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Backend Architect MCP Server

Backend Architect MCP Server

by jemhakdog
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

save_route_plan

Store API endpoint configurations including method, path, and summary to maintain project state during backend development.

Instructions

Saves API endpoints (method, path, summary) to state.

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": {
      "routes": "<routes>"
    },
    "name": "save_route_plan"
  }
}

Input Schema

NameRequiredDescriptionDefault
routesYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool saves data to state, implying a write operation, but lacks details on permissions, idempotency, error handling, or what 'state' refers to (e.g., memory, file, database). This leaves significant gaps in understanding how the tool behaves beyond its basic function.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it easy to parse quickly.

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?

Given the tool has an output schema (which should cover return values), no annotations, and low schema coverage, the description is minimally adequate. It specifies the resource being saved but lacks details on behavior, usage context, and parameter specifics, making it incomplete for safe and effective use without additional documentation.

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?

The schema description coverage is 0%, so the description must compensate. It mentions 'API endpoints (method, path, summary)', which hints at the structure of the 'routes' parameter, adding some meaning beyond the schema's generic object array. However, it doesn't fully explain the expected format or constraints, leaving ambiguity for the agent.

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 description clearly states the action ('Saves') and the resource ('API endpoints (method, path, summary) to state'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'save_database_plan' or 'save_test_plan' beyond the resource type, which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as other 'save_' tools or how it relates to operations like 'initialize_project'. There's no mention of prerequisites, exclusions, or specific contexts for usage, leaving the agent with minimal direction.

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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