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iai-mcp

memory_capture

Capture verbatim turns to preserve load-bearing decisions and corrections, with automatic deduplication at high similarity to reinforce relevant memories.

Instructions

Capture a verbatim turn. Auto-dedups at cos>=0.95 (reinforces). Use for corrections + load-bearing decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesVerbatim text to capture (user utterance, Claude decision, or observation). Min 12 chars, max 8000 (longer is truncated).
cueNoShort natural-language cue used for embedding + dedup lookup. If empty, `text` itself is embedded.
tierNoMemory tier. Default 'episodic' (verbatim user utterances). Use 'semantic' for induced summaries, 'procedural' for learned behaviour notes.episodic
session_idNoCurrent session id for provenance.
roleNoWho produced this turn — tags the record for filtering.user

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusNo
record_idNo
reasonNo
Behavior4/5

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

Adds key behavior beyond annotations: 'Auto-dedups at cos>=0.95 (reinforces)'. Annotations are all false, so this is valuable. No contradictions.

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 behavior, followed by use cases. No redundant information.

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 purpose, key behavior, and usage. Has output schema. For a tool with 5 params and 100% schema coverage, the description is adequate and provides necessary context for selection.

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%, so baseline is 3. Description does not add significant meaning beyond schema; 'verbatim turn' aligns with text param but does not elaborate on others.

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 'Capture a verbatim turn', specifying the action and resource. Distinguishes from siblings like memory_recall by focusing on capture.

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 says 'Use for corrections + load-bearing decisions', providing specific use cases. Does not explicitly list when not to use or alternatives, but gives clear context.

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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