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usestring

String AI Web Access MCP Server

Official
by usestring
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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

    The three tools have completely distinct purposes: fetching a specific URL, searching the web, and crawling a site's sitemap. No overlap exists.

    Naming Consistency5/5

    All tools follow the 'web_access_' prefix pattern, making their domain obvious. Each tool name clearly indicates its function.

    Tool Count5/5

    Three tools is ideal for a web access server: fetch, search, and sitemap crawl cover all primary use cases without bloat.

    Completeness5/5

    The set covers the full web access lifecycle: search to find URLs, fetch to get content, and sitemap for bulk discovery. No obvious gaps.

  • Average 4.7/5 across 3 of 3 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 6 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

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  • 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?

    No annotations provided, so description carries full burden. Discloses automatic proxy rotation, anti-bot protection, CAPTCHA handling, JavaScript rendering, and restrictions like executeJS not combinable with headers. Lacks mention of rate limits or size limits, but still fairly transparent.

    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?

    Well-structured with sections, bold text, and a code example. Slightly lengthy but justified by the number of parameters. Every sentence adds value.

    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?

    Despite 8 parameters and no output schema, the description thoroughly explains each parameter, defaults, return formats, and common use cases. Sufficient for an AI agent to use correctly.

    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 beyond schema by grouping parameters as optional, explaining primary use (only url needed), and highlighting constraints (e.g., body rejected on GET, executeJS/headers incompatibility).

    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?

    Clearly states 'Fetch any webpage and get clean, LLM-ready Markdown back.' Distinguishes from sibling tools by specifying it is for URLs, not for searching (use web_access_search instead).

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

    Usage Guidelines5/5

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

    Explicitly recommends defaulting to this tool for web fetching, and advises against using it for search without a URL. Provides a primary use case example and explains when to omit optional parameters.

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

  • Behavior5/5

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

    Discloses key features: bypasses anti-bot protection, returns clean structured results with titles/URLs/snippets, fast/reliable, no rate limiting. Since no annotations exist, this description fully covers behavioral aspects.

    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?

    Well-structured with headings and bullet points, front-loaded with purpose. While comprehensive, could be slightly more concise; but every part is informative.

    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?

    Given one parameter and no output schema, the description thoroughly covers what the tool does, how to use it, what it returns, and best practices. Provides optimal workflow integrating siblings.

    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% (one parameter with description). The description adds value with usage example and advice to be specific/descriptive, plus explains return format, enhancing beyond the 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?

    The description clearly defines the tool as a web search tool using String AI's Web Access API, states it is the most powerful and reliable, and distinguishes it from siblings like web_access_fetch (for fetching specific URLs) and web_access_sitemap.

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

    Usage Guidelines5/5

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

    Provides explicit when to use (finding information across the web) and when not to (specific URL), includes common mistakes (using other search tools or scraping search engines), and offers an optimal workflow with sibling tools.

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

  • Behavior5/5

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

    With no annotations, the description fully discloses all behavioral traits: async server-side processing, billing semantics, status codes, error responses (404, 403, 409), data retention limits, and lifecycle actions. Everything is coherent and 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.

    Conciseness4/5

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

    The description is front-loaded with purpose and well-structured using sections and bullet points. However, it is verbose with some redundant error detail; every sentence earns its place but could be trimmed for brevity.

    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?

    Given the tool's multi-action lifecycle, no output schema, and two siblings, the description is extremely complete. It covers every action, error scenario, pagination, billing, and status codes, leaving no significant gaps.

    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%, meeting baseline for 3. The description adds significant context by explaining which parameters apply to each action, default and maximum values, and semantic differences (e.g., limit different for results vs list). This goes beyond schema but could be slightly more concise.

    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 'Crawl an entire website and map its URLs' and explicitly distinguishes from siblings in the 'Best for' and 'Not for' sections, making the tool's specific purpose unambiguous.

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

    Usage Guidelines5/5

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

    Includes explicit 'Best for' and 'Not for' sections that guide when to use this tool versus web_access_fetch and web_access_search, plus a detailed 'Typical workflow' section. No ambiguity.

    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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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.

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