用于网络研究、学术论文、Twitter/X、YouTube 和文件提取的 MCP 服务器。为 AI 助手提供八种工具——全部通过 MCP stdio 协议实现。包含用于机构访问的凭据库、CAPTCHA 检测以及节省 Token 的输出功能。
工具 | 描述 |
| 3 层搜索级联：Brave API → DuckDuckGo → HTML 抓取器 |
| 获取任意 URL → 清理后的 Markdown，具有 SSRF 防护和 24 小时缓存 |
| 复合流水线：查询重写 → 搜索 → 并行获取 → 摘要 → 综合 |
| YouTube 视频 → 字幕、摘要、要点、章节、引用 |
| 从文件中提取文本：PDF、DOCX、XLSX、PPTX、音频、视频、图像 |
| 解析 DOI / ArXiv / PubMed → 元数据 + 通过机构访问获取全文 |
| 从 X.com/Twitter 提取推文和推文串 |
| 显示已加载的凭据配置文件和依赖状态（绝不泄露密钥） |
所有工具均为只读——它们仅获取和转换内容，从不进行任何修改。
Related MCP server: The Web MCP
pip install mcp-research或者直接使用 uvx 运行（无需安装）：
uvx mcp-research可选扩展：
pip install 'mcp-research[twitter]' # yt-dlp for Twitter extraction
pip install 'mcp-research[youtube]' # yt-dlp + faster-whisper for YouTube
pip install 'mcp-research[academic]' # PyPDF2 for academic PDFs
pip install 'mcp-research[ingest]' # PDF, DOCX, XLSX, PPTX, audio support
pip install 'mcp-research[all]' # everything检查您的设置：
mcp-research doctor添加到您的 Claude Code MCP 配置（~/.claude/settings.json 或项目 .mcp.json）：
{
"mcpServers": {
"research": {
"command": "uvx",
"args": ["mcp-research"],
"env": {
"BRAVE_API_KEY": "BSA...",
"OLLAMA_URL": "http://localhost:11434"
}
}
}
}添加到 claude_desktop_config.json：
{
"mcpServers": {
"research": {
"command": "uvx",
"args": ["mcp-research"],
"env": {
"BRAVE_API_KEY": "BSA..."
}
}
}
}所有配置均通过环境变量完成——无需配置文件（可选的凭据库除外）。
变量 | 默认值 | 描述 |
| (空) | Brave Search API 密钥。若未设置，则回退到 DuckDuckGo。 |
|
| 用于摘要/综合的 Ollama 端点。留空以禁用。 |
|
| 用于摘要和综合的模型。 |
|
| URL 获取缓存目录。 |
|
| 缓存 TTL（小时）。 |
|
| 搜索日志目录 (NDJSON)。 |
|
| 默认最大搜索结果数。 |
|
| 凭据库文件路径。 |
|
| 文件更改时自动重新加载凭据库。 |
|
| 会话空闲超时（秒）。 |
web_searchweb_search(query, max_results=5, summarize=False, auto_fetch_top=False)使用 3 层级联搜索网络以实现最大可靠性：
Brave Search API — 快速、高质量（需要 BRAVE_API_KEY）
DuckDuckGo 库 — 无需 API 密钥，在速率限制时重试
DuckDuckGo HTML 抓取器 — 最后手段的回退方案
选项：
summarize: 使用 Ollama 总结结果（需要运行 Ollama）
auto_fetch_top: 同时获取并返回顶部结果的完整内容
fetch_urlfetch_url(url, summarize=False, max_chars=15000)获取 URL 并将其转换为干净的 Markdown：
SSRF 防护: 阻止 localhost、私有 IP、非 HTTP 协议
智能重试: 在 429/5xx 错误时进行指数退避，每跳重定向验证
24 小时缓存: SHA-256 键值，可配置 TTL
内容支持: HTML → Markdown，JSON → 代码块，二进制 → 拒绝
智能截断: 在标题/段落边界处断开，而非文本中间
CAPTCHA 检测: 标记 Cloudflare、hCaptcha、reCAPTCHA、Akamai 防火墙
Token 高效: 默认 15K 字符（约 4K Token），可通过 max_chars 调整
researchresearch(query, depth="standard", context="")复合研究流水线：
查询重写 — Ollama 将您的问题优化为搜索关键词
网络搜索 — 查找相关页面（支持零结果重试扩展）
并行获取 — 并发获取前 N 个页面
摘要 — Ollama 总结每个页面
综合 — Ollama 生成最终的带引用答案
深度级别：
深度 | 页面数 | 综合 |
| 2 | 否 |
| 5 | 是 |
| 10 | 是 |
所有步骤在没有 Ollama 的情况下都会优雅降级——您仍然可以获得搜索结果和页面内容。
youtube_essenceyoutube_essence(url, mode="standard")从 YouTube 视频中提取结构化内容：
字幕: 自动字幕或 Whisper 转录（本地、私有）
摘要: 通过 Ollama 进行 AI 摘要
要点: 项目符号总结
章节: 带时间戳的片段
引用: 值得注意的引语（深度模式）
模式：quick (TL;DR), standard (+ 章节), deep (+ 引用)
需要 yt-dlp。可选：用于纯音频视频的 faster-whisper，用于媒体提取的 ffmpeg。
deep_ingestdeep_ingest(path, include_types="", max_files=200, summarize=False)从目录或单个文件中的文件中提取文本：
文本文件: .txt, .md, .json, .csv, 源代码等
PDF: 通过 PyPDF2（可选依赖）
Office: .docx, .xlsx, .pptx（可选依赖）
音频/视频: Whisper 转录（可选）
图像: 通过 Ollama 视觉模型进行 OCR（可选）
类型过滤器：text, pdf, audio, video, image, office
academic_lookupacademic_lookup(identifier, fetch_fulltext=True)从多种标识符类型解析学术论文：
DOI: 10.xxxx/... → Crossref 元数据 + 出版商重定向
ArXiv: 2301.12345 → 摘要 + PDF
PubMed: PMID → E-utilities 元数据 → DOI 链
URL: 出版商页面检测
通过凭据库获取全文访问权限：
EZproxy 重写（前缀和后缀模式）
Bearer Token、API 密钥、基本认证、Cookie Jar
自动出版商检测（IEEE, Springer, Elsevier, ACM, Wiley, Nature, JSTOR 等）
twitter_extracttwitter_extract(url, include_thread=False)使用级联策略从 X.com/Twitter 提取推文和推文串：
yt-dlp (主要) — 使用 Cookie Jar 进行认证访问
Twitter API v2 — 如果在凭据库中配置了 Bearer Token
HTML 获取 — 基于 Cookie 的最后手段
返回：文本、作者、时间戳、指标（点赞、转发、回复）、媒体 URL。
vault_statusvault_status()显示已加载的凭据配置文件、匹配模式和认证类型——绝不泄露密钥。同时检查可选依赖项的可用性。
创建 ~/.mcp-research/vault.yaml 以配置受保护源的认证：
version: 1
profiles:
# University EZproxy for IEEE
ieee-university:
match: "*.ieee.org/**"
ezproxy:
base_url: "https://ezproxy.myuniversity.edu/login?url="
mode: prefix
# Springer via API key
springer:
match: "*.springer.com/**"
auth:
type: api_key
header: "X-ApiKey"
value: "${SPRINGER_API_KEY}"
# X.com via browser cookies
twitter:
match: "*.x.com/**"
auth:
type: cookie_jar
path: "${HOME}/.mcp-research/cookies/twitter.txt"${VAR} 从环境变量解析——密钥绝不以明文存储
第一个匹配的配置文件生效（顺序很重要）
认证类型：bearer, basic, api_key, cookie_jar, headers
EZproxy 模式：prefix（前缀基础 URL）或 suffix（域名重写）
热重载：凭据库文件更改会自动生效
所有工具默认生成紧凑的输出，以避免浪费 AI 上下文窗口的 Token：
工具 | 默认输出 | 覆盖方式 |
| ~15K 字符 (~4K Token) |
|
| 每个源 ~500 Token | 优先使用摘要而非原始内容 |
| ~10K 字符全文 | 带通知截断 |
| 15 个文件，300 字符摘录 |
|
| 3K 字符字幕摘录 | 结果对象中包含完整字幕 |
SSRF 防护: 在每一跳上阻止 localhost、私有 IP、链路本地地址、非 HTTP 协议
CAPTCHA 检测: 识别 Cloudflare, hCaptcha, reCAPTCHA, Akamai, DDoS-Guard 防火墙
输入验证: 大小限制、URL 验证、安全重定向跟踪
无 eval/exec: 无动态代码执行
凭据库安全: 密钥从环境变量解析，repr() 会屏蔽所有认证值
缓存隔离: 仅限所有者的目录权限 (0o700)
优雅降级: 缺失可选依赖不会导致崩溃——功能会通过清晰的消息降级
mcp-research serve # Run MCP stdio server (default)
mcp-research search "query" # Search the web
mcp-research fetch https://example.com # Fetch URL to markdown
mcp-research youtube https://youtu.be/... # Extract YouTube video
mcp-research ingest ./docs/ # Extract text from files
mcp-research academic "10.1109/..." # Resolve academic paper
mcp-research tweet https://x.com/.../123 # Extract tweet
mcp-research vault # Show vault profiles
mcp-research doctor # Check dependenciesgit clone https://github.com/MABAAM/Maibaamcrawler.git
cd Maibaamcrawler
pip install -e ".[all]"
pytest tests/ -v
python -m mcp_research凭据库: ~/.mcp-research/vault.yaml YAML 配置，支持环境变量插值、Glob URL 匹配、EZproxy 重写、热重载
会话池: 具有凭据库认证注入、Cookie Jar 支持、空闲驱逐的按域名会话
CAPTCHA 检测: 识别 Cloudflare, hCaptcha, reCAPTCHA, Akamai, DDoS-Guard, 通用机器人防火墙
学术查找: DOI/ArXiv/PubMed 解析、Crossref 元数据、通过凭据库进行机构全文访问
Twitter/X 提取: yt-dlp, API v2, 以及支持推文串的基于 Cookie 的访问
Token 效率: 默认输出上限（获取约 4K Token，每个研究源约 500 Token）以保留 AI 上下文
Doctor 命令: mcp-research doctor 检查所有依赖项和配置
Windows 编码修复: UTF-8 stdout/stderr 包装器防止 cp1252 崩溃
YouTube 精华: 字幕提取、AI 摘要、要点、章节、引用
深度提取: PDF, DOCX, XLSX, PPTX, 音频, 视频, 图像文本提取
Ollama 集成: 查询重写、摘要、综合、视觉 OCR
搜索日志: 所有操作的 NDJSON 事件日志
Brave Search: 支持 API 密钥的主要搜索层
初始版本：3 个工具 (web_search, fetch_url, research)，SSRF 防护，缓存
MIT
Resolve a DOI, ArXiv ID, or PubMed ID. Fetch paper via institutional access if configured in vault.
Args: identifier: DOI (10.xxxx/...), ArXiv ID (2301.12345), PubMed ID (12345678), or publisher URL. fetch_fulltext: Attempt to fetch the full paper text via vault credentials / EZproxy.
| Name | Required | Description | Default |
|---|---|---|---|
| identifier | Yes | ||
| fetch_fulltext | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations: it mentions attempting to fetch full text via vault credentials/EZproxy, which is a key side effect. Annotations already declare readOnlyHint=true and idempotentHint=true, so there is no contradiction. The description supplements annotations well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the primary purpose, followed by parameter details. It contains no extraneous text. Slightly more structure (e.g., separating args clearly) could improve scannability, but it is already efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description appropriately focuses on input behavior. It covers the main use cases and mentions the vault configuration requirement. Minor gaps exist (e.g., what happens if fetch_fulltext fails), but overall it is sufficiently complete for a well-annotated tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It explains that 'identifier' can be a DOI, ArXiv ID, PubMed ID, or publisher URL, and that 'fetch_fulltext' defaults to true. This provides necessary semantics that the schema alone lacks.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool resolves specific academic identifiers (DOI, ArXiv ID, PubMed ID) and optionally fetches full text via institutional access. The verb 'Resolve' and listing of identifier types provide a specific purpose that distinguishes it from siblings like web_search and fetch_url.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use the tool (for resolving academic identifiers and fetching papers with vault access). While it does not provide explicit 'when not to use' guidance, the sibling tools offer natural alternatives, and the context is clear enough for an AI agent to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract text from files in a directory or single file. Supports text, PDF, DOCX, XLSX, PPTX, audio, video, images.
Args: path: Directory or file path to process. include_types: Comma-separated type filter (text,pdf,audio,video,image,office). Empty = all. max_files: Maximum files to process (1-5000). summarize: If true, generate an AI summary of the combined content.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| max_files | No | ||
| summarize | No | ||
| include_types | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations confirm read-only, idempotent, non-destructive behavior. The description adds value by detailing the extraction process (text from various formats) and the optional AI summarization feature, which annotations do not cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: a one-line overview followed by a clean bullet-style Args section. Each sentence serves a purpose, and the essential information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (many file types, 4 parameters, optional summarize), the description sufficiently covers purpose, parameters, and behavior. An output schema exists, so return values need not be detailed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description provides detailed parameter docs (path, include_types, max_files, summarize) with defaults and examples (e.g., 'Comma-separated type filter... Empty = all'). This fully compensates for the schema gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool extracts text from files (directories or single files), listing supported formats (text, PDF, DOCX, etc.). This distinguishes it from sibling tools like fetch_url (URLs), web_search (web queries), and youtube_essence (YouTube).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains the tool's scope (local file processing) and supported types, providing clear context. However, it does not explicitly state when not to use it or mention alternatives beyond implied differences from siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a URL, convert to markdown. SSRF-protected and cached.
Args: url: The URL to fetch. summarize: If true and Ollama is available, include a summary. max_chars: Maximum content chars (default ~15K/4K tokens). Set higher for full pages.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| max_chars | No | ||
| summarize | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds SSRF protection, caching, and conditional summarization beyond annotations' readOnly/idempotent hints. No contradictions. More details on error handling would improve, but current info is solid.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise: single opening sentence plus a three-line bullet list. No fluff, every sentence adds value. Perfect structure for quick scanning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given output schema exists, description doesn't need return details. It covers security (SSRF), caching, and parameter nuances. Missing authentication or error info, but overall adequate for a fetch tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully explains each parameter: url is the URL, summarize has Ollama condition, max_chars includes default and advice to increase for full pages. Adds significant value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Fetch a URL, convert to markdown' with specific verb and resource. It distinguishes from siblings like web_search and academic_lookup by focusing on fetching a single URL rather than searching or academic data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: fetch a specific URL for markdown conversion. It doesn't explicitly compare to siblings but provides enough context (e.g., Ollama availability for summarization) to guide appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compound research: search → fetch top pages → summarize → synthesize.
Args: query: The research question. depth: Research depth — "quick" (2 pages), "standard" (5 pages), or "deep" (10 pages). context: Optional context from prior research to inform synthesis.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | standard | |
| query | Yes | ||
| context | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: the multi-step process (search, fetch, summarize, synthesize) and the meaning of depth, which goes beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise with a front-loaded pipeline overview and bullet points for arguments. Every sentence adds value; no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there is an output schema (not shown) and annotations cover safety, the description explains the tool's composite nature, parameter meanings, and pipeline stages. It is complete for an agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description carries full burden. It explains all three parameters: query is the research question, depth with three options, and context as optional prior research. This fully compensates for missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool does 'Compound research: search → fetch top pages → summarize → synthesize', which is a specific verb+resource and distinguishes it from sibling tools like web_search, fetch_url, or academic_lookup that perform only individual steps.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides the research pipeline and explains the depth parameter with clear options. It implies use for comprehensive research combining multiple steps, but does not explicitly state when not to use or compare directly with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract tweet or thread from X.com/Twitter. Supports yt-dlp, API, and cookie-based access.
Args: url: Tweet URL (x.com/user/status/id or twitter.com/user/status/id). include_thread: If true, fetch the full conversation thread.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| include_thread | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey read-only, idempotent, and non-destructive behavior. The description adds that it supports multiple access methods, which is useful context beyond annotations, but doesn't detail error handling or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise: two sentences for purpose and two bullet-point args. No wasted words, front-loaded with main purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only 2 simple params and an output schema (not shown), the description covers the essential behavior and parameter semantics. It's mostly complete, though could mention output format briefly, but output schema covers that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining the url format (x.com/user/status/id) and the purpose of include_thread (fetch full thread). Both parameters are clearly described.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Extract' and the resource 'tweet or thread from X.com/Twitter', distinguishing it from siblings like fetch_url by being Twitter-specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides technical details (yt-dlp, API, cookie-based access) but lacks explicit guidance on when to use this tool versus alternatives like fetch_url. No when-not-to-use or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show credential vault status, loaded profiles, and optional dependency availability. Never exposes secrets.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true. Description adds security assurance 'Never exposes secrets', which is valuable beyond annotations. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, second adds critical security note. Efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Zero parameters, good annotations, output schema exists. Description fully covers the tool's behavior and safety. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, schema coverage 100%. Description adds meaning by specifying what the tool shows (status, profiles, dependencies) beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb 'Show' and resource 'credential vault status', with clear scope including loaded profiles and dependency availability. Distinguishes from siblings by being the only vault-related tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is clear: a status tool to check vault state. No explicit alternatives or exclusions, but the purpose implies when to use. Slight lack of when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the web using a 3-tier cascade (Brave → DuckDuckGo → scraper).
Args: query: Search query string. max_results: Maximum number of results to return (1-20). summarize: If true and Ollama is available, summarize the results. auto_fetch_top: If true, also fetch the full content of the top result.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| summarize | No | ||
| max_results | No | ||
| auto_fetch_top | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable transparency by revealing the 3-tier cascade (Brave, DuckDuckGo, scraper) and optional Ollama summarization, which are beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is succinct, uses a bullet list for arguments, and front-loads the cascade mechanism. Every sentence provides value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown but indicated), the description adequately covers input parameters and behavior. It is complete for an AI 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameters. It does so effectively: query as search string, max_results (1-20), summarize (if Ollama available), and auto_fetch_top (fetch top result content). This adds substantial meaning beyond the JSON schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it searches the web using a 3-tier cascade, differentiating it from siblings like academic_lookup, fetch_url, and research. The verb 'Search' and resource 'web' are specific, and the cascade detail adds precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
While the purpose is clear, the description does not provide explicit guidance on when to use this tool versus alternatives like fetch_url or research. It lacks when-not conditions or comparative context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract essence from a YouTube video: transcript, summary, key points, chapters, quotes.
Args: url: YouTube URL (youtube.com/watch?v=, youtu.be/, youtube.com/shorts/). mode: Extraction depth — "quick" (TL;DR), "standard" (+ chapters), or "deep" (+ quotes).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| mode | No | standard |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds no behavioral traits beyond these, such as external API dependency or rate limits. Despite annotations covering safety, the description misses contextual details like needing internet access.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise: a single sentence defining purpose followed by a well-structured Args list. Every sentence is meaningful, and the structure is front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 nested objects), the description covers purpose, parameters, and output types. It lacks information on error handling or return format, but the existence of an output schema mitigates this. Overall, it is adequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by explaining the 'url' parameter with allowed formats and the 'mode' parameter with three depth levels and their effects. This adds significant meaning beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Extract essence') and the resource ('YouTube video'), followed by a list of outputs (transcript, summary, key points, chapters, quotes). This distinguishes it from siblings like twitter_extract or fetch_url which target different sources or actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context via parameter explanations (allowed URL formats and mode options). However, it does not explicitly mention when to use this tool over alternatives or exclude scenarios, though the specificity to YouTube serves as implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.1academic_lookupdeep_ingestfetch_urlresearchtwitter_extractvault_statusweb_searchyoutube_essencev0.3.0academic_lookupdeep_ingestfetch_url1 field changedInput schema / properties / max_chars / defaultPrevious value: -50000New value: +0twitter_extractvault_statusyoutube_essencev0.1.0fetch_urlresearchweb_searchEach tool targets a distinct source or operation: academic references, local files, URLs, compound research, Twitter, vault status, web search, and YouTube. There is no ambiguity between tools.
Tool names use mixed conventions: verb_noun (fetch_url, web_search), noun_noun (vault_status, youtube_essence), platform_verb (twitter_extract), and single word (research). No consistent pattern.
8 tools is an appropriate scope for a research assistant, covering key sources (web, academic, social media, local files) without being overwhelming.
The toolset covers major research workflows: search, fetch, extract, and synthesize. Minor gaps like result organization or citation management are not critical for core functionality.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Web search, fetch, extract, and research for AI agents. Markdown output + AI-synthesized answers.
Web research for agents: quality-scored Google search, webpage extraction, and deep research.
LLM-ready web search + instant answers + URL-to-clean-text fetch for agents and RAG.
Agent-native search engine with live web research optimized for AI agents.
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