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flet-mcp-server

flet-mcp-server

by Nwokike
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_flet_example

Read-only

Retrieve the full source of an official Flet example by ID, including pyproject.toml and Python files, with a character budget to keep large examples readable.

Instructions

Fetch the full source of an official Flet example app (from search_flet_examples): pyproject.toml plus its Python files, bundled with a character budget so huge examples page gracefully.

Args: example_id: Example id from search_flet_examples (e.g., 'counter' or '7guis/flight_booker'). max_chars: Total character budget for the returned source (default 24000).

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": {
      "example_id": "<example_id>"
    },
    "name": "get_flet_example"
  }
}

Input Schema

NameRequiredDescriptionDefault
max_charsNo
example_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.2

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and destructiveHint, so the safety profile is covered. The description adds behavioral value beyond annotations by disclosing that the returned source is bundled with a character budget so large examples 'page gracefully', which hints at truncation or pagination behavior.

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 first sentence immediately states the tool's purpose and contents, and the Args section is compact and information-dense. Every sentence earns its place without unnecessary filler.

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

Completeness5/5

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

Given an output schema exists and the tool has only two simple parameters, the description covers everything needed to call it correctly: what is fetched, where the id comes from, and how the character budget behaves. No critical operational detail is missing.

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?

Schema description coverage is 0%, but the description compensates well: example_id is explained as coming from search_flet_examples with concrete format examples, and max_chars is clarified as a total character budget for the returned source. This adds real meaning beyond the bare schema properties.

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 ('Fetch'), a precise resource ('full source of an official Flet example app'), and the expected contents (pyproject.toml plus Python files). It also anchors the tool to 'search_flet_examples', which distinguishes it from generic source readers and doc lookup tools among the siblings.

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 a workflow by saying example_id comes from search_flet_examples, but it never explicitly tells an agent when to choose this tool over alternatives like get_flet_doc or read_flet_source. There is clear use context, but no exclusions or alternative routing.

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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