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ceaksan

dnomia-knowledge

by ceaksan

read_file

Retrieve relevant file sections from large indexed files using a query, with fallback to full content for non-indexed files.

Instructions

Smart file reading with index awareness.

If the file is indexed and large, returns relevant chunks instead of full content. Falls back to raw file reading for non-indexed files.

Args: file_path: Absolute path to the file to read query: Optional search query to find relevant sections in large files project: Project ID (default: auto-detect from file path)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
projectNo
file_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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 does reveal the main conditional behavior: chunks for indexed large files, raw fallback for non-indexed files. Still, it leaves gaps around what counts as 'large', what happens when query is omitted, and how query behaves for non-indexed files.

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 compact and front-loaded: a one-line summary, a short conditional behavior statement, and a clear Args list. Every sentence earns its place, with no unnecessary elaboration.

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?

The core behavior and parameter semantics are covered well, and an output schema apparently handles return-value details. However, important operational context is missing: the threshold for 'large', behavior when query is absent on an indexed file, and guidance on how this differs from using search. This is adequate for straightforward use but incomplete for edge cases.

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 does. It explains all three parameters meaningfully: file_path must be absolute, query finds relevant sections in large files, and project defaults to auto-detection from the file path. This goes beyond the bare schema names and defaults.

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 identifies the tool as a file reader with index-aware chunking: it reads files, returns relevant chunks for indexed large files, and falls back to raw reading for non-indexed files. This is a specific verb-resource pairing that is distinguishable from siblings like search or index_project, though it does not explicitly name those alternatives.

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?

The description implies when the tool is appropriate by describing behavior for indexed vs. non-indexed files and by mentioning query for relevant sections. However, it does not explicitly say when to prefer a sibling tool such as search instead, nor does it provide when-not-to-use guidance.

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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