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Agent Memory Bridge

export

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Instructions

Export bridge content into a readable or portable format.

Use this tool when you want to inspect a namespace outside the MCP client, create a human-readable snapshot, or move memory into another system without opening the database directly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namespaceYesNamespace to export, such as `project:<workspace>`, `domain:<name>`, or `global`.
formatNoOutput format for the exported memory. Use `markdown` for readable notes, `json` for structured interchange, or `text` for plain text.markdown
queryNoOptional full-text query to narrow the export. Leave empty to export by filters alone.
kindNoOptional type filter for the export.
signal_statusNoOptional status filter when exporting signal entries.
tags_anyNoOptional OR-style tag filter. Any matching tag is enough for an entry to be included.
limitNoMaximum number of entries to export in one call.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations provided, so description carries full disclosure burden. Adds valuable context about external portability and avoiding direct database access, but omits critical safety profile (read-only vs. destructive), idempotency, or whether 'export' implies copy or move semantics. 'Move memory' phrasing slightly ambiguous regarding source data retention.

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 well-structured paragraphs with zero redundancy. First sentence establishes core function; second paragraph delivers specific usage contexts. Front-loaded and efficient—every sentence earns its place without repeating schema details.

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?

Rich 7-parameter schema with 100% coverage and existing output schema reduces description burden. Description adequately covers the 'why' and 'when' for this complex filtering/export tool, though could acknowledge the batch nature (limit parameter) or signal-specific filtering capabilities.

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%, establishing baseline 3. Description provides conceptual framing ('human-readable snapshot') that aligns with format options, but does not add parameter-specific guidance, syntax examples, or filtering logic beyond the comprehensive schema documentation.

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?

Clear verb (export) and resource (bridge content) with scope (readable/portable format). Distinguishes from siblings like 'recall' and 'browse' by emphasizing external use cases and portability, though could explicitly contrast with internal retrieval tools.

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?

Explicit 'Use this tool when...' statement lists three concrete scenarios: external namespace inspection, human-readable snapshots, and inter-system data transfer. Lacks explicit negative constraints (when not to use) or named alternatives, but effectively guides selection via positive use cases.

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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