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apatoliya

MCP-RAG Server

by apatoliya

ingest_documents

Add documents from a local file to the knowledge base for semantic search and retrieval in RAG systems.

Instructions

Ingest documents from a local file into the knowledge base.
Args:
    local_file_path: The path to the local file containing the documents to ingest.
Returns:
    str: A message indicating the documents have been ingested.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
local_file_pathYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('ingest') and return value, but lacks details on permissions, side effects (e.g., overwriting), rate limits, or error handling. This is inadequate for a mutation tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and concise, with a clear purpose statement followed by Args and Returns sections. Each sentence adds value, though the return description could be more specific (e.g., success/failure indicators).

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 tool's complexity (a mutation with no annotations or output schema), the description is minimally adequate. It covers purpose and parameters but lacks behavioral details and output specifics. The absence of annotations increases the burden, leaving gaps in understanding the tool's full behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful context for the single parameter 'local_file_path' by explaining it's 'the path to the local file containing the documents to ingest.' With schema description coverage at 0%, this compensates well, though it doesn't specify format constraints (e.g., absolute vs. relative paths).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Ingest documents from a local file into the knowledge base.' It specifies the verb ('ingest'), resource ('documents'), and source ('local file'), but doesn't explicitly differentiate from sibling tools like 'process_search_query' or 'search_doc_for_rag_context', which appear to be for querying rather than ingestion.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., file format requirements), exclusions, or compare it to sibling tools. The agent must infer usage from the purpose alone.

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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