Skip to main content
Glama

read_project_artifacts

Read project artifact files in batch, supporting full content, specific sections, or line ranges with truncation and line numbers.

Instructions

[ARTIFACT TOOLS] Reads one or more artifact files in a single batch call. Each item in reads targets one file and specifies an independent read mode.

Read modes per item: full — returns the entire file content (default). section — returns only the content under a named ## header (requires section_name). lines — returns a specific line range (requires start_line and end_line).

Optional per-item fields: max_chars : int — truncate response at N characters (default 10000). skip_chars : int — skip N characters from the start of the selection. direction : 'begin' | 'end' — read from start or end of file (default 'begin'). line_numbers : bool — prefix each line with its 1-based line number.

Do NOT loop this tool per file — batch all reads into a single call to minimise round-trips. For source code files in src/, use view_file_source instead.

Returns: list of result objects — each with path and content (or error if not found). Raises: per-item error entry if a path does not exist; does not abort the batch.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
readsYesList of read requests
projectYesProject name

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, but description fully discloses read modes, optional fields, default values, batch error handling (per-item error does not abort), and return structure. Provides comprehensive behavioral context.

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?

Well-structured with clear sections for read modes and optional fields. Front-loaded with main purpose, then details. No wasted sentences; every part contributes to understanding.

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

Completeness5/5

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

Given no annotations, high schema coverage, and output schema present, description covers usage, behavior, parameter details, return format, and error handling. Complete for a complex batch tool.

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 coverage is 100%, but description adds significant meaning by explaining each read mode, usage of optional fields (skip_chars, direction, line_numbers), and how they interact. Goes beyond 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?

Clearly states it reads artifact files in a batch call, with specific verb 'reads' and resource 'artifact files'. Distinguishes from sibling 'view_file_source' for source code files.

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

Usage Guidelines5/5

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

Explicitly instructs to batch all reads into a single call to avoid looping, and provides alternative tool for source code files. Tells when not to use and gives a concrete alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/desikai-lab/Marrow'

If you have feedback or need assistance with the MCP directory API, please join our Discord server