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Glama

read_memory

Retrieve persistent project memory for AI coding assistants, optionally limiting output by line count to get quick summaries or full content.

Instructions

Read project memory.

Args:
    max_lines: Maximum number of lines to return (default: 100).
               Set to None for full content. Use smaller values for quick summaries.

Returns:
    Memory content (possibly truncated)

Input Schema

NameRequiredDescriptionDefault
max_linesNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.12.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose a key behavioral trait: output may be truncated depending on max_lines. It says nothing about prerequisites (project root initialization), the memory format, or whether the read is scoped to the current project only.

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?

Front-loaded with the one-line purpose, then Args/Returns. Every sentence carries information about the parameter or return, though the docstring scaffolding adds mild overhead.

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?

An output schema exists and the parameter is documented, so calling it is feasible. But in a suite of ~50 tools with three memory-reading siblings, the absence of any routing or prerequisite information leaves a real gap.

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 0%, so the description must explain the single parameter, and it does well: default of 100, None meaning full content, and the small-value tradeoff for summaries. Only the interaction with the 'possibly truncated' return is left implicit.

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?

States a specific verb and resource ('Read project memory'), so the intent is unambiguous. However, it does not distinguish itself from close siblings like read_memory_section, read_memory_index, or search_memory, leaving the agent to guess which one to call.

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?

The only guidance ('use smaller values for quick summaries') concerns how to set max_lines, not when to choose this tool over read_memory_section or search_memory. No prerequisites (e.g., that a project root must be set) or exclusion conditions are given.

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