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LeoWorks Review & Lead Data Tools (via Apify MCP)

leoworks/spanish-review-classifier

leoworks--spanish-review-classifier
Destructive

This tool calls the Actor "leoworks/spanish-review-classifier" and retrieves its output results. Actor description: Classify Spanish reviews (MercadoLibre reseñas) into complaint types, sentiment and purchase motive — Spanish review sentiment analysis. Beta. Use for Spanish; for Korean use leoworks/korean-review-classifier. datasetId (text auto-detected) or texts. ~100 reviews/5 s, pay per review.

Input Schema

NameRequiredDescriptionDefault
textsNoPaste review texts directly (one per line). Use this OR “Reviews dataset”. Example values: ["El paquete llegó aplastado y uno de los auriculares no funciona.","Siempre lo compro, excelente calidad. ¡Lo volvería a comprar!"]
idFieldsNoFields copied unchanged from each input item to the output so you can join results back (e.g. reviewId, productId). Leave empty to auto-pick reviewId/id/url.
maxItemsNoClassify at most this many reviews (0 = all).
waitSecsNoMax seconds (0–45, default 30) to cap the wait for the Actor run to reach terminal state. For long-running Actors the response returns at the cap with the current run status; follow `nextStep` to poll via get-actor-run. Set to 0 to fire-and-forget.
datasetIdNoAn Apify dataset containing reviews — for example the output of a MercadoLibre review scraper. Use this OR “Review texts”.
textFieldNoField holding the review text. Leave empty to auto-detect (content, text, review, body, reviewText, …).
outputModeNoMinimal mode returns only label keys — smaller and easier to aggregate. Example values: "full"full
healthCheckNoInternal: fail the run when results look degraded (used by the developer's scheduled checks).
customLabelsNoUp to 10 extra yes/no labels (each up to 200 characters) written in plain language (English or Spanish), e.g. “mentions the smell”, “menciona el olor”. Each gets a probability.
maxConcurrencyNoReviews classified in parallel. Example values: 10
extraTextFieldsNoFields to prepend to the text (up to 5), e.g. `title` for Coupang review headlines. Leave empty if unsure.
summaryGroupFieldNoField in your dataset that identifies the product (e.g. `productId`, `productName`, `placeId`). The run then saves a REPORT record with, per product: complaint rate and complaint mix, sentiment shares, purchase motives, average rating and 3 example complaints. Leave empty to detect it automatically (overall summary only if none is found). No extra charge.
complaintThresholdNoMinimum probability (0–1) for a complaint type or custom label to be reported. Raise it for fewer, surer labels. Example values: 0.5

Output Schema

NameRequiredDescriptionDefault
tipNoAdvisory guidance RAG Web Browser wrote to its key-value store under the reserved "TIP" key
runIdYesActor run ID
statsNoRun statistics
statusYesRun status: READY | RUNNING | TIMING-OUT | TIMED-OUT | ABORTING | ABORTED | SUCCEEDED | FAILED
actorIdYesStable Apify Actor ID from the run record
summaryYesPast-tense summary of the run state
exitCodeNoActor process exit code; populated for terminal states (especially FAILED)
nextStepYesOne primary follow-up action with identifiers interpolated
storagesYesDataset and key-value store metadata, keyed by alias. "default" is always the primary entry.
actorNameNo"username/actor-name"
startedAtNoISO timestamp when the run started
finishedAtNoISO timestamp when the run finished (terminal states only)
statusMessageNoPass-through from Apify run.statusMessage
apifyConsoleUrlNoPersonalized Apify Console link to the run; present only for Console sessions

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedOutput schema / properties / storages / properties / datasets / properties / default / properties / itemsSchema
      Added value: +{
      +  "description": "JSON Schema for rows in the dataset at `storages.datasets.default.id` — describes row shape only; the rows themselves are NOT returned inline in this response. Inferred from this Actor's historical successful runs. To fetch actual rows, call `get-dataset-items` with the dataset id and a `fields` projection drawn from this schema.",
      +  "properties": {
      +    "index": {
      +      "type": "number"
      +    },
      +    "labels": {
      +      "properties": {
      +        "complaint": {
      +          "items": {
      +            "properties": {
      +              "label": {
      +                "type": "string"
      +              },
      +              "labelEs": {
      +                "type": "string"
      +              },
      +              "probability": {
      +                "type": "number"
      +              }
      +            },
      +            "type": "object"
      +          },
      +          "type": "array"
      +        },
      +        "complaintScores": {
      +          "items": {
      +            "properties": {
      +              "label": {
      +                "type": "string"
      +              },
      +              "labelEs": {
      +                "type": "string"
      +              },
      +              "probability": {
      +                "type": "number"
      +              }
      +            },
      +            "type": "object"
      +          },
      +          "type": "array"
      +        },
      +        "motive": {
      +          "properties": {
      +            "confidence": {
      +              "type": "number"
      +            },
      +            "label": {
      +              "type": "string"
      +            },
      +            "labelEs": {
      +              "type": "string"
      +            },
      +            "probability": {
      +              "type": "number"
      +            }
      +          },
      +          "type": "object"
      +        },
      +        "sentiment": {
      +          "properties": {
      +            "confidence": {
      +              "type": "number"
      +            },
      +            "label": {
      +              "type": "string"
      +            },
      +            "labelEs": {
      +              "type": "string"
      +            },
      +            "probability": {
      +              "type": "number"
      +            }
      +          },
      +          "type": "object"
      +        }
      +      },
      +      "type": "object"
      +    },
      +    "model": {
      +      "type": "string"
      +    },
      +    "rating": {
      +      "type": "null"
      +    },
      +    "text": {
      +      "type": "string"
      +    }
      +  },
      +  "type": "object"
      +}
  2. Changed1 schema field changed
    • removedOutput schema / properties / storages / properties / datasets / properties / default / properties / itemsSchema
      Removed value: -{
      -  "description": "JSON Schema for rows in the dataset at `storages.datasets.default.id` — describes row shape only; the rows themselves are NOT returned inline in this response. Inferred from this Actor's historical successful runs. To fetch actual rows, call `get-dataset-items` with the dataset id and a `fields` projection drawn from this schema.",
      -  "properties": {
      -    "index": {
      -      "type": "number"
      -    },
      -    "labels": {
      -      "properties": {
      -        "complaint": {
      -          "items": {
      -            "properties": {
      -              "label": {
      -                "type": "string"
      -              },
      -              "labelEs": {
      -                "type": "string"
      -              },
      -              "probability": {
      -                "type": "number"
      -              }
      -            },
      -            "type": "object"
      -          },
      -          "type": "array"
      -        },
      -        "complaintScores": {
      -          "items": {
      -            "properties": {
      -              "label": {
      -                "type": "string"
      -              },
      -              "labelEs": {
      -                "type": "string"
      -              },
      -              "probability": {
      -                "type": "number"
      -              }
      -            },
      -            "type": "object"
      -          },
      -          "type": "array"
      -        },
      -        "motive": {
      -          "properties": {
      -            "confidence": {
      -              "type": "number"
      -            },
      -            "label": {
      -              "type": "string"
      -            },
      -            "labelEs": {
      -              "type": "string"
      -            },
      -            "probability": {
      -              "type": "number"
      -            }
      -          },
      -          "type": "object"
      -        },
      -        "sentiment": {
      -          "properties": {
      -            "confidence": {
      -              "type": "number"
      -            },
      -            "label": {
      -              "type": "string"
      -            },
      -            "labelEs": {
      -              "type": "string"
      -            },
      -            "probability": {
      -              "type": "number"
      -            }
      -          },
      -          "type": "object"
      -        }
      -      },
      -      "type": "object"
      -    },
      -    "model": {
      -      "type": "string"
      -    },
      -    "rating": {
      -      "type": "null"
      -    },
      -    "text": {
      -      "type": "string"
      -    }
      -  },
      -  "type": "object"
      -}
  3. Changed1 schema field changed
    • addedOutput schema / properties / storages / properties / datasets / properties / default / properties / itemsSchema
      Added value: +{
      +  "description": "JSON Schema for rows in the dataset at `storages.datasets.default.id` — describes row shape only; the rows themselves are NOT returned inline in this response. Inferred from this Actor's historical successful runs. To fetch actual rows, call `get-dataset-items` with the dataset id and a `fields` projection drawn from this schema.",
      +  "properties": {
      +    "index": {
      +      "type": "number"
      +    },
      +    "labels": {
      +      "properties": {
      +        "complaint": {
      +          "items": {
      +            "properties": {
      +              "label": {
      +                "type": "string"
      +              },
      +              "labelEs": {
      +                "type": "string"
      +              },
      +              "probability": {
      +                "type": "number"
      +              }
      +            },
      +            "type": "object"
      +          },
      +          "type": "array"
      +        },
      +        "complaintScores": {
      +          "items": {
      +            "properties": {
      +              "label": {
      +                "type": "string"
      +              },
      +              "labelEs": {
      +                "type": "string"
      +              },
      +              "probability": {
      +                "type": "number"
      +              }
      +            },
      +            "type": "object"
      +          },
      +          "type": "array"
      +        },
      +        "motive": {
      +          "properties": {
      +            "confidence": {
      +              "type": "number"
      +            },
      +            "label": {
      +              "type": "string"
      +            },
      +            "labelEs": {
      +              "type": "string"
      +            },
      +            "probability": {
      +              "type": "number"
      +            }
      +          },
      +          "type": "object"
      +        },
      +        "sentiment": {
      +          "properties": {
      +            "confidence": {
      +              "type": "number"
      +            },
      +            "label": {
      +              "type": "string"
      +            },
      +            "labelEs": {
      +              "type": "string"
      +            },
      +            "probability": {
      +              "type": "number"
      +            }
      +          },
      +          "type": "object"
      +        }
      +      },
      +      "type": "object"
      +    },
      +    "model": {
      +      "type": "string"
      +    },
      +    "rating": {
      +      "type": "null"
      +    },
      +    "text": {
      +      "type": "string"
      +    }
      +  },
      +  "type": "object"
      +}
  4. Changed1 schema field changed
    • removedOutput schema / properties / storages / properties / datasets / properties / default / properties / itemsSchema
      Removed value: -{
      -  "description": "JSON Schema for rows in the dataset at `storages.datasets.default.id` — describes row shape only; the rows themselves are NOT returned inline in this response. Inferred from this Actor's historical successful runs. To fetch actual rows, call `get-dataset-items` with the dataset id and a `fields` projection drawn from this schema.",
      -  "properties": {
      -    "index": {
      -      "type": "number"
      -    },
      -    "labels": {
      -      "properties": {
      -        "complaint": {
      -          "items": {
      -            "properties": {
      -              "label": {
      -                "type": "string"
      -              },
      -              "labelEs": {
      -                "type": "string"
      -              },
      -              "probability": {
      -                "type": "number"
      -              }
      -            },
      -            "type": "object"
      -          },
      -          "type": "array"
      -        },
      -        "complaintScores": {
      -          "items": {
      -            "properties": {
      -              "label": {
      -                "type": "string"
      -              },
      -              "labelEs": {
      -                "type": "string"
      -              },
      -              "probability": {
      -                "type": "number"
      -              }
      -            },
      -            "type": "object"
      -          },
      -          "type": "array"
      -        },
      -        "motive": {
      -          "properties": {
      -            "confidence": {
      -              "type": "number"
      -            },
      -            "label": {
      -              "type": "string"
      -            },
      -            "labelEs": {
      -              "type": "string"
      -            },
      -            "probability": {
      -              "type": "number"
      -            }
      -          },
      -          "type": "object"
      -        },
      -        "sentiment": {
      -          "properties": {
      -            "confidence": {
      -              "type": "number"
      -            },
      -            "label": {
      -              "type": "string"
      -            },
      -            "labelEs": {
      -              "type": "string"
      -            },
      -            "probability": {
      -              "type": "number"
      -            }
      -          },
      -          "type": "object"
      -        }
      -      },
      -      "type": "object"
      -    },
      -    "model": {
      -      "type": "string"
      -    },
      -    "rating": {
      -      "type": "null"
      -    },
      -    "text": {
      -      "type": "string"
      -    }
      -  },
      -  "type": "object"
      -}
  5. Added

TDQS

A3.8/5.0
Behavior3/5

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

Annotations declare destructiveHint=true and readOnlyHint=false, but the description frames the tool as calling an Actor and 'retrieving its output results,' never explaining what is mutated or consumed. It does add value the annotations lack — throughput (~100 reviews/5 s), a pay-per-review cost model, and Beta status — which keeps it above a bare baseline.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The leading sentence ('This tool calls the Actor "leoworks/spanish-review-classifier" and retrieves its output results. Actor description: ...') is boilerplate meta-text that delays the useful content. The remainder is dense and front-loads the classification purpose and language routing, but the opening wastes space.

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 and full schema coverage, the description does not need to explain return shapes; it correctly focuses on purpose, language routing and cost. It leaves the destructive behavior unexplained, which is the one meaningful gap for a tool flagged destructive.

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 all 13 parameters are documented in the schema already. The description only restates the two main input modes ('datasetId (text auto-detected) or texts') without adding syntax or semantics beyond the schema, so the baseline of 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 specific verb and resource: classify Spanish reviews into complaint types, sentiment and purchase motive, with the MercadoLibre context. It also distinguishes itself from the sibling korean-review-classifier by naming the language routing explicitly, so an agent can select it without opening schemas.

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 signal (Spanish) and points to the concrete alternative (leoworks/korean-review-classifier for Korean; datasetId or texts as input modes). It stops short of covering the other classifier siblings (Japanese, Amazon, Google Maps, social comment), so the routing map is incomplete but still actionable.

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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