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gisgp

by io.github.uponex
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bundle_export_multi_format

PAID (120 credits) — fetch ALL records from a live FeatureServer layer once, then export the same data as Shapefile + CSV + GeoJSON + KML + Excel in a single ZIP (base64-encoded). Replaces an uncapped fetch plus 4 separate format-conversion calls. Requires Authorization: Bearer <api_key> and sufficient credits balance.

Returns JSON: {ok, feature_count, truncated, zip_base64, formats}.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "service_url": "<service_url>"
    },
    "name": "bundle_export_multi_format"
  }
}

Input Schema

NameRequiredDescriptionDefault
tokenNo
whereNo1=1
service_urlYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations provided, the description bears the full burden. It discloses cost (120 credits), operational scope (ALL records), output format (ZIP base64), and the JSON response shape. It lacks details on size limits/truncation behavior beyond the 'truncated' flag, but overall it is transparent enough for safe use.

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?

The description is dense and well-structured: first sentence defines the action, second explains the efficiency benefit, third states requirements, fourth specifies the return value. Every sentence earns its place, and the information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, cost, auth, output, and expected response. However, with an output schema present, return values are not the main gap. The missing parameter semantics (especially what 'where' does) leave the tool incompletely specified for callers, so overall completeness is only adequate.

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

Parameters1/5

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

Schema description coverage is 0%, yet the description does not explain any of the three parameters (token, where, service_url). It only indirectly refers to 'live FeatureServer layer' without connecting to the service_url parameter. The description completely fails to compensate for the missing schema documentation.

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 uses specific verbs and resources: 'fetch ALL records from a live FeatureServer layer' and 'export the same data as Shapefile + CSV + GeoJSON + KML + Excel in a single ZIP (base64-encoded).' This clearly distinguishes it from sibling tools like bundle_export_fixed or individual format conversions.

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?

The description mentions it 'replaces an uncapped fetch plus 4 separate format-conversion calls,' clearly indicating when to use this tool over multiple alternatives. It also states prerequisites (Authorization header and credit balance), providing actionable context for invocation.

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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