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Wiro MCP Server

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TypeScript
Remote

Recommend Wiro models

recommend_model
Read-onlyIdempotent

Describe your task and receive ranked AI model recommendations, then follow the returned schema to run the best match.

Instructions

Describe a generation goal in natural language and receive ranked model recommendations. Follow the returned get_model_schema next action before running a model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesWhat you want to do, e.g. "generate a photorealistic portrait", "upscale an image to 4K", "transcribe audio to text".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitYes
startYes
totalYes
modelsYes
nextStartYes
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 / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Changed2 schema fields changedv1.2.1
    • changedInput schema / properties / task / description
      Previous value: -"What you want to do, e.g. \"generate a photorealistic portrait\", \"upscale an image to 4K\", \"transcribe audio to text\""New value: +"What you want to do, e.g. \"generate a photorealistic portrait\", \"upscale an image to 4K\", \"transcribe audio to text\"."
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "limit": {
+      "exclusiveMinimum": 0,
+      "type": "integer"
+    },
+    "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"
+    },
+    "nextAction": {
+      "additionalProperties": false,
+      "properties": {
+        "arguments": {
+          "additionalProperties": {},
+          "type": "object"
+        },
+        "reason": {
+          "type": "string"
+        },
+        "tool": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "tool",
+        "arguments",
+        "reason"
+      ],
+      "type": "object"
+    },
+    "nextStart": {
+      "anyOf": [
+        {
+          "minimum": 0,
+          "type": "integer"
+        },
+        {
+          "type": "null"
+        }
+      ]
+    },
+    "start": {
+      "minimum": 0,
+      "type": "integer"
+    },
+    "total": {
+      "minimum": 0,
+      "type": "integer"
+    }
+  },
+  "required": [
+    "models",
+    "total",
+    "start",
+    "limit",
+    "nextStart"
+  ],
+  "type": "object"
+}
  3. First observedv1.1.4

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. Beyond those, the description adds the critical behavioral detail that the tool returns a `get_model_schema` next action, guiding the agent's workflow. This enriches the annotation coverage, though not exhaustively.

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, highly efficient. The main purpose is front-loaded, and the follow-up action is stated crisply. No wasted words.

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?

With an output schema present, the description need not detail return values. It explains the purpose and the immediate next step. It lacks explicit mention of when to avoid this tool (e.g., when a specific model name is known), but that is a usage-guideline gap rather than a completeness gap. Overall adequate for a recommendation tool.

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?

The schema covers the single 'task' parameter with a clear description and example. The tool description reiterates 'natural language' but does not add significant new meaning beyond the schema. Baseline 3 is appropriate given full schema coverage.

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 ('recommend') and resource ('models') based on a natural language goal, clearly distinguishing it from siblings like search_models or get_model_schema. It explains the purpose without ambiguity.

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

Usage Guidelines3/5

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

The description implies usage timing ('before running a model') and mentions the next action, but does not explicitly contrast with alternatives like search_models or state when NOT to use this tool. The guidance is present but minimal.

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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