MCP Info Gatherer

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation4/5
Most tools target distinct sources (web, Twitter, GitHub, etc.), but search_telegram and search_telegram_channel have overlapping purposes that could confuse an agent. Otherwise, the tool set is well-separated.
Naming Consistency4/5
The majority follow a consistent 'search_<source>' pattern, but 'get_telegram_channel_info' and 'get_trends' use a different verb prefix. The inconsistency is minor but noticeable.
Tool Count5/5
With 12 tools covering multiple search sources (web, social media, code repositories, academic papers), the count is well-scoped for an information gathering server. Each tool has a clear role.
Completeness4/5
The server covers a wide range of search sources including web, Twitter, Hugging Face, Telegram, GitHub, and arXiv. Missing sources like YouTube or news are not critical but represent a minor gap.
Average 4/5 across 12 of 12 tools scored. Lowest: 3.3/5.
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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It mentions the tool analyzes trends and returns a list, but lacks details on whether it's read-only, rate limits, authentication needs, or any side effects. The return format is partially described but behavioral specifics are missing.
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 well-structured: a brief purpose, usage bullet points, and clear Args/Returns sections. It is concise without unnecessary repetition, though the usage list could be integrated more succinctly.
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 has 3 parameters, no annotations, and no output schema, the description covers essential aspects (Args, Returns, usage). However, it omits error handling, authentication requirements, and more detailed behavioral traits, leaving some gaps.
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 supplement. It includes an Args block explaining each parameter: topic with an example, max_results range, and source options. This adds meaningful context beyond the schema's defaults and titles.
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 tool retrieves trends by topic and distinguishes from sibling search tools by focusing on trending topics rather than general search results. It explicitly lists use cases like content planning and audience research.
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 provides usage cases ('content plan, audience research, finding current topics') but does not explicitly state when to avoid this tool or suggest alternatives. The sibling tools are search functions, implying differentiation, but no direct comparison.
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?
No annotations provided, so description carries full burden. It mentions using Tavily API and describes the return format, but does not explicitly state it is read-only or address rate limits/auth requirements.
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 fairly concise with a structured list of args and returns. Could be slightly tighter by not listing use cases separately, but overall efficient.
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 the tool has 2 parameters and no output schema, the description adequately covers input examples and return structure. Missing explicit error handling or edge cases, but sufficient for a general search tool.
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 description coverage is 0%, but the description adds example for query (e.g., 'AI trends 2026') and range for max_results (1-20), providing meaningful context beyond the schema types.
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 tool performs web search and lists specific use cases. However, it does not explicitly distinguish from sibling tools like search_twitter or search_github, though the name implies general web search.
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 lists suitable use cases (fact-checking, market research, etc.) but does not provide when-not-to-use or mention that platform-specific searches should use respective sibling tools.
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?
Discloses use of HF Hub API, free and no key required, and describes return structure with error field. Lacks details on rate limits or pagination, but adequate for a search tool with no annotations.
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?
Description is compact with clear sections (purpose, usage, args, returns), but uses some unnecessary repetition (e.g., 'Поиск моделей на Hugging Face' could be more concise).
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 no output schema and no annotations, description covers purpose, API source, use cases, parameters, and return format adequately. Could mention pagination, but overall complete for a simple search tool.
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?
With 0% schema description coverage, description compensates by explaining query as search query and max_results as maximum results (1-100), but lacks deeper details like query format or default value (though schema shows default 10).
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 'Поиск моделей на Hugging Face' (search models on Hugging Face), uses specific verb+resource, and distinguishes from sibling tools like search_huggingface_datasets.
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?
Mentions suitability for searching AI models, monitoring releases, analyzing trends, but does not explicitly state when not to use or alternative tools among many siblings.
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?
The description mentions using Telethon and required tokens, which adds behavioral context. But without annotations, it lacks details on safety, destructiveness, or rate limits. Moderate transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is adequately structured with sections but is somewhat verbose. Could be more concise by removing redundant docstring formatting.
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 search tool across channels, the description includes return format and prerequisites but misses error handling specifics or pagination details. Sufficient but not complete.
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?
Despite 0% schema coverage via structured descriptions, the docstring in the description explains query and max_results with purpose and range, adding value beyond the schema's type/title.
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 'Search messages across all available Telegram channels' with a specific verb (search) and resource (Telegram channels), and the tool name distinguishes it from siblings like search_telegram_channel.
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?
Usage contexts are given: 'monitoring channels, searching discussions, gathering topic information.' However, no explicit exclusion or comparison to sibling tools is provided.
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 bears full responsibility for behavioral disclosure. It only mentions using the 'GitHub Code Search API', but does not disclose rate limits, authentication needs, error behavior, or limitations on result content. The Return section describes structure but lacks operational details.
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 purpose, API source, use cases, and a clear Args/Returns block. Every element contributes without redundancy.
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?
Despite no output schema, the description includes a Return structure defining fields. However, it lacks details on pagination, total count, error handling, and how it differs from the sibling search_github tool, which could be relevant for selection.
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 compensates by adding value: it provides an example for query ('openai client lang:python') and specifies a range for max_results (1-100). This adds meaningful context beyond the schema property names.
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 'Поиск кода на GitHub' (Search code on GitHub), identifying the specific verb and resource. It distinguishes the tool from siblings like search_github (repos) and search_github_issues by focusing on code, and provides example use cases (code examples, libraries, utilities).
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 explicitly notes suitability for searching code examples, libraries, and utilities, giving contextual guidance. However, it does not provide negative guidance (when not to use) or explicitly compare to siblings, though the resource differentiation is implied.
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 reveals it is free, requires no key, and uses the HF Hub API. It also describes the return structure, providing transparency about 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 has a clear structure with purpose, usage, args, and returns sections. It is front-loaded. Minor redundancy from mixed Russian/English wording, but still efficient.
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 the tool's simplicity (2 parameters, no output schema), the description sufficiently covers purpose, parameters, and return format. Schema coverage is low but description compensates well.
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 description adds context to both parameters: query example ('russian text') and max_results range (1-100). This goes beyond the schema's minimal titles (Query, Max Results).
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 searches for datasets on Hugging Face using the HF Hub API. It distinguishes itself from sibling tools like search_web or search_twitter by specifying the resource type (datasets).
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?
It gives a usage suggestion ('Подходит для: поиск датасетов для обучения, анализа данных') but lacks explicit when-not-to-use or alternative tool guidance. This is adequate but not exemplary.
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?
No annotations are provided, so the description carries the burden. It discloses the use of Telethon (MTProto) and required credentials, and states the return format (dict with fields). However, it does not mention error handling, rate limits, or side effects. Given no annotations, the description provides moderate transparency.
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 line for purpose, then technology, then Args/Returns sections. Every sentence adds value without redundancy. It is appropriate in length.
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 simple tool (single parameter, no output schema), the description fully covers what the agent needs: input formats, output structure, and implementation detail. It is complete for the tool's complexity.
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?
The schema only defines 'channel' as a string. The description adds significant meaning: it accepts @username, chat_id, or invite link. This compensates for the 0% schema description coverage and is very helpful for an agent.
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 'Получить информацию о Telegram канале' (Get info about a Telegram channel), and specifies the method (Telethon/MTProto). It distinguishes itself from sibling tools like 'search_telegram_channel' by focusing on information retrieval for a specific channel.
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 mentions prerequisites (TELEGRAM_API_ID and TELEGRAM_API_HASH) but does not provide explicit guidance on when to use this tool versus alternatives. It implies usage for getting details of a known channel but lacks when-not-to-use information.
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?
No annotations exist, so description carries full burden. It states it uses GitHub Issues API but lacks details on authentication, rate limits, read-only nature, or pagination 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?
Description is well-structured with sections for purpose, API, suitability, args, and returns, but contains slightly redundant phrasing (e.g., repeating 'Search for issues and PRs').
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?
Returns a specific SearchResponse structure with fields, partially covering the absence of output schema. However, missing details on error handling or rate limits for a search tool.
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?
Both parameters are clearly explained with examples (query search syntax) and constraints (max_results 1-100), fully compensating for the lack of schema descriptions (0% coverage).
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 searches GitHub issues and PRs, and differentiates from sibling tools like search_github (general) and search_github_code.
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?
It mentions suitability for monitoring bugs, discussions, and feature requests, implying use for issue/PR related queries, but does not explicitly state when not to use or provide 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?
Discloses free, no-key API access; documents query format with examples; specifies return structure. No annotations present, so description carries full burden and does well.
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-line purpose, then usage context, then args. No redundant sentences. Front-loaded with purpose. Efficient and clear.
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?
Covers inputs and outputs adequately for a simple search tool. Lacks details on error handling or rate limits, but these are minor given the tool's simplicity.
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?
With 0% schema description coverage, the description fully compensates: query is explained with examples (including category syntax), max_results stated range 1-100. Adds significant value beyond schema.
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 tool searches scientific articles on arXiv, a specific resource. It distinguishes from sibling search tools by naming the arXiv API, but does not explicitly compare to siblings.
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?
Provides explicit use cases: research topic exploration, relevant work search, new publication monitoring. However, no guidance on when not to use or 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?
Discloses rate limits with and without token (60 vs 5000 req/h) and mentions using GitHub REST API v3. No annotations were provided, so the description carries the full burden of behavioral disclosure and does so effectively.
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?
Description is front-loaded with purpose, then rate limits, usage, and parameter details. Every sentence adds value, though the args section could be tighter. No wasted words.
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 the presence of sibling tools and no output schema, the description adequately explains the tool's scope (repos), auth requirements, and return structure (SearchResponse fields). Sufficient for an agent to decide and invoke correctly.
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 has 0% description coverage, so description compensates by explaining query supports qualifiers (language:python, stars:>100, topic:ai) and max_results range 1-100. Adds meaning beyond 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 starts with 'Поиск репозиториев на GitHub' (Search repositories on GitHub), a specific verb+resource pairing. It distinguishes from sibling tools like search_github_code and search_github_issues, which target code or issues instead of repos.
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 lists suitable use cases: 'поиск open-source решений, анализ аналогов, мониторинг трендовых проектов' (open-source solutions, alternatives analysis, trend monitoring). Does not state exclusions or when not to use, but the context is clear.
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?
Given the absence of annotations, the description carries the burden of behavioral disclosure. It mentions the use of Telethon (MTProto) and required tokens, but does not cover potential limitations (e.g., rate limits, pagination behavior, or error handling beyond the response structure). The description adds some context but is not exhaustive.
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 separate sections for purpose, requirements, arguments, and return value. Every sentence adds value without redundancy.
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?
For a tool with 3 parameters, no output schema, and no annotations, the description is fairly complete. It covers prerequisites, parameter formats, and return fields. Minor gaps exist (e.g., no mention of error conditions or rate limits), but overall it provides sufficient context for an agent to invoke the tool correctly.
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 coverage is 0%, so the description fully compensates. It explains that 'channel' can be a @username, chat_id, or invite link; 'query' is the search query; and 'max_results' has a default of 10 and range 1-100. This adds meaningful guidance 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?
The description clearly states it searches messages in a specific Telegram channel, using a specific verb ('Поиск сообщений') and identifies the resource ('конкретном Telegram канале'). This distinguishes it from siblings like 'search_telegram' (which likely searches across channels) and 'get_telegram_channel_info' (which retrieves metadata).
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 lists prerequisites: tokens (TELEGRAM_BOT_TOKEN, TELEGRAM_API_ID, TELEGRAM_API_HASH) and that the bot must be added to the channel. It also specifies argument formats (e.g., channel as @username, chat_id, or invite link). However, it does not explicitly contrast with sibling tools or state when not to use this tool.
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 provided, the description carries full burden. It discloses the API version (v2), authentication requirement (Bearer Token), and return structure. It does not mention rate limits or destructive potential, but the tool is read-only, so this is acceptable.
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 well-structured with sections for purpose, usage, args, and returns. It is somewhat verbose with blank lines but remains front-loaded and efficient. Could be slightly tighter but still good.
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 no output schema, the description adequately explains the return format. It mentions error field. It does not cover pagination or detailed error handling, but for a search tool this is sufficient. Sibling tools are diverse, so the platform specificity helps completeness.
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 must add meaning. It provides examples for query (e.g., 'AI news lang:en') and explicitly states the range for max_results (1-100), going beyond just the type and default.
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 searches posts on Twitter/X, which is a specific verb-resource combination. It distinguishes itself from sibling search tools (e.g., search_web, search_github) by naming the platform and use cases.
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 lists explicit use cases: monitoring discussions, opinion search, real-time trend tracking. However, it does not mention when not to use this tool or provide alternatives, though the use cases are clear.
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

Copy to your README.md:

[![mcp-info-gatherer MCP server](https://glama.ai/mcp/servers/ESkuratov/mcp-info-gatherer/badges/card.svg)](https://glama.ai/mcp/servers/ESkuratov/mcp-info-gatherer)

Score Badge

Copy to your README.md:

[![mcp-info-gatherer MCP server](https://glama.ai/mcp/servers/ESkuratov/mcp-info-gatherer/badges/score.svg)](https://glama.ai/mcp/servers/ESkuratov/mcp-info-gatherer)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/ESkuratov/mcp-info-gatherer'

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