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CSOAI-ORG

Proofof AI MCP

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detect_deepfake_image

Analyze image metadata to detect AI-generated signatures. Checks EXIF, PNG chunks, and compression patterns for known AI tool markers.

Instructions

Check image metadata for AI generation signatures.

Performs lightweight metadata-based analysis (EXIF, PNG chunks, compression patterns) without requiring ML inference. Checks for known AI tool signatures, suspicious metadata patterns, and generation parameters.

Args: image_base64: Base64-encoded image data. image_path: Local file path to image (alternative to base64).

Returns: Detection results with metadata findings and risk assessment.

Behavior: This tool generates structured output without modifying external systems. Output is deterministic for identical inputs. No side effects. Free tier: 10/day rate limit. Pro tier: unlimited. No authentication required for basic usage.

When to use: Use this tool when you need structured analysis or classification of inputs against established frameworks or standards.

When NOT to use: Not suitable for real-time production decision-making without human review of results. Behavioral Transparency: - Side Effects: This tool is read-only and produces no side effects. It does not modify any external state, databases, or files. All output is computed in-memory and returned directly to the caller. - Authentication: No authentication required for basic usage. Pro/Enterprise tiers require a valid MEOK API key passed via the MEOK_API_KEY environment variable. - Rate Limits: Free tier: 10 calls/day. Pro tier: unlimited. Rate limit headers are included in responses (X-RateLimit-Remaining, X-RateLimit-Reset). - Error Handling: Returns structured error objects with 'error' key on failure. Never raises unhandled exceptions. Invalid inputs return descriptive validation errors. - Idempotency: Fully idempotent — calling with the same inputs always produces the same output. Safe to retry on timeout or transient failure. - Data Privacy: No input data is stored, logged, or transmitted to external services. All processing happens locally within the MCP server process.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyNo
image_pathNo
image_base64No
Behavior5/5

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

With no annotations, the description carries full burden and delivers comprehensive transparency: side effects (read-only), authentication (basic no auth, pro requires API key), rate limits (10/day free, unlimited pro), error handling (structured error objects), idempotency (fully deterministic), and data privacy (no data stored). No contradictions.

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

Conciseness3/5

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

The description is structured but verbose and contains redundancy (e.g., 'Behavior' paragraph is repeated in 'Behavioral Transparency' section). Could be more concise while retaining key information.

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?

No output schema is provided; the return value is vaguely described as 'Detection results with metadata findings and risk assessment.' Missing details on the output structure, and one parameter (api_key) is not explained. Adequate but not complete.

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 coverage is 0%, so description must compensate. It explains image_base64 and image_path in 'Args', but omits the api_key parameter entirely. The behavioral transparency mentions an API key for pro tiers but does not explicitly link it to the parameter. Partial coverage of 3 parameters.

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 starts with a specific verb+resource: 'Check image metadata for AI generation signatures.' It clearly distinguishes the tool from siblings like check_provenance and verify_certificate by focusing on deepfake detection via metadata analysis.

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 includes explicit 'When to use' and 'When NOT to use' sections, providing clear context and exclusions. However, it does not differentiate among sibling tools or offer specific alternatives.

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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