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forge-mcp

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forge_metric_create

Create a metric to evaluate conversations by defining name, description, services, tags, and metric value type (boolean, numerical, or categorical).

Instructions

Create a new metric for evaluating conversations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesUnique metric name within the organization
tagsYesTags (alphanumeric keys and values, no hyphens or special characters)
org_idNoOrg ID (uses active org if omitted)
descriptionYesDescription of the metric
metric_valueYesMetric value definition. Must include "type" discriminator: "boolean", "numerical", or "categorical"
additional_notesYesAdditional notes about the metric (null if none)
applied_to_servicesYesService IDs this metric applies to

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.1.14
    • removedInput schema / properties / additional_notes / anyOf
      Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
    • addedInput schema / properties / additional_notes / type
      Added value: +[
+  "string",
+  "null"
+]
  2. First observedv0.1.13

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full behavioral burden, but it only says 'Create a new metric'. It does not disclose side effects, uniqueness conflict behavior, response format, or any other operational traits beyond the obvious creation action.

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 a single front-loaded sentence with no wasted words. It is concise, though it could be slightly more structured by including a sibling reference or high-level behavior note.

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?

Despite rich parameter schemas, this is a create operation with no output schema and no annotations. The description omits return value expectations, duplicate-name behavior, and any post-creation effects, leaving an agent without important operational context.

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 the schema already documents all seven parameters thoroughly. The description adds no parameter-level meaning, but it does not need to compensate for missing schema information, landing at the baseline.

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 a specific action ('Create') and resource ('a new metric'), with the domain context 'for evaluating conversations'. It is unambiguous among the sibling tools, though it does not explicitly differentiate itself from forge_metric_update.

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 given for when to use this tool versus alternatives such as forge_metric_update or forge_entity_create. There is no mention of prerequisites, preferred conditions, or exclusions, so an agent must infer usage from the name 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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