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picsha-ai

Picsha AI MCP Server

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by picsha-ai

search_assets

Search digital assets using vector or keyword queries. Use natural language filters to locate specific assets.

Instructions

Search for assets in the Picsha AI platform using vector or standard keyword search. Note: If your agent instance is sandboxed to a specific user via environment variables, this search will ONLY return assets owned by that specific user. You are securely retrieving their contextual assets.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query string. You can append natural language dates/actions like 'added today' or 'uploaded last week' to filter by time.
modeNoSearch mode. 'ai' uses vector hybrid search, 'standard' uses exact keyword / tag matching.ai
thresholdNoConfidence threshold for AI searches (0.0 to 1.0)
sortNoSort order for the resultsrelevance
Behavior4/5

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

With no annotations, the description carries full burden. It discloses sandboxing behavior (returns only user's assets if sandboxed) and search modes. Lacks mention of pagination or rate limits, but adds meaningful context beyond 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?

Two sentences front-loaded with purpose followed by a critical usage constraint. No wasted words; every sentence adds value.

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 no output schema and 4 parameters, the description is mostly complete. It explains search behavior and sandboxing. Could mention that results are a list of assets, but overall 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 coverage is 100%, and the description adds extra meaning: explains natural language date filtering in query and differentiates mode options. This improves upon the schema alone.

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 tool's purpose: 'Search for assets... using vector or standard keyword search.' It differentiates from siblings like get_asset (single asset) and list_recent_assets by focusing on query-based 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?

The description implies usage (search when you have a query) and distinguishes between vector and standard modes. However, it does not explicitly state when not to use this tool versus alternatives like get_asset for known IDs.

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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