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AlexiFeng

MCP Chat Logger

by AlexiFeng
OverviewSchemaRelated ServersScoreDiscussions
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

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly distinct as it is the sole operation available.

    Naming Consistency5/5

    The single tool name 'save_chat_history' follows a clear verb_noun pattern. Since there is only one tool, consistency is inherently perfect with no deviations to assess.

    Tool Count2/5

    A single tool is too few for a server named 'MCP Chat Logger', which suggests broader logging capabilities. This minimal set feels thin and under-scoped for the implied domain of chat logging.

    Completeness2/5

    The tool surface is severely incomplete for a chat logger. It only saves history, lacking essential operations like retrieving, updating, deleting, or listing chat logs, which are core to logging workflows.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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.

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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 of behavioral disclosure. It states the tool saves to a file but doesn't specify where the file is saved (e.g., local path, cloud storage), permissions required, error handling, or whether the operation is idempotent. This leaves significant gaps in understanding the tool's behavior.

    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 and front-loaded with the core purpose, followed by parameter explanations. It avoids unnecessary words, though the formatting with 'Args:' could be slightly more integrated. Overall, it's efficient with minimal waste.

    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 moderate complexity (2 parameters, no annotations, no output schema), the description covers the basic purpose and parameters but lacks details on output (e.g., file location, success indicators), error cases, and behavioral traits. It's minimally viable but has clear gaps for a file-saving operation.

    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 basic semantics for both parameters ('messages' as a list of chat messages with role and content, 'conversation_id' for file naming), which is valuable since schema description coverage is 0%. However, it doesn't detail the structure of message objects or provide examples, leaving some ambiguity.

    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 action ('Save chat history') and the output format ('as a Markdown file'), providing a specific verb+resource combination. It distinguishes the tool's function well, though there are no sibling tools to differentiate from, which prevents a perfect score of 5.

    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, prerequisites, or context for invocation. It lacks any mention of when-not-to-use scenarios or comparisons with other tools, leaving usage entirely implicit.

    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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