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EcuDataMCP

by DweskZ
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Python
Hybrid

Detectar patrón de series periódicas en un dataset

detect_series_pattern
Read-only

Determine whether a dataset's periodic files are cumulative or incremental by comparing period values in its two newest files.

Instructions

Tell whether a dataset's periodic files are cumulative (newest file suffices) or incremental (combine all) by comparing period values in the two newest files. Heuristic over up to 500 rows each.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNotext summary (default) or json structured result.text
sourceNoCKAN portal; nacional (default), cuenca, latacunga or iadb.nacional
dataset_idYesThe dataset ID or slug
resource_id_newNoOptional -- newer resource ID to compare (auto-detected from the dataset's periodic-name group if omitted)
resource_id_oldNoOptional -- older resource ID to compare (auto-detected if omitted).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.13.0
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "type": "object"
-}New value: +null
  2. Changed5 schema fields changedv0.10.0
    • addedInput schema / properties / dataset_id / description
      Added value: +"The dataset ID or slug"
    • addedInput schema / properties / format / description
      Added value: +"text summary (default) or json structured result."
    • addedInput schema / properties / resource_id_new / description
      Added value: +"Optional -- newer resource ID to compare (auto-detected from the dataset's periodic-name group if omitted)"
    • addedInput schema / properties / resource_id_old / description
      Added value: +"Optional -- older resource ID to compare (auto-detected if omitted)."
    • addedInput schema / properties / source / description
      Added value: +"CKAN portal; nacional (default), cuenca, latacunga or iadb."
  3. Changed7 schema fields changedv0.8.14
    • removedInput schema / properties / dataset_id / title
      Removed value: -"Dataset Id"
    • removedInput schema / properties / format / title
      Removed value: -"Format"
    • removedInput schema / properties / resource_id_new / title
      Removed value: -"Resource Id New"
    • removedInput schema / properties / resource_id_old / title
      Removed value: -"Resource Id Old"
    • removedInput schema / properties / source / title
      Removed value: -"Source"
    • removedInput schema / title
      Removed value: -"detect_series_patternArguments"
    • removedOutput schema / title
      Removed value: -"detect_series_patternDictOutput"
  4. First observedv0.8.12

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare it read-only, non-destructive, open-world. The description adds genuine behavioral context beyond them: it only samples up to 500 rows per file and is explicitly a heuristic, so the agent knows the result is approximate and bounded. That is exactly the kind of caveat annotations do not carry.

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 tight sentences with zero filler. The core purpose and the cumulative-vs-incremental distinction are front-loaded, and the heuristic/row-limit caveat follows immediately.

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 no output schema, the description still conveys the decision it returns (cumulative vs incremental) and the format switch is documented in the schema. It is nearly complete, missing only how the conclusion is expressed in the text/json output.

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 every parameter (source portal, format, dataset_id, resource_id_new/old with auto-detection) is already documented in the schema. The description adds only the notion that the two newest files are used, which is baseline-level value when the schema does the heavy lifting.

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+resource: determining whether a dataset's periodic files are cumulative or incremental. It even names the mechanism (comparing period values in the two newest files), so an agent can distinguish it from list_dataset_resources or investigate_dataset without opening a schema.

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 when the result matters (choosing whether the newest file suffices or all files must be combined), but never states when to call this tool versus siblings like list_dataset_resources or investigate_dataset, nor any prerequisites or exclusions. Usage must be inferred from the purpose.

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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