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@aiwerk/mcp-server-elevenlabs

by AIWerk

query_agent_knowledge_base_rag_route

Retrieve relevant chunks from an ElevenLabs agent's knowledge base RAG index by agent ID and query; optionally use branch or neutral defaults for auditing.

Instructions

Query Agent Knowledge Base Rag

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesQuery to run against the agent's knowledge base RAG index.
agent_idYesThe id of an agent. This is returned on agent creation.
branch_idNoThe ID of the branch to use
use_agent_defaultsNoWhen true (the default), retrieval uses the agent's own RAG settings, reproducing exactly what the agent would retrieve. Set to false to retrieve with neutral default RAG settings instead (the agent's embedding model is always kept, since it determines which vector index exists). Useful for auditing
max_documents_lengthNo
max_retrieved_rag_chunks_countNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.3/5.0
Behavior2/5

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

Annotations declare readOnlyHint=false, openWorldHint=true, idempotentHint=false, but the description supplies none of the context this raises: it never says whether the call has side effects, whether retrieval is deterministic, or whether the agent's config is mutated. Notably the schema's own use_agent_defaults text describes audit behavior that the description omits.

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

Conciseness3/5

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

It is short and front-loaded, but that brevity is under-specification rather than conciseness. As a single verbatim-restatement sentence it wastes no words but conveys almost nothing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-parameter RAG retrieval tool with an unusual use_agent_defaults audit mode and no output schema, the description should explain return shape and the agent-defaults behavior. It explains nothing, leaving the agent reliant entirely on partial schema text.

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 coverage is 67% and the two parameters that lack descriptions, max_documents_length and max_retrieved_rag_chunks_count, are undocumented here as well. The description adds zero parameter meaning, but the baseline for moderate schema coverage is 3.

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

Purpose2/5

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

The description 'Query Agent Knowledge Base Rag' is essentially a restatement of the tool name with no verb-object framing or scope. It does not say what querying returns (retrieved chunks? an answer?), nor how it differs from siblings like search_knowledge_base_content_route or get_knowledge_base_content. An agent cannot distinguish its purpose from the other knowledge-base read tools.

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?

There is no when-to-use guidance at all. The schema's use_agent_defaults parameter hints at an auditing use case, but the description never states when to prefer this tool over search_knowledge_base_content_route or the document/chunk retrieval tools. Nothing steers the agent.

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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