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allenc84
by allenc84

search_memory

Search memory by semantic similarity to surface prior decisions, context, and evolved thinking for topic-specific conversations.

Instructions

Search the memory system by semantic similarity. Use this at the start of any topic-specific conversation to surface relevant context, prior decisions, and evolved thinking — without being asked. Returns memories ranked by relevance × salience.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query describing what context you need
top_kNoNumber of memories to return (default 6)
typesNoFilter by memory type. Options: ['episodic', 'feedback', 'project', 'reference', 'semantic', 'user']. Omit to search all.
namespaceNoNamespace to search. Omit for this server's namespace; '*' searches every namespace.
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions ranking by 'relevance × salience' but does not disclose read-only nature, required permissions, side effects, or rate limits. The phrase 'without being asked' is slightly misleading for an explicit search tool.

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?

The description is two sentences long, front-loads the core purpose, and contains no extraneous information. Every sentence adds value.

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 4 parameters, no output schema, and no annotations, the description provides purpose and ranking behavior but lacks output format details, error handling, and sufficient differentiation from sibling tools. It is adequate but has gaps.

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% and parameter descriptions in the schema are detailed. The tool description adds no additional parameter-level information beyond the schema, so baseline score of 3 is appropriate.

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?

The description clearly states it searches the memory system by semantic similarity and provides a specific use case (start of topic-specific conversation). However, it does not explicitly differentiate from the sibling tool 'get_context_brief', which likely retrieves context in a different way.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a clear usage context ('at the start of any topic-specific conversation') but lacks explicit when-not-to-use guidance or comparison with alternatives like 'get_context_brief' or 'save_memory'.

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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