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fenghaochang

loreroom

by fenghaochang
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Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves recent context by time, the other searches history by keywords. There is no overlap.

    Naming Consistency5/5

    Both tools use consistent snake_case naming with a verb_noun pattern (get_recent_context, search_tg_history), making them predictable.

    Tool Count3/5

    With only 2 tools, the server feels thin for its intended purpose of Telegram history retrieval, but it is borderline as it covers two essential retrieval methods.

    Completeness3/5

    The tool set covers time-based and keyword-based retrieval, but lacks other potentially useful operations like retrieving by user or date range, leaving notable gaps.

  • Average 4.1/5 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

  • 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.

  • This repository includes a glama.json configuration file.

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

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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.

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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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations provided. Description discloses what is returned (time, sender, content, both directions) but not limits, pagination, or side effects. Adequate for a read tool but incomplete.

    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?

    Two sentences: one defines function, one gives usage guidance. Zero waste, front-loaded with purpose.

    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?

    No output schema; description partially explains return format (time, sender, content) but lacks structure details or limits. For a retrieval tool, more completeness would be beneficial.

    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 descriptions already cover both parameters (hours, chat_id) with 100% coverage. Description adds context on when to use them but no additional syntax or meaning beyond 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 states the verb (retrieve), resource (Telegram conversations), and scope (last N hours, bidirectional). It distinguishes from sibling by implying this tool is for broad context retrieval when lacking context.

    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?

    Explicitly says when to use: 'when you lack context, or the user mentions just now/today/yesterday/recently'. Does not explicitly state when not to use, but presence of sibling suggests alternative for specific searches.

    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. Description implies read-only search behavior and includes scope (past conversations, bidirectional, with time and sender). Does not explicitly state non-destructive nature or rate limits, but adequate for a search 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?

    Two concise sentences, no fluff. First sentence defines action and scope, second provides clear usage directive. Every sentence earns its place.

    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 low complexity (2 params, no output schema, no annotations), description covers core purpose, usage, and behavioral context. Missing details on return format or chat_id usage, but not critical for typical search tool.

    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 coverage is 50% (keyword described, chat_id not). Description adds no information about chat_id parameter beyond schema. Only keyword is implied by context. Insufficient compensation for missing schema description.

    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?

    Description clearly states full-text search of all past Telegram conversations with direction, time, and sender. Explicitly prioritizes this tool over not remembering, distinguishing it from siblings like get_recent_context.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Explicitly instructs to use when users mention past events like '之前/上次/我們討論過', and not to default to saying '不記得'. Provides clear when-to-use guidance.

    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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