexes a document into Elasticsearch with smart duplicate prevention and ID generation. It distinguishes from sibling tools like create_document_template by recommending using that tool first.

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?

Explicitly recommends using create_document_template first to avoid validation errors, providing clear usage sequencing. However, it does not contrast with batch_index_directory for bulk operations.

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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