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Wiro MCP Server

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Explore Wiro models

explore
Read-onlyIdempotent

Browse curated AI models by category to find one for generation, editing, or analysis. Select a model, then retrieve its schema via get_model_schema before running it.

Instructions

Browse curated Wiro models grouped by category. Choose a model and call get_model_schema before run_model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoriesYes
nextActionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.2.3
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Changed1 schema field changedv1.2.1
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "categories": {
+      "items": {
+        "additionalProperties": false,
+        "properties": {
+          "models": {
+            "items": {
+              "additionalProperties": false,
+              "properties": {
+                "categories": {
+                  "items": {
+                    "type": "string"
+                  },
+                  "type": "array"
+                },
+                "description": {
+                  "type": "string"
+                },
+                "estimatedCostUsd": {
+                  "type": [
+                    "number",
+                    "null"
+                  ]
+                },
+                "pricing": {
+                  "type": "string"
+                },
+                "slug": {
+                  "type": "string"
+                },
+                "title": {
+                  "type": "string"
+                }
+              },
+              "required": [
+                "slug",
+                "title",
+                "categories"
+              ],
+              "type": "object"
+            },
+            "type": "array"
+          },
+          "title": {
+            "type": "string"
+          },
+          "total": {
+            "minimum": 0,
+            "type": "integer"
+          }
+        },
+        "required": [
+          "title",
+          "total",
+          "models"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "nextAction": {
+      "additionalProperties": false,
+      "properties": {
+        "arguments": {
+          "additionalProperties": {},
+          "type": "object"
+        },
+        "reason": {
+          "type": "string"
+        },
+        "tool": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "tool",
+        "arguments",
+        "reason"
+      ],
+      "type": "object"
+    }
+  },
+  "required": [
+    "categories"
+  ],
+  "type": "object"
+}
  3. First observedv1.1.4

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already cover the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the bar is lower. The description adds the curation/grouping behavior and the workflow context, which is useful but not extensive. No contradiction with annotations; 'Browse' is consistent with the read-only flags.

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?

Two sentences, zero waste. The purpose is front-loaded in the first sentence and the actionable workflow directive sits in the second. Every word earns its place.

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 a parameterless browse tool with an output schema (so return format needs no explanation) and annotations covering the safety profile, the description is nearly complete. The only minor gap is not explicitly distinguishing itself from search_models, but an agent can still call it correctly.

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

Parameters4/5

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

The tool has zero parameters, so the baseline is 4 per the rubric. Schema description coverage is vacuously complete. There is nothing to document, and the description appropriately spends no space on parameters.

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?

The description states a specific verb ('Browse') plus a well-characterized resource ('curated Wiro models grouped by category'). The curation and grouping qualifier is what makes it distinct from the sibling search_models, so an agent can tell them apart 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?

It gives a clear workflow directive: 'Choose a model and call get_model_schema before run_model', which tells the agent what to do after exploring. It doesn't, however, explicitly explain when to reach for explore versus the search_models sibling, so the usage guidance is strong on next-steps but lacks an explicit exclusion.

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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