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JamesZor

Antigravity MCP Server

by JamesZor

index_code

Reads specific directories or files and returns a distilled architectural index, allowing you to understand a codebase subset without processing all raw content.

Instructions

Reads specific directories or files using Antigravity's massive context window and returns a distilled architectural index.
Use this to quickly understand a subset of a codebase (or a whole repo) without pulling all the raw files into the frontier model's context window.

Args:
    paths: A list of absolute paths to directories or files to index.
    focus: What to focus the index on (e.g., 'general architecture', 'database schemas', 'API routes'). Default is 'general architecture'.
    out_path: Optional absolute path to also persist the index to (e.g. '<repo>/docs/ARCHITECTURE.md') so it survives for incremental reuse instead of living only in the caller's context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
focusNogeneral architecture
pathsYes
out_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations provided, so the description must disclose behavioral traits. It mentions 'reads' and 'returns', implying read-only access, but does not explicitly state that the tool is non-destructive, idempotent, or safe. It also lacks information on rate limits, authentication, or potential side effects like file creation via out_path.

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 concise at about 100 words, with a clear one-sentence purpose, a usage guideline sentence, and a structured Args section. Every sentence adds value without redundancy.

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?

For a tool with 3 parameters and an output schema, the description provides essential information about what the tool does, when to use it, and parameter details. It does not cover error handling or output format, but the output schema likely addresses the latter. Overall, it is nearly complete for an agent to select and invoke correctly.

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

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully explains all three parameters: paths (list of absolute paths), focus (with examples and default), and out_path (with example and purpose). This compensates entirely for the missing schema descriptions.

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 the tool 'reads specific directories or files' and 'returns a distilled architectural index', with a specific verb and resource. It is distinct from sibling tools which include auto_git_commit, check_job_status, etc., and effectively communicates its unique role in codebase understanding.

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

Usage Guidelines4/5

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

The description explicitly says when to use: 'to quickly understand a subset of a codebase... without pulling all raw files'. While it doesn't explicitly state when not to use or name alternatives, the usage context is clear and specific.

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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