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palinode_dedup_suggest

Read-onlyIdempotent

Checks draft memory against existing notes using semantic similarity, flags duplicates with similarity ≥0.90 to suggest updating instead of creating.

Instructions

Given draft memory content the LLM is about to save, return the top-K existing memory files whose embeddings are semantically near it. Use BEFORE writing a new memory to decide 'create new' vs 'update existing'. Each result includes a strong_dup flag — when true (similarity ≥ 0.90), the existing file is a near-paraphrase and the LLM should usually update rather than create. Preprocessing strips wikilink syntax and the auto-generated ## See also footer so notes linking the same entities don't false-positive as duplicates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_kNoMaximum number of candidate files to return. Default 5.
contentYesThe draft memory body about to be saved (markdown, with or without frontmatter).
min_similarityNoMinimum cosine similarity to surface (0.0–1.0). Default 0.80.
Behavior5/5

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

Adds valuable behavioral details beyond annotations: preprocessing strips wikilink syntax and auto-generated footer to avoid false positives, explains strong_dup flag meaning and recommended action. No contradiction with 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?

Three sentences with efficient structure: purpose, usage guideline, preprocessing detail. No wasted words.

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, return value (strong_dup), and preprocessing. Lacks return structure details, but given no output schema, it is adequate.

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 covers parameters fully (100%), but description adds meaning about output (strong_dup flag) which is not in schema, compensating for lack of output schema.

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?

Description clearly states 'return the top-K existing memory files whose embeddings are semantically near it', with specific verb+resource and distinct purpose from siblings like palinode_save or palinode_search.

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 BEFORE writing a new memory to decide create new vs update existing', providing clear context. Doesn't mention when not to use, but the guidance is sufficient.

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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