Skip to main content
Glama
cevatkerim

unsplash-mcp

by cevatkerim

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: random photos, keyword search, and download tracking. There is no overlap or confusion.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (get_random_photos, search_photos, track_download), making it easy to predict their function.

    Tool Count5/5

    Three tools is appropriate for the scope: retrieving photos (random or search) and tracking downloads. Not excessive or insufficient.

    Completeness4/5

    The set covers the core workflow of finding and downloading photos with required attribution tracking. Minor gap: no tool for retrieving a specific photo by ID, but the random and search tools cover most use cases.

  • Average 4/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

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

    No annotations provided, so description bears full burden. Mentions random nature and optional filters but lacks details on side effects, rate limits, or auth requirements. Adequate for a simple read tool but could be more informative.

    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?

    Three sentences, front-loaded with core function. Every sentence adds value: purpose, usage guidance, and differentiation. No wasted words.

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

    Completeness4/5

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

    Output schema exists, so return values are covered. Description explains randomness, optional keyword, and suitable use cases. Lacks details on error handling or count limits, but overall sufficient for intended use.

    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%, so baseline is 3. Description only adds that query is a keyword filter; other parameters (count, orientation, content_filter) are left to schema. No additional meaningful context beyond schema.

    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?

    Description clearly states verb 'Get random photos' and resource 'from Unsplash', with optional keyword filter. Distinguishes from sibling 'search_photos' by emphasizing variety and lack of specific intent.

    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 suggests use cases (hero images, backgrounds) and when to use (variety, no specific image). Implicitly contrasts with search_photos for specific needs, though no direct 'do not use when' clause.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries full burden. It adds one behavioral requirement: 'full attribution data that MUST be displayed.' However, it does not disclose other behaviors such as rate limits, pagination behavior, or error handling. The attribution info is useful but insufficient for full transparency.

    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 extremely concise: two sentences, no wasted words. The first sentence states the purpose, and the second provides usage context and a crucial requirement. It is front-loaded and efficient.

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

    Completeness4/5

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

    Given that an output schema exists, the description does not need to explain return values. It covers the main purpose and a key usage requirement (attribution). However, with 7 parameters and no annotations, it could briefly mention pagination or sorting behavior (already in schema but not highlighted). Still, it is reasonably complete for a search tool.

    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 schema already documents all 7 parameters thoroughly. The description adds no additional parameter semantics beyond what is in the schema. Baseline score of 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 clearly states 'Search for photos on Unsplash by keyword,' which is a specific verb+resource. It is easily distinguishable from sibling tools like get_random_photos (random selection) and track_download (tracking), even though no explicit differentiation is provided.

    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 provides clear context: 'Use this tool when you need to find photos for a specific topic or theme.' It also mentions the attribution requirement. However, it does not explicitly state when not to use this tool or how it compares to siblings, which would improve guidance.

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

  • Behavior3/5

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

    With no annotations provided, the description carries the burden of behavioral disclosure. It explains that the tool is mandatory for API compliance and tracks usage statistics, but does not detail side effects (e.g., whether it mutates state), rate limits, authorization needs, or return behavior. The requirement 'MUST call' is clear, but additional context on what exactly happens when called (e.g., API call logging) is lacking.

    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 highly concise with four sentences, each adding distinct value: requirement statement, when to call, why required, and when not to call. No redundancies or unnecessary details.

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

    Completeness4/5

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

    Given the tool is simple (1 required parameter, output schema present), the description adequately covers usage context, necessity, and timing. It does not explain return values, but the presence of an output schema mitigates that need. Minor gap: no mention of error handling or idempotency, but overall complete for the tool's purpose.

    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%, so the baseline is 3. The description adds no additional meaning to the 'photo_id' parameter beyond what the schema already provides ('The photo ID from a previous search_photos or get_random_photos result'). The description merely references this parameter context without enriching it.

    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 purpose: tracking photo downloads as required by Unsplash API guidelines. It specifies the action ('track a photo download'), the resource (photo), and distinguishes from sibling tools like search_photos and get_random_photos by noting when to call it ('AFTER the user confirms they want to download/use the image') and when not to ('not when just displaying search results').

    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 explicitly provides usage context: it must be called when a user downloads or saves a photo, and is required by Unsplash guidelines. It also states when NOT to use it ('not when just displaying search results'). However, it does not explicitly name alternative tools or explain why search tools are inappropriate for tracking, missing an opportunity for full sibling differentiation.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

unsplash-mcp MCP server

Copy to your README.md:

Score Badge

unsplash-mcp MCP server

Copy to your README.md:

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/cevatkerim/unsplash-mcp'

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