Skip to main content
Glama
seekr-sh

MCP Seekr Server

by seekr-sh
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: seekr_query performs web searches to find URLs and information, while seekr_prism extracts detailed content from specific URLs. There is no overlap or ambiguity between querying for information and extracting content from a given URL.

    Naming Consistency5/5

    Both tools follow a consistent 'seekr_' prefix pattern with descriptive suffixes (query and prism). This naming convention is uniform and predictable, making it easy to understand the tool's function from its name alone.

    Tool Count3/5

    With only two tools, the server feels thin for a web search and content extraction domain. While the tools cover core workflows (search and extract), the scope might benefit from additional tools for tasks like filtering results, managing search history, or handling different content types.

    Completeness4/5

    The tool set covers the essential workflow of querying for information and extracting detailed content from URLs, with clear guidance on how to use them together. A minor gap is the lack of tools for advanced search parameters or content processing, but agents can effectively perform core tasks with the provided tools.

  • Average 3.9/5 across 2 of 2 tools scored.

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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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 are provided, so the description carries the full burden. It discloses the tool's behavior as extracting 'detailed text content' and 'full content' from URLs, which is useful. However, it lacks information on potential limitations (e.g., rate limits, authentication needs, error handling for invalid URLs, or content types). The description doesn't contradict annotations, but it could be more comprehensive given the absence of annotations.

    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 and well-structured, consisting of two sentences. The first sentence states the purpose clearly, and the second provides usage guidelines. There is no wasted text, and it is front-loaded with essential information, making it efficient and easy to understand.

    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's complexity (simple single-parameter operation), high schema coverage (100%), and the presence of an output schema (implied by context signals), the description is reasonably complete. It covers purpose and usage context adequately. However, it could improve by addressing behavioral aspects like error cases or performance, especially since no annotations are provided to fill those gaps.

    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 has 100% description coverage, with the 'url' parameter well-documented as a 'Valid HTTP/HTTPS URL to prism and extract text content from.' The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

    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 clearly states the tool's purpose: 'Prism and extract detailed text content from a specific webpage URL.' It uses specific verbs ('prism and extract') and identifies the resource ('webpage URL'). However, it doesn't explicitly differentiate from its sibling tool 'seekr_query' beyond mentioning 'query results' and 'query snippets,' which is implied but not direct.

    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 for when to use this tool: 'Use this tool to get full content from URLs found in query results. This provides more detailed information than query snippets alone.' It implies an alternative (query snippets from 'seekr_query') and suggests using this for deeper content extraction. However, it doesn't explicitly state when not to use it or name the sibling tool directly.

    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 the full burden. It mentions that the tool performs web queries and finds current information, which implies it's a read-only operation without destructive effects. However, it lacks details on rate limits, authentication needs, or specific behavioral traits like pagination or error handling. The description adds basic context but doesn't fully compensate for the absence of annotations.

    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 appropriately sized and front-loaded: it starts with the core purpose, lists usage scenarios, and ends with a clear call-to-action for the sibling tool. Every sentence adds value without redundancy, making it efficient and well-structured.

    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's complexity (a web query tool with 2 parameters), the description provides good context on usage and sibling tool relationship. Since an output schema exists, the description doesn't need to explain return values. However, with no annotations, it could benefit from more behavioral details like rate limits or error handling, slightly reducing completeness.

    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 both parameters (query and num) with descriptions. The description doesn't add any additional meaning or semantics beyond what the schema provides, such as query formatting examples or result interpretation. This meets the baseline of 3 when schema coverage is high.

    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 clearly states the tool's purpose: 'Query for information using Google query engine' and 'performs web queries to find current information, news, and diverse content.' It specifies the verb (query) and resource (web information via Google). However, it doesn't explicitly differentiate from its sibling tool seekr_prism beyond mentioning their relationship, which slightly reduces clarity.

    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?

    The description provides explicit usage guidelines: 'Use this for: current events, news, product information, troubleshooting, recent developments, and real-time information.' It also specifies when to use an alternative: 'After getting query results, use seekr_prism to get detailed content from specific URLs.' This clearly distinguishes when to use this tool versus its sibling.

    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

mcp-server-seekr MCP server

Copy to your README.md:

Score Badge

mcp-server-seekr 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/seekr-sh/mcp-server-seekr'

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