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cowpin-ai

Cowpin Agent Memory MCP Server

Official
by cowpin-ai

cowpin_search_memory

Search saved research and notes using semantic vectors and keyword matching to retrieve relevant memories with optional tag filters.

Instructions

Perform semantic vector and full-text keyword search over the agent's persistent memory vault using Reciprocal Rank Fusion. Returns relevant past research and snippets.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoOptional tag filter
limitNoMaximum number of results to return (default: 5, max: 20)
queryYesNatural language search query or keywords

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only states the action and result, but does not reveal whether the operation is read-only, if it has side effects, or any limitations. For a search tool, it is implied to be non-destructive, but the description does not explicitly confirm this.

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 a single, concise sentence that front-loads the primary action and returns expectation. No filler or redundant content. It is efficient and well-structured.

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?

For a tool with only three documented parameters and no output schema, the description provides a vague sense of output ('relevant past research and snippets') but does not describe the structure of results (e.g., list of items with fields, ranking order). It is adequate but could be more specific about the return format.

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 description coverage is 100%, so the schema already documents each parameter. The description adds no additional detail about parameter usage beyond the general search method; it does not elaborate on tags, limit, or query interpretation. This meets the baseline but does not enhance.

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 clearly states the action (semantic vector and full-text keyword search), the resource (persistent memory vault), and the method (Reciprocal Rank Fusion). It also specifies the return type (relevant past research and snippets), making it distinct from sibling tools like save or balance.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives. It does not mention other sibling tools or conditions for exclusion. The usage context is only implied by the purpose, not explicitly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.