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code-lens

code-lens

by j0hanz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Ask About Code

ask_about_code
Read-onlyIdempotent

Answer questions about a cached code file. Auto-detects programming language. Requires prior file load.

Instructions

Answer questions about a cached file. Prerequisite: load_file. Auto-infer language.

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": {
      "question": "<question>"
    },
    "name": "ask_about_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
questionYesQuestion about the loaded file.
languageNoPrimary language (e.g. TypeScript, Python, JavaScript, Go, Rust, Java). Auto-infer from files.

Output Schema

NameRequiredDescriptionDefault
okYesWhether the tool completed successfully.
resultNoSuccessful result payload.
errorNoError payload when ok is false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.12.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so risk profile is clear. The description adds valuable behavioral context: a prerequisite (load_file) and auto-inference of language. However, caching scope could be more explicit.

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?

Extremely concise: a single sentence and a note. Every element (prerequisite, auto-inference) has purpose. No wasted words.

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?

Given the presence of an output schema and comprehensive annotations, the description is mostly complete. It covers prerequisite and auto-inference, but could mention that questions are answerable only after file loading. Still, it meets the bar for a focused tool.

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?

Schema description coverage is 100% and already describes both parameters adequately. The description adds 'Auto-infer language' which reinforces schema but does not significantly expand on it. With high schema coverage, baseline 3 is appropriate.

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 clearly states 'Answer questions about a cached file' with a specific verb and resource. It distinguishes from sibling tools (e.g., analyze_pr_impact, generate_documentation) by focusing on Q&A over a pre-loaded file.

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?

It mentions 'Prerequisite: load_file' implying when to use, but does not explicitly state when not to use it or contrast with alternatives. The usage context is implied rather than fully explicit.

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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