Skip to main content
Glama

Penfield Memory

by io.github.penfieldlabs
OverviewDiscussionsAdmin

list_contexts

Read-only
List available context checkpoints.

Shows all saved contexts available for multi-agent workflows.

Args:
    limit: Maximum number of contexts to return (default 20, max 100)
    offset: Number of contexts to skip for pagination (default 0)
    name_pattern: Filter contexts by name (case-insensitive substring match)
    include_descriptions: Include full descriptions in output (default False for compact listing)
    ctx: MCP context (automatically provided)

Returns:
    Dict with list of available contexts and their details

Examples:
    >>> await list_contexts()
    {'success': True, 'total': 3, 'contexts': [...]}

    >>> await list_contexts(limit=5, name_pattern='investigation')
    {'success': True, 'total': 2, 'contexts': [...]}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
offsetNo
name_patternNo
include_descriptionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Consistent with readOnlyHint and openWorldHint annotations. Adds details on pagination, filtering, and default behavior beyond annotations.

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 purpose statement, args documentation, return description, and examples. No wasted words; efficient and front-loaded.

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?

Covers all necessary aspects: input parameters, return value (Dict with contexts), and examples. Output schema is present but not fully detailed in description; still adequate.

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?

Schema has 0% description coverage, but the description thoroughly explains each parameter: limit (default 20, max 100), offset, name_pattern (case-insensitive substring), include_descriptions (default False). Adds significant value.

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 the tool lists available context checkpoints for multi-agent workflows. Distinct from sibling tools like save_context and restore_context.

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?

Provides usage context (multi-agent workflows) and examples with filtering. Lacks explicit guidance on when to prefer this tool over alternatives like list_artifacts.

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

All Penfield Memory toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources