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IlyesTal

akyn-ai

by IlyesTal
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.15

  • Disambiguation5/5

    The two tools have clearly distinct purposes: query retrieves context from the knowledge base, while list_sources enumerates indexed sources. No overlap or ambiguity exists between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: 'query' and 'list_sources'. The naming is predictable and aligned with the server's domain.

    Tool Count3/5

    With only two tools, the set feels thin for a knowledge base server, but it covers the core query and source-listing operations. It is on the borderline of being too minimal but not egregiously so.

    Completeness3/5

    The server provides only read-oriented operations (query and list_sources). Missing source management capabilities such as adding, updating, or deleting sources represent notable gaps for a complete knowledge base lifecycle, though the core query functionality is present.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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.

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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?

    With no annotations, the description carries the full burden. It states a basic read action but does not disclose behavior such as pagination, sorting, return format, or any limits. It is a minimal description.

    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 concise sentence that directly conveys the tool's purpose without redundant or filler content.

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

    Completeness3/5

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

    The tool is simple (no parameters, no output schema), so the description covers the basic scope. However, it lacks any indication of what the output looks like or any additional context that would help the agent fully understand the tool's behavior.

    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 tool has zero parameters, so the baseline is 4. The description does not need to add parameter details, and it avoids unnecessary information.

    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 function: listing all sources (documents, URLs) indexed in the Knowledge Base. It uses a specific verb and resource, but does not explicitly distinguish itself from the sibling tool 'query'.

    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?

    No guidance is provided on when to use this tool versus the 'query' tool. There is no mention of scenarios, prerequisites, or alternatives.

    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 full burden of behavioral disclosure. It states that the tool 'Returns relevant context chunks from the indexed content,' which informs the agent of the return behavior. However, it does not mention potential limitations, error handling, or whether the operation is strictly read-only. For a simple query tool this is adequate but not richly transparent.

    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 long and every word earns its place. It front-loads the core action and resource, then immediately states the return value. There is no redundant information or filler.

    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 a single parameter and no output schema, the description covers the essential aspects: what it does, what input it takes, and what it returns. It lacks detail on response size or potential pagination, but given the simplicity of the tool, the description is reasonably complete.

    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 schema already provides a clear description for the 'question' parameter ('The question to search for in the knowledge base'). The tool description adds the nuance that this is a 'natural language question,' clarifying that the agent can formulate queries in everyday language rather than requiring specific keywords. This adds meaning beyond the schema description.

    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 function: 'Query the Knowledge Base knowledge base with a natural language question.' It specifies the verb (Query), the resource (Knowledge Base), and the expected output (relevant context chunks). This differentiates it from the sibling tool 'list_sources' which lists sources rather than performing semantic queries.

    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 the tool: when you have a natural language question about the knowledge base. It does not explicitly mention alternatives or when not to use it, but the use case is evident from the phrasing. For a single-purpose query tool with a sibling that lists sources, this level of guidance is sufficient.

    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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