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hexonal

grok-import-mcp

by hexonal
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a distinct verb: batch_add_tokens adds, batch_delete_tokens deletes, enable_nsfw toggles a flag, and list_tokens lists. The descriptions clarify scope (specific pool vs. all pools), so there is no overlap or ambiguity.

    Naming Consistency4/5

    Three tools use a clear verb_noun pattern (list_tokens, enable_nsfw, batch_add_tokens), but the 'batch_' prefix on add/delete creates a slight inconsistency. Still, all names are snake_case and readable, with predictable structure.

    Tool Count5/5

    Four tools is perfectly scoped for a focused token management utility. Each tool handles a distinct operation without unnecessary bloat or redundancy.

    Completeness2/5

    The surface is missing key lifecycle operations: delete only works across all pools (not a specific pool), there is no disable_nsfw counterpart, and no way to update token properties besides NSFW. These are significant gaps for a token management tool.

  • Average 3.8/5 across 4 of 4 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

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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 provided, the description carries the full burden. It only states the basic action and does not disclose side effects, idempotency, error handling, or required permissions. Very limited behavioral detail.

    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, focused sentence with no filler. It is concise and front-loaded, stating exactly what the tool does.

    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?

    For a simple batch operation with two parameters and an output schema, the description is adequate but lacks contextual details such as prerequisites or behavior under failure. It does not explain when to use this over alternatives, but the core function is clear.

    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 description coverage is 100% for both parameters, so the description does not need to add much. The phrase 'specific pool' aligns with pool_name but adds no new meaning beyond the schema.

    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 (batch add), the resource (tokens), and the target (a specific pool). It distinguishes the tool from siblings like batch_delete_tokens and list_tokens.

    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: use this tool when you need to add multiple tokens to a pool. However, there is no explicit guidance on when not to use it or alternatives (e.g., for deletions).

    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 the full burden. It fails to disclose that deletion is destructive, irreversible, or what happens when tokens are missing. This is a significant gap for a delete operation.

    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 sentence of 18 words, front-loaded with the action and scope, with no wasted 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?

    The tool is simple with one parameter and an output schema exists, so return values need not be explained. However, the lack of behavioral transparency (destructive nature) leaves gaps for an agent selecting this 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% for the 'tokens' parameter, which is described. The description adds no extra semantic detail beyond the schema, so the baseline 3 applies.

    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 and scope: 'Batch delete tokens from all pools.' This distinguishes it from siblings like batch_add_tokens (opposite operation) and list_tokens (read-only).

    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 phrase 'from all pools' provides context, implying it operates across all pools, but it does not explicitly state when to use this tool versus alternatives such as batch_add_tokens or clarify exclusions.

    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 are provided, so the description carries the full burden. It discloses the key behavioral edge case (empty/None enables for all tokens) but does not mention whether the operation is idempotent, reversible, or if any permissions are required. For a simple enable action, this is adequate but not rich.

    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-structured sentence that front-loads the action and covers the critical edge case. Every word earns its place with no unnecessary repetition 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?

    The tool is simple with one optional parameter, the schema fully documents that parameter, and an output schema exists so return values are already specified. The description covers the primary behavior and the all-tokens case, leaving only minor gaps such as whether the operation is persistent or scoped to a session.

    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% because the schema and description both explain the 'empty or None = all tokens' behavior. The description adds no additional semantic detail beyond what the schema already documents, so the baseline 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 the tool enables NSFW for specified tokens, with a specific verb and resource. It distinguishes from sibling tools (add/delete/list) by focusing on the enable operation and explicitly handling the all-tokens edge case.

    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 when NSFW needs to be enabled, and the empty/None behavior is a useful nuance, but it does not explicitly state when to use this tool versus alternatives like batch_add_tokens or list_tokens. No alternative names are mentioned.

    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?

    No annotations are provided, so the description carries the transparency burden. It discloses the key conditional behavior (empty pool_name means all pools) and implies a read-only operation via 'List'. For a simple listing tool, this is sufficient, though it doesn't discuss permissions or performance.

    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 sentence, front-loaded with the action and resource, and includes the conditional behavior concisely. No wasted words.

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

    Completeness5/5

    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 an output schema. The description covers the core functionality and the optional parameter's effect. Return values are handled by the output schema, so no further explanation is needed.

    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 adds no new meaning beyond what the schema already states (pool_name description is essentially identical). The description restates the parameter semantics without further elaboration.

    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 ('List tokens') and the resource scope ('in a specific pool, or all pools if pool_name is empty'). It distinguishes itself from sibling tools, which are all mutations (add/delete/enable).

    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?

    Provides clear context for when to use the tool: to list tokens within a specific pool or globally. It doesn't explicitly mention alternatives or exclusions, but the sibling tools are obviously different operations, making the usage context clear.

    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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