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Ingest a document for RAG

file_ingest

Add a text document to the authenticated user's knowledge base. The content is chunked, embedded, and stored so it becomes retrievable via rag_search.

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": {
      "name": "<name>",
      "content": "<content>"
    },
    "name": "file_ingest"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesA human-readable name/title for the document.
contentYesThe text content to ingest. It is chunked, embedded, and made retrievable via rag_search.
group_keyNoOptional group key to scope the document. Pass the same value to rag_search to retrieve only this group; omit it to search the whole corpus.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare the mutation profile (readOnly=false, idempotent=false, destructive=false, openWorld=false), so the bar is lower. The description usefully adds the processing pipeline (chunking/embedding) and the user-scoping of the target corpus, but it omits the consequence of idempotentHint=false — whether re-ingesting the same name duplicates or overwrites a document — which is the most decision-relevant unknown here.

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?

Two sentences, zero waste, with the action and its purpose front-loaded before the retrieval consequence. Nothing is repeated or padded.

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?

For a 3-parameter tool with full schema coverage and annotations covering safety, the description is nearly sufficient; no output schema exists so return values need not be explained. The remaining gap is the non-idempotent behavior (duplicate handling) and any content-size limits, which an agent calling a write tool would want before ingesting.

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 all three parameters including the group_key/rag_search pairing are already documented in the schema; baseline 3 applies. The description adds no parameter-level meaning beyond restating that content is chunked and embedded.

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?

States a specific verb and resource ('Add a text document to the authenticated user's knowledge base') plus the downstream effect (chunked, embedded, retrievable via rag_search). This cleanly separates it from the read-side rag_search/rag_similar siblings, but it never distinguishes itself from memory_add, the other write-to-knowledge sibling.

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

Usage Guidelines3/5

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

Usage is only implied by the causal chain 'ingest now so rag_search can find it later'; there is no explicit when-to-use statement, no prerequisites, and no guidance on choosing this over memory_add. The agent must infer the ingest-then-search workflow from the mention of rag_search.

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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