PhotoForge MCP Server

generate_caption

Generate viral captions and hashtags from an image description, tailored for Instagram, TikTok, LinkedIn, or Twitter posts.

Instructions

Generate viral captions and hashtags tailored for Instagram, TikTok, or LinkedIn.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "imageDescription": "<imageDescription>"
    },
    "name": "generate_caption"
  }
}

Input Schema

Name	Required	Description	Default
`platform`	No	Platform.	instagram
`imageDescription`	Yes	Description of the image.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 24, 2026

TDQS

B3.1/5.0

Behavior2/5

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

With no annotations, the description carries the full behavioral burden. It says what is generated but omits whether credits are consumed, whether output is saved or listed in generations, moderation constraints, or any rate limits.

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?

A single front-loaded sentence with no wasted words. It states the action, output, and platform tailoring efficiently.

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 low-complexity two-parameter tool, the description is minimally sufficient to call correctly. However, it lacks return-format guidance (no output schema), misses the Twitter platform present in the schema, and does not address credit or persistence behavior.

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 baseline is 3. The description adds 'tailored for Instagram, TikTok, or LinkedIn' but does not explain the parameters beyond the schema, and notably omits 'twitter' even though the enum supports it.

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 states a clear verb and resource ('Generate viral captions and hashtags') and adds platform tailoring. It is distinguishable from siblings like generate_production_script or generate_content_calendar, though it does not explicitly name alternatives.

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?

It gives no guidance on when to use this tool versus alternatives such as improve_prompt or generate_content_calendar, nor any prerequisites or exclusions. The platform list implies context but does not explain selection criteria.

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