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NimbleBrainInc

MCP Echo Service

by NimbleBrainInc
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.9

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: echo_json handles structured JSON with analysis, echo_message handles plain text with optional formatting, and echo_with_delay adds a timing component. There is no ambiguity or overlap between the tools.

    Naming Consistency5/5

    All tool names follow a consistent 'echo_' prefix with snake_case, and each name clearly indicates its function (json, message, with_delay). The pattern is predictable and uniform.

    Tool Count5/5

    With 3 tools, the server is well-scoped for an echo service. Each tool provides a distinct mode of echoing, covering the basic needs without being overly numerous or sparse.

    Completeness5/5

    The tool surface covers the core use cases for an echo service: plain text, JSON, and delayed echoing. There are no obvious missing operations given the server's simple purpose.

  • Average 4/5 across 3 of 3 tools scored. Lowest: 3.1/5.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 2 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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 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 present, so the description must disclose behavioral traits. It mentions validation and analysis but does not specify what those entail, nor does it state whether the tool is read-only or has 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 brief with a single sentence and an Args/Returns section. It is front-loaded with the primary purpose, though the Args section adds minimal 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?

    Given the simple single-parameter tool and the existence of an output schema, the description is adequate but lacks details on validation/analysis specifics. It does not explain the output format beyond 'Echo response with data analysis.'

    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%, yet the description only restates the parameter name and type from the schema ('The JSON data to echo back'). It adds no additional semantic detail about structure or constraints.

    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 echoes back structured JSON data with validation and analysis. It distinguishes itself from sibling tools 'echo_message' and 'echo_with_delay' by focusing on JSON handling.

    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 alternatives. The description does not mention context, prerequisites, or exclusions.

    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?

    With no annotations, the description carries the full burden. It discloses the simulated delay, max duration (5 seconds), and return of timing information. This is transparent for a simple tool.

    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 and well-structured with clear sections (Args, Returns). Every sentence serves a purpose with 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?

    Given the tool's simplicity and presence of an output schema, the description covers input parameters, behavior, and return value adequately. It is complete for the task.

    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 coverage is 0%, so the description must add meaning. It clarifies the delay_seconds max (5.0) and that message is echoed. It adds value beyond the bare schema, though more detail on constraints would improve.

    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's function: echoing a message after a delay. It implicitly distinguishes from siblings like echo_json and echo_message, which likely have different behavior.

    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 when to use (when a delayed echo is needed) but does not provide explicit when-to-use or when-not-to-use guidance compared to alternatives.

    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 provided, so description must disclose traits. It states the tool echoes a message with optional formatting and returns metadata, which is sufficient for a simple, side-effect-free operation. However, it does not explicitly confirm idempotency or lack of mutations.

    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 and well-structured: a one-line summary, followed by Args and Returns sections. Every sentence provides essential information without redundancy.

    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?

    Given the simplicity of the tool, the description covers purpose, parameters, and return value adequately. The presence of an output schema (not shown but indicated) further reduces the need for extensive description.

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

    Parameters5/5

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

    Schema description coverage is 0%, so the description compensates fully by explaining each parameter: 'message' as the content to echo, 'uppercase' as a boolean toggle for case conversion. This adds meaning beyond the schema types.

    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 uses a specific verb ('echo back') and identifies the resource ('a message') with optional formatting. Siblings 'echo_json' and 'echo_with_delay' are clearly different, as this tool handles plain text echoing.

    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?

    The description does not explicitly state when to use this tool versus siblings, but the purpose and sibling names imply use cases: plain text vs JSON or delayed echoing. An explicit note would improve clarity.

    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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