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FadhliRajwaaRahmana

mcp-vision-image-fadhli

by FadhliRajwaaRahmana
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

mcp-vision-image-fadhli

MCP server untuk analisis gambar (image vision) menggunakan Gemini API — berjalan via stdio.

Fitur

  • analyze_image — analisis gambar dari path lokal atau URL online (PNG/JPG/WebP/GIF/BMP/SVG)

  • get_usage_stats — statistik pemakaian API (harian, per model, sisa limit)

Related MCP server: vision-mcp

Instalasi & Penggunaan

Jalankan langsung via npx (tanpa install):

GEMINI_API_KEY=xxx npx -y mcp-vision-image-fadhli

Setup di Claude Code

claude mcp add mcp-vision-image -s user -t stdio -e GEMINI_API_KEY=xxx -- npx -y mcp-vision-image-fadhli

Atau otomatis via claudecode-setup — wizard akan mendaftarkan server ini beserta MCP lain.

Catatan: GEMINI_API_KEY wajib di-set (env atau parameter apiKey). Dapatkan key gratis di https://aistudio.google.com/apikey

Penggunaan di Claude Code

Setelah terdaftar, minta bantuan dengan menyebut file/URL gambar:

analisis gambar ini: C:\path\ke\foto.png

Development

npm install
GEMINI_API_KEY=xxx npm test   # smoke test
npm start                     # jalankan sebagai stdio server

Lisensi

MIT

Available Tools

2 tools
analyze_imageA

Menganalisis dan mendeskripsikan gambar (file path lokal atau URL) menggunakan Gemini Vision AI.

ParametersJSON Schema
NameRequiredDescriptionDefault
modelNoModel Gemini yang digunakan. Default: "gemini-3.7-flash". Bisa di-override via env GEMINI_MODEL.
promptNoPertanyaan atau instruksi spesifik seputar gambar (misal: "Bacakan teks di gambar ini", "Apakah ada kucing?"). Default: deskripsi lengkap.
image_urlNoURL gambar online yang di-copy dari browser atau clipboard (misal: "https://example.com/foto.jpg" atau URL berakhiran .png/.jpg/.webp/.gif). Server akan mengunduh gambar dari URL tersebut.
image_pathNoPath file gambar lokal di sistem (misal: "C:\path\to\image.png" atau "./foto.jpg")

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals that the tool uses Gemini Vision AI and accepts local paths or URLs, but it does not disclose the return format, network dependency, file size limits, or privacy implications. An agent needs more context to anticipate side effects and output shape.

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?

The description is a single, front-loaded sentence with no unnecessary words. Every element—verb, resource, input types, and model provider—is useful and helps the agent quickly understand and select the tool.

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 moderately complex image analysis tool with no annotations and no output schema, the description covers the core capability and input forms but omits output format, supported file types, size limits, and network behavior. It is adequate for basic invocation but not fully complete for an agent that needs to anticipate results and constraints.

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 100% for all four parameters, so the schema already explains the model, prompt, image_url, and image_path in detail. The description's mention of 'file path lokal atau URL' loosely maps to the image_path and image_url parameters, but it adds no additional semantic meaning beyond what the schema already provides. Baseline 3 is appropriate.

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 uses a specific verb ('Menganalisis dan mendeskripsikan') and identifies the resource ('gambar' / image), explicitly naming the supported input types (local file path or URL) and the underlying model (Gemini Vision AI). This clearly distinguishes it from the sibling tool get_usage_stats, which has a completely different purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for image analysis and description by naming the input types, but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it reference the sibling tool as an alternative. The sibling is distinct enough that confusion is unlikely, but the description itself offers no usage rules beyond the obvious.

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

get_usage_statsA

Menampilkan statistik pemakaian API Gemini Vision (jumlah panggilan hari ini, total, sisa limit harian, per model, dan riwayat harian).

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. The verb 'Menampilkan' (displays) indicates a read-only operation, and the description clearly lists the types of data returned. It does not mention side effects, auth requirements, or rate limits, but for a simple stats display tool, this is adequate.

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?

The description is a single, well-structured sentence that front-loads the main purpose and then lists specific statistics. It is concise with no unnecessary words, making it easy for an agent to parse quickly.

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?

For a tool with no parameters and no output schema, the description fully specifies what the tool does and what data it returns. The sibling tool is clearly different, and the description is complete for its simplicity.

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?

The tool has 0 parameters, so the schema is non-informative. The description adds no parameter details because there are none to add. Baseline for 0 parameters is 4, and the description does not need to compensate for any missing schema coverage.

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 function: displaying usage statistics for the Gemini Vision API, listing specific metrics such as today's calls, total calls, remaining daily limit, per-model breakdown, and daily history. This distinguishes it from the sibling tool analyze_image, which is for image 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 usage context is implied by the description: use when needing API usage stats. It does not explicitly exclude other tools or provide alternatives, but the clear focus on usage statistics makes the use case obvious. No explicit 'when not to use' is given, but it is not necessary given the simplicity.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv1.3.0
    • First observedanalyze_image
    • First observedget_usage_stats
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TDQS

A4/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: analyze_image handles image analysis, while get_usage_stats reports API usage metrics. No overlap or ambiguity exists.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern: analyze_image and get_usage_stats. The naming is uniform and predictable.

Tool Count3/5

With only 2 tools, the server feels thin but is reasonable for a focused single-purpose image analysis service. It sits at the borderline of being too minimal.

Completeness5/5

For its stated purpose of image analysis via Gemini Vision, the server covers the core operation (analyze_image) and adds useful monitoring (get_usage_stats). No obvious missing operations within this narrow domain.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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