Skip to main content
Glama

verify_image

Read-only

Verify a photo's authenticity by checking capture time, device, and signs of editing or AI generation. Returns a verdict and confidence score using forensic metadata and pixel analysis.

Instructions

Verify whether a photo is authentic: when it was captured, on what device, and whether it shows signs of editing or AI generation. Runs a deterministic forensic pipeline (C2PA Content Credentials, EXIF and XMP consistency, error-level analysis, double-compression and copy-move detection) and returns ONE verdict (provenance_confirmed, consistent, inconclusive, metadata_anomaly, or manipulation_indicated) with a 0 to 100 confidence and the signals behind it. Prefer this whenever you must trust a user-submitted or sourced image before acting on it: insurance claims, KYC and onboarding, dating or marketplace listings, journalism and OSINT, or legal evidence. Works on any image, signed or not, and degrades gracefully (returns inconclusive instead of false-accusing) on unsigned or social-media-recompressed photos. Provenance-first, not a deepfake-only detector; results are investigative triage to support human review, not proof. Provide exactly one of url, file_path, or image_base64.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoA publicly reachable image URL; the server fetches it.
file_pathNoAbsolute path to a local image file to verify.
image_base64NoBase64-encoded image bytes (no data: prefix).
Behavior5/5

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

Annotations (readOnlyHint, openWorldHint) are supplemented with rich detail: deterministic pipeline, specific checks, verdict types, confidence, graceful degradation, and caution that results are investigative triage not proof. No contradiction.

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?

Dense but effective; each sentence adds value. Could be slightly more concise, but structure is logical: summary, methods, usage, output, caveats, input requirement.

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?

No output schema, but description fully explains output: verdict enum, confidence range, and signals. Input parameters fully described with exclusivity rule. Graceful degradation noted. Complete for agent invocation.

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%, but description adds constraint 'Provide exactly one of url, file_path, or image_base64' which is absent from schema. This extra guidance earns a score above baseline 3.

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?

States specific verb+resource: 'Verify whether a photo is authentic', listing capture time, device, and editing signs. Distinguishes itself as a forensic pipeline with multiple techniques. No sibling ambiguity.

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 says when to use: 'Prefer this whenever you must trust a user-submitted or sourced image' and lists concrete domains (insurance, KYC, etc.). No explicit when-not or alternatives, but clear context.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/beeswaxpat/chronoverify-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server