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@aiwerk/mcp-server-elevenlabs

@aiwerk/mcp-server-elevenlabs

by AIWerk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

post_conversation_feedback_route

Submit like or dislike feedback for a specific conversation by conversation ID to record user sentiment.

Instructions

Send Conversation Feedback

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": {
      "conversation_id": "<conversation_id>"
    },
    "name": "post_conversation_feedback_route"
  }
}

Input Schema

NameRequiredDescriptionDefault
feedbackNo
conversation_idYesThe id of the conversation you're taking the action on.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

D1.8/5.0
Behavior2/5

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

Annotations already establish the behavior profile (readOnlyHint=false, idempotentHint=false, openWorldHint=true), so the description is not required to repeat them. However, it adds no behavioral context at all: no statement that this mutates an existing conversation's feedback state, whether repeat calls are allowed, or who may submit feedback. It does not contradict the annotations.

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

Conciseness2/5

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

It is short, but the brevity reflects under-specification rather than efficiency — there is no scoping, no alternative routing, and no parameter hints. Four words cannot earn their place when they merely echo the tool title.

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 mutation tool with no output schema, an undocumented enum parameter, and only annotation-level behavioral coverage, the description should explain what is being changed and the meaning of the feedback values. It provides none of this, leaving an agent able to guess only the roughest intent.

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

Parameters2/5

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

Schema coverage is only 50%: conversation_id is documented in the schema, but the feedback parameter (enum like/dislike) has no schema description, and the description supplies nothing about it. The description mentions "feedback" only as part of the tool name, so it fails to compensate for the gap.

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?

"Send Conversation Feedback" restates the tool name almost verbatim rather than stating what sending feedback accomplishes (e.g., rating a conversation as like/dislike). There is no differentiation from siblings such as get_conversation_summary_route or run_conversation_evaluations, so an agent gains nothing beyond the name itself.

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

Usage Guidelines1/5

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

No guidance whatsoever on when to call this tool, what prerequisites exist (e.g., the conversation must already exist), or when an alternative like run_conversation_evaluations would be more appropriate. The agent must guess entirely from the name.

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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