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export_context_bundle

Export a portable Markdown or JSON bundle of structured memory for cross-tool context transfer, audits, or resuming work. Supports prime, query, and graph selection modes with filtering by agent, project, or session.

Instructions

Export a portable Markdown and/or JSON context bundle for handing memory to another AI or a human. Use for cross-tool context transfer, audits, and resumable work. Returns file paths, counts, and render hints.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoBundle selection mode: scoped prime context, query result, or broad graph export.prime
queryNoNatural-language query used when mode is 'query'.
agent_idNoOptional agent or client identifier used to partition memory.
projectNoOptional project or workspace name used to partition memory.
session_idNoOptional conversation or run identifier used to partition memory.
max_nodesNoMaximum number of nodes to include.
max_depthNoRelationship traversal depth for query or prime context modes.
retrieval_modeNoRetrieval strategy for query mode: graph-only, transcript replay, or fused results.graph
formatNoOutput format to write.both
output_pathNoOptional destination file path or directory prefix for the bundle.
include_edgesNoWhether relationship edges should be included in the export.
include_timestampsNoWhether created and updated timestamps should be included.
include_source_promptNoWhether original source prompts should be included when available.
audienceNoTarget audience used to tune bundle rendering.llm
Behavior3/5

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

No annotations provided, so description carries full burden. It mentions return values ('file paths, counts, and render hints') but lacks details on side effects like file creation or overwrite behavior. More transparency on what happens during export would be beneficial.

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?

Two sentences, front-loaded with purpose and usage. Every sentence earns its place; no unnecessary words.

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?

Given 14 parameters and no output schema, the description is brief. It covers high-level functionality but omits details like output_path behavior or file format specifics. Schema descriptions compensate partially, but overall completeness is moderate.

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

Parameters3/5

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

Schema coverage is 100% with detailed parameter descriptions. The description itself adds no new parameter information beyond stating the output nature. Baseline 3 is appropriate as description does not enhance schema semantics.

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 verb 'Export', the resource 'portable Markdown and/or JSON context bundle', and the purpose 'handing memory to another AI or a human'. It distinguishes from siblings like export_graph_backup and export_markdown_vault by focusing on cross-tool context transfer.

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?

Explicitly states use cases: 'cross-tool context transfer, audits, and resumable work'. While it does not list negative cues or compare directly to all siblings, it provides clear context for when to use this tool.

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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