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LLM Router MCP

LLM Router MCP

by Devatva24
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

implement_feature

Implement features, generate tests, and automate repetitive coding tasks by routing to GPT-4o for reliable execution.

Instructions

Always uses GPT-4o for feature implementation, test generation, or repetitive coding tasks.

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": {
      "prompt": "<prompt>"
    },
    "name": "implement_feature"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYes
session_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

C2.7/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It discloses that GPT-4o is always used, but omits important behavioral details such as whether the tool modifies the codebase, requires specific permissions, or has side effects. The mention of 'repetitive coding tasks' hints at scope but is insufficient.

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 description is a single concise sentence with no wasted words. It front-loads the key information about the tool's function (using GPT-4o for coding tasks), though it sacrifices necessary detail for brevity.

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

Completeness2/5

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

Given the tool's two parameters, no output schema, and sibling tools, the description is inadequate. It fails to explain the input prompt's structure, the tool's role relative to 'generate_code', or any expected outcomes, leaving the agent without sufficient context to invoke it correctly.

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

Parameters2/5

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 for the two parameters 'prompt' and 'session_id'. It implies that 'prompt' is the task description, but provides no explicit explanation of the parameters or their formats. This minimal compensation leaves significant semantic gaps.

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 tool uses GPT-4o for feature implementation, test generation, or repetitive coding tasks, providing a specific function. However, it does not differentiate from the sibling tool 'generate_code', which likely overlaps in purpose.

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?

No guidance is given on when to use this tool versus alternatives like 'generate_code' or 'plan_workflow'. The description only mentions the model used and task types, without context for selection.

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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