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agishub

AgisHub MCP Server

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by agishub

memory_search

Perform semantic search over a stored memory namespace to retrieve the most relevant entries. Use this to recall context for RAG pipelines.

Instructions

Semantically search a memory collection (namespace) and return the most relevant stored entries. The retrieval half of RAG.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural-language query; returns the most semantically similar stored entries.
top_kNoHow many results to return (default 5).
namespaceYesThe namespace to search within (the same key used on upsert).
Behavior4/5

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

With no annotations provided, the description must convey behavioral traits. It clearly indicates a read-only retrieval operation ('semantically search', 'return') with no mutation implied. It does not mention ordering, pagination, or error cases, but for a standard search tool the core behavior is transparent enough.

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 only two sentences: the first states the core function precisely, and the second adds helpful context ('retrieval half of RAG'). No wasted words, and the most important information is front-loaded.

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?

Given the tool's simplicity, the absence of an output schema, and the lack of annotations, the description provides sufficient context: it explains what the tool does, the namespace concept, and the RAG relationship. It could mention return format details, but 'most relevant stored entries' is adequate for an agent to understand the expected result.

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?

The input schema already provides complete descriptions for all three parameters (query, top_k, namespace) with 100% coverage. The tool description does not add new parameter-level detail beyond what the schema states, so it earns the baseline score.

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 states a specific action ('semantically search'), a clear resource ('memory collection (namespace)'), and the outcome ('return the most relevant stored entries'). It also distinguishes itself from siblings like memory_upsert by calling itself 'The retrieval half of RAG,' making its role unambiguous.

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?

The phrase 'The retrieval half of RAG' implies this tool is for retrieval tasks and suggests a complementary relationship with memory_upsert. However, it does not explicitly state when not to use it or name alternatives, so it falls short of full guidance.

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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