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konseki-official

Konseki MCP

Official
by konseki-official
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.3

  • Disambiguation5/5

    Each tool targets a distinct data type: analysis data, metadata, and supported symbols. There is no overlap in their purposes, making it clear which tool to use for a given retrieval task.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun_konseki pattern: 'get_konseki_analysis', 'get_konseki_metadata', and 'list_konseki_symbols'. The use of 'get' for single items and 'list' for collections is appropriate and predictable.

    Tool Count4/5

    With 3 tools, the server is slightly on the low end but still well-scoped for a focused API wrapper that provides read-only access to three core data types. Each tool serves a clear purpose without unnecessary bloat.

    Completeness4/5

    The tools cover the main retrieval operations for the Konseki API: analysis, metadata, and symbol listing. Minor gaps might include filtering or parameterized queries, but the set is complete for basic data access needs.

  • Average 3.4/5 across 3 of 3 tools scored. Lowest: 2.8/5.

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

    • No community issues in the last 6 months
    • 14 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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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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

  • Behavior1/5

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

    No annotations are provided, so the description must bear the full burden. It only states 'Fetch raw analysis JSON' but does not disclose behavioral traits such as whether the operation is read-only, destructive, requires authentication, or has rate limits. No additional behavioral context beyond the basic purpose.

    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 clear sentence with no extraneous text. It is efficiently front-loaded, though it could benefit from additional structure (e.g., noted parameter details).

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

    Completeness2/5

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

    Given the lack of annotations and output schema, the description is insufficient. It does not explain the return format, error conditions, or provide examples. For a tool with three required parameters and no additional metadata, more context is needed for an agent to use it effectively.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The description mentions the three parameters (symbol, exchange, lookback) but does not explain their meaning, constraints, or examples. The schema lacks descriptions (0% coverage), and the description adds minimal semantic value beyond naming the parameters.

    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 'Fetch raw analysis JSON for a symbol, exchange, and lookback', specifying the verb, resource, and key parameters. It distinguishes from sibling tools 'get_konseki_metadata' and 'list_konseki_symbols' by focusing on raw analysis data.

    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 on when to use this tool versus alternatives. The description does not mention the sibling tools or provide any context about when to prefer this over get_konseki_metadata or list_konseki_symbols.

    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?

    The description does not disclose any behavioral traits such as authentication needs, rate limits, or side effects. With no annotations, the description carries the full burden but provides minimal transparency.

    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 sentence with no wasted words. It is appropriately concise for a parameter-free tool, though it could be slightly more informative.

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

    Completeness2/5

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

    Given no parameters, no output schema, and no annotations, the description lacks detail about the metadata content, potential authentication requirements, or how to handle the response. The tool is simple but the description is incomplete for an agent to fully understand its usage.

    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 no parameters, and the description adds no additional meaning beyond the schema. Coverage is 100%, so a baseline score of 3 is appropriate.

    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 it fetches raw metadata JSON from the Konseki public API, using a specific verb and resource. It distinguishes from siblings like get_konseki_analysis and list_konseki_symbols by indicating it returns raw metadata.

    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 its siblings. There is no mention of use cases or alternatives, leaving the agent without context for selection.

    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?

    Discloses it fetches raw JSON, which is sufficient for a simple read operation. No annotations exist, but the description clearly communicates the action and output format. Could mention if it's cached or has rate limits, but not required.

    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?

    Single, front-loaded sentence that covers the essential information without padding. Every word adds value.

    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 no output schema, the description specifies the returned content ('symbols JSON') which is adequate for a simple list endpoint. Could include example or more detail, but sufficient for tool selection.

    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?

    No parameters; schema coverage is 100% (trivially). Description doesn't need to add parameter details, so baseline 4 is appropriate.

    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 it fetches raw supported symbols JSON from the Konseki public API. The verb 'Fetch' combined with 'list' indicates a read operation, and it distinguishes from siblings like get_konseki_analysis and get_konseki_metadata.

    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?

    Lacks explicit guidance on when to use this tool over alternatives. The description implies it's for listing symbols, but no direct comparison with siblings or note on prerequisites.

    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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  • 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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