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picsha-ai

Picsha AI MCP Server

Official
by picsha-ai

upload_asset

Upload a local file to Picsha AI, which automatically triggers AI analysis (metadata, thumbnails, faces, tags). The asset returns in pending state; use get_asset to retrieve final processed results.

Instructions

Upload a local file directly to the Picsha AI platform. This acts as a proxy, fetching a pre-signed S3 URL and executing the PUT request automatically. If your agent is running with user sandboxing, this file will automatically be securely bound to that user's identity. Note: Uploading immediately triggers the asynchronous 'picsha-ai-ingest' pipeline which will extract metadata, generate thumbnails, and run AI analysis (faces, tags, bedrcock summaries). Therefore, the returned asset will initially be in a 'pending' state. You should use the 'get_asset' tool a few seconds after uploading to retrieve the final AI-processed results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filePathYesAbsolute path to the local file (e.g. /Users/name/images/photo.jpg) to upload
filenameNoOptional original filename to associate with the asset. Defaults to the file's basename.
Behavior5/5

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

No annotations, so description carries full burden. Discloses proxy behavior, sandboxing binding, async pipeline triggering, pending state, and recommendation for retrieval. Comprehensive for a simple upload tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Moderately sized but every sentence earns its place. Logical flow: purpose, proxy details, sandboxing, pipeline, state, recommendation. Slightly verbose but not wasteful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations or output schema, description covers all necessary aspects: input, process, side effects, state transitions, and next steps. Sufficient for correct invocation and expectation management.

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%, so baseline 3. Description adds value: filePath example format and absolute path hint, filename default behavior. Enhances schema without repeating.

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?

Describes a specific action (upload) and resource (local file to Picsha AI platform) with clear scope (directly, via proxy). Differentiates from siblings like trigger_url_ingest which handles URL ingestion.

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 states when to use: uploading local files. Mentions sandboxing binding and async pipeline, implying post-upload step (use get_asset). Does not explicitly list when not to use or alternatives, but context is clear.

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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