Skip to main content
Glama
hffmnnj

openai_websearch

by hffmnnj
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.2.0

  • Disambiguation5/5

    image_search and web_search are clearly distinct: one retrieves images, the other general web results. The names and descriptions leave no ambiguity about which tool to use for a given query.

    Naming Consistency5/5

    Both tools follow the identical <object>_search pattern, creating a predictable and consistent naming convention that aligns with their functions.

    Tool Count3/5

    With only two tools, the server feels minimal but covers the two primary search needs (general and image). It is borderline because the scope could reasonably include other search types.

    Completeness4/5

    The tool surface covers general web search and image search well, but is missing potentially common search types like news or video. Overall, the core search functionality is not left with dead ends.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 10 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

  • Behavior4/5

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

    With no annotations, the description carries the transparency burden and does well by disclosing that search is server-side, powered by a subscription, and that context_size affects speed/quality. It also promises real URLs and citations, giving useful behavioral expectations.

    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 concise: two short sentences plus a bullet for context_size, with the core purpose front-loaded. Every sentence adds unique value without redundancy or unnecessary detail.

    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?

    Despite having no output schema, the description explains key return characteristics (clean results, real URLs, citations). It covers the essential behavior and parameter variants for a simple search tool, though it omits failure modes or edge cases like empty results.

    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 description coverage is 100%, providing a baseline of 3. The description adds extra meaning to context_size by mapping values to cost/speed trade-offs ('fast/cheap' vs 'thorough/deep'), going beyond the schema's basic 'How much web context to retrieve'.

    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 uses the specific verb 'Search the web' and names the resource as the web, clearly indicating the tool's function. It also mentions returning URLs and citations, which helps distinguish it from the sibling tool 'image_search', though it does not explicitly contrast them.

    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 provides context about server-side execution and subscription reliance, plus context_size options, but it does not explicitly say when to use this tool over image_search or when not to use it. Usage is implied rather than explicitly guided.

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

  • Behavior4/5

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

    With no annotations, the description carries the full burden of behavioral context. It usefully discloses that the tool performs a web search and returns real URLs, not AI-generated images, and that context_size controls depth. It does not mention rate limits, pagination, or other operational details, but for a read-only search tool this is sufficient disclosure.

    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 compact, with the main purpose in the first sentence and two bullet points that add practical details. No filler or repetition, and information is front-loaded for quick scanning.

    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?

    Since there is no output schema, the description responsibly explains what the tool returns (image URLs, titles, source pages). It also gives a key behavioral detail (real images, not AI-generated). It does not discuss error cases or limitations, but for a simple search tool with only three parameters, the description is sufficiently 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?

    The input schema already has 100% parameter description coverage, including 'Search depth' for context_size. The description's note about contextSize controlling search depth paraphrases the schema without adding new meaning. It does not clarify the parameter list beyond what is already structured.

    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 the web for images' with a specific verb and resource. It also lists return types (image URLs, titles, source pages), and explicitly distinguishes itself from the sibling web_search by focusing on images rather than general web 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 clearly implies a use case (searching for images, not text) and notes that it returns real image URLs rather than AI-generated ones. However, it does not explicitly state when to prefer this over web_search or any exclusions, so it stops short of full usage guidance.

    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

openai_websearch MCP server

Copy to your README.md:

Score Badge

openai_websearch 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/hffmnnj/openai_websearch'

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