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icck

Toy MCP Server

by icck
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Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one counts letter occurrences in a word, while the other generates UUIDv7 identifiers. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool based on the task.

    Naming Consistency3/5

    The tool names follow a verb_noun pattern (count_letters, generate_uuid7s), which is consistent. However, the naming is slightly inconsistent in detail: 'count_letters' uses plural 'letters' while 'generate_uuid7s' uses an abbreviated 'uuid7s' instead of a full noun like 'uuids', but this is minor and does not hinder readability.

    Tool Count2/5

    With only 2 tools, the server feels thin and under-scoped for a general-purpose 'Toy MCP Server'. This limited set may not adequately cover typical toy or utility domains, suggesting it could benefit from additional tools to provide more comprehensive functionality.

    Completeness2/5

    Given the server's name implies a toy or utility domain, the tool set is severely incomplete. It lacks basic operations like string manipulation, random generation, or other common utility functions, leaving significant gaps that would limit an agent's ability to handle diverse tasks within this domain.

  • Average 3.2/5 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

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  • This repository includes a README.md file.

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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool does but lacks critical details: it doesn't specify if the operation is idempotent, has side effects, requires permissions, or involves rate limits. For a generation tool with zero annotation coverage, this is a significant gap in transparency.

    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 extremely concise and front-loaded, consisting of a single sentence that directly states the tool's function and key parameter detail. There is no wasted language, making it efficient and easy to parse for an agent.

    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 the tool's low complexity (one parameter, no nested objects) and the presence of an output schema (which handles return values), the description is minimally adequate. However, it lacks context on behavioral aspects like idempotency or side effects, which are important for a generation tool without annotations, leaving some gaps in completeness.

    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 description adds minimal meaning beyond the input schema, which has 100% coverage for the single parameter 'count'. It mentions the default value (1) and implies the parameter controls quantity, but the schema already documents this thoroughly. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't add significant value.

    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 purpose: generating UUIDv7 identifiers with a specified quantity. It uses specific verbs ('generates') and resources ('UUIDv7'), but does not distinguish from the sibling tool 'count_letters', which is unrelated. The description avoids tautology by explaining functionality beyond the name.

    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 or in what context it is appropriate. It mentions a default count but offers no usage scenarios, prerequisites, or exclusions. This leaves the agent without direction on application.

    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 provided, the description carries the full burden of behavioral disclosure. It describes the basic function but lacks information about error handling, case sensitivity, performance characteristics, or what happens with invalid inputs. This is inadequate for a tool with no annotation coverage.

    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, clear sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized and front-loaded, making it highly efficient.

    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 the tool's simplicity (2 parameters, 100% schema coverage, output schema exists), the description is reasonably complete for its purpose. The output schema handles return values, so the description doesn't need to explain them. However, it could benefit from more behavioral context given the lack of annotations.

    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 schema description coverage is 100%, with both parameters clearly documented in the schema. The description adds no additional parameter semantics beyond what's already in the schema, so it meets the baseline of 3 for high schema coverage without adding value.

    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 purpose with specific verbs ('count') and resources ('number of times a letter appears in a word'), making it immediately understandable. However, it doesn't differentiate from the sibling tool 'generate_uuid7s' (which is unrelated), so it doesn't fully address sibling distinction.

    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 or in what context it's appropriate. It states what the tool does but offers no usage instructions, prerequisites, or exclusions.

    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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