PyEye Server

Overview Schema Source Related Servers Discussions Admin

find_flask_routes

Discovers all Flask routes in a Python project and lists their endpoints, helping you map URL handlers across main, all, or namespace scopes.

Instructions

Find all Flask routes in the project.

Args: scope: Search scope (default: this tool's scope_defaults entry in the project config, else scope_defaults.global, else "main"): - "main": Only the main project (default for plugins) - "all": Include configured namespaces - "namespace:name": Specific namespace

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "find_flask_routes"
  }
}

Input Schema

Name	Required	Description	Default
`scope`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv2.0.0Nov 7, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses the default-resolution chain for scope (tool config entry, then global defaults, then "main"), which is real behavioral context, and "find" implies a non-mutating scan. However it says nothing about whether the scan is project-wide, how expensive it is, or whether it requires an indexed/configured project.

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

Conciseness4/5

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

The purpose sentence is front-loaded and the argument block is compact. The default-resolution sentence is somewhat wordy with its triple fallback chain, but that detail is genuinely useful rather than filler.

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?

This is a low-complexity tool: one optional parameter and an output schema that already documents return values. The description covers purpose and the only parameter's semantics well; the remaining shortfall is routing guidance versus sibling Flask tools.

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%, so the description must compensate, and it largely does: it enumerates the accepted scope forms ("main", "all", "namespace:name") and the fallback order. The one gap is that the schema permits an array of strings while the description only discusses single string values, leaving multi-scope usage unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence gives a specific verb and resource ("Find all Flask routes in the project"), which an agent can distinguish from siblings like find_flask_views or find_flask_blueprints by resource name alone. It does not explicitly state how it differs from those adjacent Flask tools, keeping it short of a 5.

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?

Usage is implied by the name and purpose sentence — call it when you need the project's route table — but there is no explicit when-to-use/when-not guidance and no mention of the sibling tools (find_flask_views, find_flask_blueprints, find_error_handlers) that could also surface routing information.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.