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prasad-a-abhishek

mcp-msgdump

by prasad-a-abhishek
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

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: analyze_log produces a report, replay_session replays a session, and check_schemas validates schemas. There is no meaningful overlap between the three.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun snake_case pattern: analyze_log, replay_session, check_schemas. The naming is predictable and easy to infer.

    Tool Count5/5

    Three tools is well-scoped for the apparent domain of MCP log inspection. Each tool has a distinct role without unnecessary bloat.

    Completeness4/5

    The core workflows of analyzing, replaying, and schema-checking are covered. Missing features like exporting results or filtering by time could exist, but the current set is reasonably complete for log analysis.

  • Average 3.5/5 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 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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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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, and the description does not disclose side effects, permissions required, read-only status, or output format. It only says it 'checks' schemas, which is minimal behavioral information.

    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 unnecessary words or repetition. It clearly conveys the tool's core function.

    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 tool with one parameter and no output schema, the description covers the main purpose. However, it does not explicitly state what the tool returns or whether it produces a report, which would improve 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 single parameter 'path' is fully described in the schema, and the description reinforces that it is a log file path. Since schema coverage is 100%, the baseline of 3 applies; the description does not add extra semantic detail 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 tool's specific action ('Check tool schemas in a log file') and its purpose ('for common issues such as missing descriptions or untyped parameters'). It is distinguishable from sibling tools like analyze_log and replay_session.

    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 gives no explicit guidance on when to use this tool versus the sibling tools. It does not mention alternatives or conditions for selection, leaving the agent to infer usage from the name alone.

    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 of disclosing side effects. 'Replay' strongly implies that MCP messages will be executed or sent, which could cause side effects, but the description does not state this or any safety implications.

    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, concise sentence with no redundant or vague wording. It efficiently communicates the core purpose and the optional filtering capability.

    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 description covers the basic purpose and parameters, but it omits important context such as what happens when messages are replayed, whether the operation is safe, and what output or result is expected. Given no annotations or output schema, these gaps leave the description only partially complete.

    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%, and each parameter has a corresponding description. However, the descriptions add little beyond the schema itself—for example, 'filter_tool' is described only as a string filter and 'start_index' as a zero-based index, with no additional semantics 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 the specific action ('Replay a log file of MCP messages') and the optional filtering constraint ('filtered by tool name'). This distinguishes it from the sibling tool 'analyze_log', which implies analysis rather than replay.

    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 the tool—when replaying a log—but does not explicitly state when to prefer it over alternatives like 'analyze_log' or when not to use it. The usage guidance is implicit rather than explicit.

    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?

    There are no annotations, so the description must carry the behavioral transparency burden. It states the action (analyze) and output (structured report), implicitly suggesting it is read-only, but it does not explicitly mention side effects, file modification, authentication requirements, or error behavior.

    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 brief and to the point, using two straightforward sentences. There is no redundant or extraneous information.

    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 description mentions a 'structured report' but does not specify the report's format, content, or any error behavior. Given the lack of an output schema, this omission leaves some ambiguity for an agent trying to anticipate the tool's result.

    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?

    The only parameter, path, has a clear description stating it points to the JSONL log file to analyze. This adds meaningful context beyond the parameter name and type, even though the schema coverage is complete.

    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: analyzing a JSONL log file of MCP JSON-RPC messages and producing a structured report. It distinguishes this from sibling tools like replay_session and check_schemas by specifying the input format and action.

    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 explicit guidance is given about when to use this tool versus the sibling tools. The description implies it is for analyzing logs, but it does not state conditions or alternatives, leaving the agent to infer the appropriate context.

    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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