Skip to main content
Glama
ramich2077

tgreader-mcp

by ramich2077
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are completely distinct: one lists channels, the other reads messages from a specific channel. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow the verb_noun pattern ('list_channels', 'read_messages'), making them predictable and easy to understand.

    Tool Count4/5

    With only 2 tools, the set is minimal but well-suited for a read-only Telegram client. It could benefit from additional tools like a channel info getter, but it is not overly sparse.

    Completeness4/5

    The tools cover the core read operations: listing channels and reading messages. Missing features like retrieving a single channel's details or searching across channels are minor gaps for a focused read-only tool.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 5 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.

  • Add a glama.json file to provide metadata about your server.

  • 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.

  • Add related servers to improve discoverability.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries full transparency burden. It discloses the returned data structure (Count, Channels with fields), default and maximum limits, and that search is a RegExp pattern. It does not cover side effects, authentication, or rate limits, but for a read operation the described behavior is sufficient.

    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?

    Description is concise (3 lines plus return structure) and well-structured with bullet points for parameters. Every sentence adds value 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?

    Description fully covers the tool's purpose, all parameters with constraints, and return format. Output schema exists but description still adds readability. Sibling tool is not related, so no cross-referencing needed. Complete for a list operation.

    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 description adds crucial meaning: 'account' is account name with default, 'search' is optional RegExp pattern with max 200 chars, 'limit' is max channels with defaults and max 500. This goes well beyond the bare 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?

    Description clearly states 'List Telegram channels the account is subscribed to' which uses a specific verb and resource. It easily distinguishes from sibling tool 'read_messages' which deals with messages, not channels.

    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?

    Description implies usage context (listing subscribed channels) but does not explicitly provide when to use vs. alternatives, nor does it mention conditions to avoid using this tool. No exclusions or guidance on when not to use.

    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?

    Describes limits (default 20, max 1000 for limit/cap), offset_date behavior, search regex, and return structure. No annotations, so description reasonably covers non-destructive read operation.

    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?

    Well-structured with Args and Returns sections, front-loaded purpose, no redundant text. Each sentence adds value.

    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?

    Covers all 5 parameters, describes return format, includes relevant constraints (max limits, default account). Output schema exists but description provides additional context for completeness.

    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?

    Adds meaning beyond schema: explains limit default and max, offset_date as 'before', search as 'RegExp pattern max 200 chars', and account default. Schema has 0% description coverage, so description compensates well.

    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?

    Clearly states 'Read messages from a Telegram channel' with specific verb and resource. Distinguishes from sibling 'list_channels' by focusing on message content vs channel listing.

    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?

    Implicitly clarifies use case (reading messages) vs sibling (listing channels), but lacks explicit when-not or alternative conditions. Still clear enough for typical usage.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

tgreader-mcp MCP server – quality and maintenance score on Glama

Copy to your README.md:

Score Badge

tgreader-mcp MCP server – quality and maintenance score on Glama

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ramich2077/tgreader-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server