forge-mcp

forge_conversation_evaluate

Evaluate a conversation against a specified metric on demand. Optionally focus on a single interaction or the entire conversation.

Instructions

Run on-demand metric evaluation against a conversation.

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>",
      "metric_name": "<metric_name>"
    },
    "name": "forge_conversation_evaluate"
  }
}

Input Schema

Name	Required	Description
`org_id`	No	Org ID (uses active org if omitted)
`metric_name`	Yes	The metric name to evaluate
`interaction_id`	No	Specific interaction ID (omit for full conversation)
`conversation_id`	Yes	The conversation ID

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.13Jun 11, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries full burden. It implies a read-like operation ('evaluation') but does not disclose whether it is destructive, requires permissions, or has side effects. The minimal disclosure is insufficient.

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 single sentence is very concise and front-loaded. While it lacks details, it avoids unnecessary verbosity. Slightly lower score because it could include key context without losing conciseness.

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?

The description lacks essential context: no mention of output, prerequisites (e.g., metric must exist), behavior with optional interaction_id, or possible metric_name values. Even without output schema, the description should provide more completeness.

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 100%, so baseline is 3. The description adds no extra parameter meaning beyond what the schema already provides. No improvement or degradation.

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 it runs an on-demand metric evaluation against a conversation, using a specific verb and resource. However, it does not differentiate from sibling tools like forge_conversation_insights, which may also analyze conversations.

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?

No guidance is provided on when to use this tool versus alternatives (e.g., forge_conversation_insights), nor are prerequisites or restrictions mentioned. The description only states the function without context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server