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vertical-mcp

ntis-mcp

by vertical-mcp
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct entity type in the R&D domain: announcements (funding calls), programs (umbrella programs), and projects (individual projects). No overlap in purpose, and the descriptions clearly differentiate them.

    Naming Consistency5/5

    All tools follow the exact pattern 'search_rnd_<entity>', using snake_case consistently. The prefix 'search_rnd_' is uniform, and the entity names ('announcements', 'programs', 'projects') are clear and parallel.

    Tool Count4/5

    Three tools is slightly low but appropriate given the focused domain of Korean national R&D data. Each tool covers a core entity, and no unnecessary tools exist. A few more detail-oriented tools could be added, but the current count is reasonable.

    Completeness4/5

    The tools cover the primary searchable entities in the R&D funding lifecycle: announcements (calls), programs, and projects. Missing are detail retrieval tools for specific entities, but the search tools return rich metadata, so agents can work within this surface.

  • Average 3.6/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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 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.

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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

  • Behavior3/5

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

    Given no annotations, the description partially fulfills behavioral transparency by listing return fields and stating no API key required. However, it lacks disclosure of rate limits, data freshness, or any side effects.

    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 concise, with front-loaded purpose and a list of returned fields. However, it could be more structured by separating input and output details.

    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?

    Given no output schema, the description covers return fields acceptably. But absence of input parameter descriptions and usage guidelines makes it incomplete for an agent to use 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?

    Schema description coverage is 0%. The description does not clarify the input parameters (query, page, page_size) beyond their names and defaults. It does not explain query syntax, valid values, or how pagination works.

    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 searches South Korea's national R&D projects via NTIS, specifies the data source and fields returned, and distinguishes from sibling tools (announcements, programs) by focusing on projects.

    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 usage for searching R&D projects but does not provide explicit guidance on when to use this tool vs alternatives or when not to use it. No exclusions or context for optimal use.

    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, the description must disclose behavioral traits. It mentions derived status and data source but does not cover pagination behavior, authentication needs, or how 'recent' is defined.

    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 with two sentences, front-loaded with the verb and resource, and no unnecessary information. Every sentence adds value.

    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?

    Without an output schema, the description explains return fields adequately. However, it lacks parameter descriptions and comparison with sibling tools, making it moderately complete given the tool's complexity.

    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?

    Schema description coverage is 0%, and the description does not elaborate on any parameters (query, page, page_size). It adds no meaning beyond the parameter names, leaving agents to guess semantics.

    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 searches R&D funding calls (announcements) across Korean ministries, specifies the data source (ntis.go.kr), and lists the returned fields. It distinguishes from sibling tools (programs and projects) by focusing on calls.

    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 the tool is for finding open or recent announcements, but provides no explicit guidance on when to use this tool versus siblings (search_rnd_programs, search_rnd_projects), nor when not to use it.

    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 provided, so description carries full burden. It lists return fields and data source but does not disclose side effects, authentication needs, or rate limits. For a read-only search, safety is implied but not explicit.

    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?

    Two sentences with clear structure: first states purpose and resource, second lists return fields and data source. No unnecessary words.

    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?

    Reasonably complete for a simple search tool, covering purpose, output fields, and data source. However, missing parameter explanations and explicit usage guidance reduce completeness.

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

    Parameters1/5

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

    Schema description coverage is 0% and the description does not explain any parameters. It omits explanation of 'query', 'page', or 'page_size' beyond their schema names, failing to compensate for the gap.

    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 searches Korean national R&D programs, explains what a program is (umbrella for projects), lists return fields, and distinguishes from siblings by naming search_rnd_announcements and search_rnd_projects.

    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?

    Implicitly guides usage by differentiating from siblings (projects vs announcements), but lacks explicit when-to-use or when-not-to-use instructions.

    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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