AI Skill Hub 推荐使用:开源MCP工具 是一款优质的MCP工具。AI 综合评分 7.5 分,在同类工具中表现稳健。如果你正在寻找可靠的MCP工具解决方案,这是一个值得深入了解的选择。
开源MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
开源MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/vassiliylakhonin/grantflow
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"--mcp--": {
"command": "npx",
"args": ["-y", "grantflow"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 开源MCP工具 执行以下任务... Claude: [自动调用 开源MCP工具 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"__mcp__": {
"command": "npx",
"args": ["-y", "grantflow"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
Agent-native grant workflow infrastructure for governed proposal operations.
GrantFlow gives AI agents and workflow systems a governed API for donor-aware proposal drafting, preflight checks, human review, traceability, and export-ready evidence packs.
It is not a grant-writing chatbot. It is the API layer an agent can discover, register with, call safely, and audit.
Boundaries. Not legal, compliance, financial, or grant-eligibility advice. GrantFlow enforces evidence structure and grounding signals — it does not verify the factual truth of any claim and does not retrieve live sources on its own. A human must review before any submission.
---
Start the API:
make bootstrap-dev
source .venv/bin/activate
uvicorn grantflow.api.app:app --reload
Discover the agent contract:
export GRANTFLOW_BASE_URL="http://127.0.0.1:8000"
curl "$GRANTFLOW_BASE_URL/.well-known/agent-capabilities.json"
curl "$GRANTFLOW_BASE_URL/.well-known/agent-tools.json"
curl "$GRANTFLOW_BASE_URL/.well-known/agent-recipes.json"
Request self-serve onboarding:
curl -X POST "$GRANTFLOW_BASE_URL/agents/onboarding" \
-H "Content-Type: application/json" \
-d '{
"agent_name": "proposal-worker",
"auth_type": "api_key",
"requested_scopes": ["generate:write", "status:read", "quality:read"]
}'
Request OAuth client credentials and exchange a Bearer token:
curl -X POST "$GRANTFLOW_BASE_URL/agents/onboarding" \
-H "Content-Type: application/json" \
-d '{
"agent_name": "proposal-worker",
"auth_type": "oauth_client_credentials",
"requested_scopes": ["generate:write", "status:read", "quality:read"]
}'
curl -X POST "$GRANTFLOW_BASE_URL/agents/oauth/token" \
-H "Content-Type: application/json" \
-d '{
"grant_type": "client_credentials",
"client_id": "'"$GRANTFLOW_CLIENT_ID"'",
"client_secret": "'"$GRANTFLOW_CLIENT_SECRET"'",
"scope": "generate:write status:read quality:read"
}'
Register a sandbox agent for sample payloads:
curl -X POST "$GRANTFLOW_BASE_URL/agents/register" \
-H "Content-Type: application/json" \
-d '{
"agent_name": "proposal-worker",
"agent_type": "workflow_agent",
"purpose": "Run deterministic GrantFlow smoke workflows"
}'
The response includes sample_requests.preflight and sample_requests.generate, so an agent can immediately run a safe sandbox workflow.
Full guide: docs/agents/quickstart.md
Self-serve agent keys carry expiry, tenant, and scopes. Agent-critical endpoints enforce tenant_id and scopes when API-key auth is active.
Short-lived runtime sessions are available at POST /agents/session.
Agents can validate X-API-Key or Authorization: Bearer credentials with POST /agents/introspect before calling protected tools.
Agents can rotate or revoke self-serve credentials:
curl -X POST "$GRANTFLOW_BASE_URL/agents/credentials/rotate" \
-H "Content-Type: application/json" \
-d '{"credential":"'"$GRANTFLOW_AGENT_CREDENTIAL"'","ttl_seconds":3600}'
curl -X POST "$GRANTFLOW_BASE_URL/agents/credentials/revoke" \
-H "Content-Type: application/json" \
-d '{"credential":"'"$GRANTFLOW_AGENT_CREDENTIAL"'","reason":"credential rotation completed"}'
Revocation uses an in-process jti denylist for sandbox/controlled deployments. The revoke response includes revocation_scope: "in_process" and restart_safe: false so agents read this limitation without consulting docs. Multi-replica production should enforce revocation at the gateway layer.
Run an external agent conformance smoke:
python -m grantflow.agents.conformance --base-url "$GRANTFLOW_BASE_URL"
高质量的开源MCP工具,具有受控的资助提案工作流程
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
总体来看,开源MCP工具 是一款质量良好的MCP工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。
| 原始名称 | grantflow |
| 原始描述 | 开源MCP工具:Agent-native API for governed grant-proposal workflows: donor-aware drafting, pr。⭐6 · Python |
| Topics | mcpagentic-aiagentsaudit-trail |
| GitHub | https://github.com/vassiliylakhonin/grantflow |
| License | MIT |
| 语言 | Python |
收录时间:2026-06-08 · 更新时间:2026-06-08 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端