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vyavahare-kishor

ai-mcp-toolkit

by vyavahare-kishor
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a completely different function—concept explanation, code review, and web research—with no overlap in purpose or inputs. An agent would not confuse them.

    Naming Consistency3/5

    Two tools follow a clear verb_noun pattern (explain_concept, review_code_diff), but web_research reverses the order (noun_verb if 'research' is the verb) or could be read as noun_noun, breaking consistency.

    Tool Count3/5

    With only 3 tools, the server feels minimal for a general-purpose AI toolkit. While the count itself is acceptable, the scope is too narrow to be considered well-rounded.

    Completeness2/5

    The tools are a random collection with no domain coherence. Missing fundamental AI capabilities like text generation, summarization, or image analysis, making the surface severely incomplete for a toolkit.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 3 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

  • 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

  • Behavior2/5

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

    No annotations are provided, so the description must cover behavioral traits. It only mentions the output format (concise summary with key points) but does not disclose important behaviors like data freshness, source reliability, error handling, rate limits, or any side effects. For a data-fetching tool, this is insufficient.

    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 that efficiently conveys the action, input, and output. No extraneous words or redundancy.

    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 simplicity (one parameter, no annotations, has output schema), the description is largely complete. It could be slightly improved by hinting at the output structure or that results are drawn from live web data, but overall it provides sufficient context for an agent to understand the tool's basic function.

    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 input schema has only one parameter 'topic' with 0% description coverage. The description adds meaning by explaining that the tool searches for current information on a 'topic', clarifying the parameter's purpose beyond the schema's bare type definition.

    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 action (search the web) and the resource (current information on a topic) with a specific output (concise summary with key points). It distinguishes itself from sibling tools like 'explain_concept' and 'review_code_diff' by focusing on web search and summarization.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives, such as when to search the web versus using other research or explanation tools. No exclusions or prerequisites are mentioned.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It mentions using concrete analogies but lacks disclosure of any side effects, authorization needs, or limitations. Behavioral traits are minimally covered.

    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 a single concise sentence that front-loads the key action and purpose. However, it could be slightly more structured to include additional context without being verbose.

    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?

    For a tool with only two simple parameters and an output schema (not shown), the description adequately covers the main functionality. It might miss details about edge cases or format, but overall it is sufficient for an AI to understand usage.

    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?

    With 0% schema description coverage, the description compensates by indicating that 'audience' should specify background level and that the explanation uses analogies. This adds meaning beyond the schema's bare parameter names and types.

    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 verb 'explain', resource 'technical/AI concept', and specifies tailoring to audience background with concrete analogies. It distinctly differentiates from sibling tools like review_code_diff and web_research.

    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 use when explaining concepts to a specific audience, but it does not provide explicit guidance on when not to use or how it compares to alternatives. No when/when-not statements are included.

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

  • Behavior2/5

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

    No annotations are present, so the description must fully convey behavioral traits. It mentions 'structured, actionable feedback' but does not describe what structure, any limitations, or side effects. Lacks details on return format, performance, or error handling, leaving agents uninformed.

    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 two sentences, no wasted words. It is direct and front-loaded with the primary purpose, followed by usage instructions. Highly concise.

    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 simplicity (2 params, 1 required, output schema existing), the description covers the core usage adequately. It could mention handling of invalid diffs or output format, but the output schema likely fills that gap.

    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 0%, but the description adds meaning: 'Pass raw diff text' clarifies the diff parameter's content, and 'Optionally specify a focus area like 'security'' gives an example for the focus parameter. This compensates well for the missing schema descriptions.

    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 action ('Review a code diff') and the expected output ('structured, actionable feedback'). It is specific and distinguishes the tool from siblings (explain_concept, web_research) which have different purposes.

    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 instructs to 'Pass raw diff text' and optionally specify a focus area. This provides clear input guidance. While it doesn't explicitly state when to use vs. siblings, the tool's unique purpose (code review) makes it evident.

    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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  • Evaluate tool definition quality.

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