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pyrfor

fsnb-mcp

by pyrfor
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JavaScript

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 has a clearly distinct purpose: retrieve a specific norm by code, perform full-text search, and validate code existence. No overlap or ambiguity.

    Naming Consistency4/5

    All tools use the 'fsnb_' prefix and underscore style, but the order of verb and object varies: 'rate_get' (object+verb), 'search' (verb-only), 'validate_code' (verb+object). Mostly consistent but not perfectly uniform.

    Tool Count4/5

    3 tools is minimal but well-scoped for a norm lookup service: search, get by code, and validate. Not too many or too few for the intended functionality.

    Completeness4/5

    Covers essential read operations: retrieval, search, and existence check. Minor gaps like pagination or listing all codes are absent but not critical for the domain.

  • Average 3.4/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 9 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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

  • Behavior2/5

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

    With no annotations, the description carries full burden for behavioral disclosure. It only states 'get norm by code' without mentioning required permissions, error behavior (e.g., not found), response format, or side effects. 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.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is very concise (one sentence) and front-loaded with the action. However, the parentheses add format examples, which are helpful. No waste, but could be slightly more structured (e.g., bullet format examples).

    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 one parameter and no output schema, the description adequately explains how to invoke the tool but leaves uncertainty about what a 'norm' contains or how errors manifest. Complete enough for basic use but lacks output context.

    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 coverage is 100%, and the description repeats the code format example already present in the schema. It adds minimal extra meaning beyond what the schema provides, so baseline 3 is appropriate.

    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 retrieves a GESN norm by code, with a specific format example. It distinguishes from siblings implicitly (fsnb_search searches, fsnb_validate_code validates), but could be more explicit about its unique role.

    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?

    Usage is implied: you need a norm code. However, no explicit guidance on when to use this over siblings (e.g., fsnb_search) or conditions (e.g., code must be valid). Lacks when-not or alternatives.

    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?

    With no annotations, the description alone must disclose behavior. It only says 'check existence' but omits what happens (e.g., return boolean, throw error), side effects, authentication needs, or output format.

    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 with no wasted words. It starts with the verb, but could benefit from additional structure. Still efficient and front-loaded.

    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 with one parameter and no output schema. However, the description fails to explain the return behavior or how to interpret the validation result, which is essential for a validation tool.

    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 coverage is 100% and already describes the parameter as 'код нормы для валидации'. The description adds no new meaning beyond restating the schema, so baseline 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 the action ('Проверить существование') and the resource ('кода нормы в базе'), distinguishing it from sibling tools like fsnb_rate_get (get rate) and fsnb_search (search). It is specific and unambiguous.

    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. It does not mention context, prerequisites, or situations where validation is appropriate.

    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 carries the full burden. It states the tool performs full-text search using FTS5, but fails to disclose behaviors like read-only nature, performance, or authorization requirements. The description is adequate but lacks depth.

    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, well-formed sentence that efficiently conveys the tool's purpose with relevant technical detail (FTS5). It is front-loaded and contains no extraneous information.

    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?

    The description omits details about the output format or fields, which is critical since no output schema is provided. It also assumes domain knowledge (GESN norms) without clarification. For a search tool, expected return structure should be mentioned.

    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 description coverage is 100%, providing baseline clarity. The tool description adds context that the query searches within norms by name and composition, enhancing understanding beyond the schema's generic 'search query' label. This modest extra meaning earns a 4.

    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 specifies a full-text search of GESN norms by name and composition, clearly identifying the action (search), resource (norms), and scope (by name and composition). This distinguishes it from siblings like fsnb_rate_get and fsnb_validate_code.

    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 does not explicitly state when to use this tool vs alternatives. While the sibling tools have distinct purposes (get a rate, validate a code), usage guidelines are only implied and not articulated, leaving room for confusion.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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