Skip to main content
Glama

ZeroWidth

by ai.zerowidth
OverviewDiscussionsAdmin

Add items to a Caliper dataset

caliper_datasets_add_items

Appends items to an existing dataset — use this to grow coverage (new edge cases, scenarios from a completed interview) instead of creating a parallel dataset. Same four shapes as caliper_datasets_create (Q&A, chat, sequence, simulated). Existing items and their ratings are untouched.

Input Schema

NameRequiredDescriptionDefault
itemsYesItems to append (1-100).
datasetIdYesId of the dataset to extend.
workspaceNoWorkspace slug. Personal tokens with no default workspace MUST pass this; tokens with a default can override per call. Ignored for workspace API keys.
approvalIdNoApproval id from a prior needs_confirmation response. Omit on the first call.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / items / items / properties / expectedResponse / description
      Previous value: -"Sequence: the standard for the FINAL reply."New value: +"Sequence: the standard for the FINAL reply. Chat: the standard for the next reply."
    • addedInput schema / properties / items / items / properties / messages
      Added value: +{
+  "description": "Chat: the conversation so far as role/content messages (user and assistant alternating, an optional system first), 1-50. Every assistant message here is FIXED, written by you, not the model; the flow under test writes only the NEXT reply. End on a user message and put the standard for that reply in expectedResponse. This is the shape for testing the 5th reply of a conversation with the first four baked in.",
+  "items": {
+    "properties": {
+      "content": {
+        "type": "string"
+      },
+      "role": {
+        "enum": [
+          "system",
+          "user",
+          "assistant"
+        ],
+        "type": "string"
+      }
+    },
+    "required": [
+      "role",
+      "content"
+    ],
+    "type": "object"
+  },
+  "type": "array"
+}
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations declare a non-read-only, non-destructive write. The description adds real value beyond them: appends are additive, existing items and their ratings are untouched, and the accepted item shapes ('same four shapes as caliper_datasets_create') are named. It doesn't state auth/approval behavior, though the schema's approvalId covers that.

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?

Three short sentences with the core action and its key routing decision front-loaded. Every clause earns its place — the shape reference and the non-destructiveness note both carry information an agent needs.

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 an append mutation with 4 params and no output schema, the definition covers action, alternatives, additivity, and item shapes. The only gap is what a successful call returns (e.g., new item ids), which the absence of an output schema leaves unaddressed.

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 item-shape fields, datasetId, workspace and approvalId are already fully documented in structured data. The description's 'same four shapes' hint adds orientation but no per-parameter syntax or constraints beyond the schema. Baseline 3 applies.

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

Purpose5/5

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

States a precise verb+resource ('appends items to an existing dataset') and immediately distinguishes it from the parallel-dataset path (caliper_datasets_create). An agent can tell it apart from siblings like generate_items or update_item without opening the schema.

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

Usage Guidelines4/5

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

Gives a clear when-to-use rationale ('grow coverage — new edge cases, scenarios from a completed interview') and an implicit alternative via 'instead of creating a parallel dataset'. It does not, however, address other plausible routes such as caliper_datasets_generate_items or update_item, so the routing is not fully exhaustive.

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

All ZeroWidth toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources