MCP Blog API
MCP 블로그 API
FastMCP와 httpx로 구축된 간단한 블로그 API 서비스입니다. 이 프로젝트는 빠르고 안정적인 Python 패키지 관리 및 환경 설정을 위해 uv를 지원합니다.
설명
이 프로젝트는 모의 블로그 API 서비스와 상호작용하는 API 도구 세트를 제공합니다. 사용자는 모든 블로그를 가져오고, 제목으로 블로그를 검색하고, 새 블로그 게시물을 추가할 수 있습니다. 이 프로젝트에는 재현 가능한 종속성 관리를 위한 uv.lock 파일이 포함되어 있습니다.
Related MCP server: hello-mcp
특징
모의 API에서 모든 블로그 가져오기
제목으로 블로그 검색
새로운 블로그 게시물 추가
요구 사항
Python 3.13 이상
httpx 0.28.1 이상
mcp 1.9.0 이상
설치
uv 사용 (추천)
지엑스피1
pip 사용하기
# Clone the repository
git clone https://github.com/yourusername/mcp-blog.git
cd mcp-blog
# Install dependencies
pip install -e .용법
# Import the MCP server
from main import mcp
# Get all blogs
blogs = mcp.tools.get_blogs()
# Search for blogs by title
search_results = mcp.tools.search_blogs(query="Python")
# Add a new blog
new_blog = mcp.tools.add_blog(title="My New Blog", body="This is the content of my new blog post.")서버 실행
uv 사용 (추천)
uv python main.py표준 Python 사용
python main.py환경 관리
uv 사용 (추천)
uv는 Python 프로젝트에 대한 빠른 환경 관리 기능을 제공합니다. 이 프로젝트의 가상 환경을 생성하고 관리하는 방법은 다음과 같습니다.
# Create a new virtual environment
uv venv
# Activate the virtual environment
# On Unix/macOS
source .venv/bin/activate
# On Windows
.venv\Scripts\activate
# Install dependencies in the virtual environment
uv pip install -e .
# Update dependencies
uv pip syncAPI 참조
get_blogs()
모의 API에서 모든 블로그를 가져옵니다.
검색_블로그(쿼리: str)
모의 API를 사용하여 제목으로 블로그를 검색합니다.
add_blog(제목: str, 본문: str)
모의 API에 새로운 블로그를 추가합니다.
특허
[여기에 라이센스 정보를 추가하세요]
기여하다
[여기에 기여 지침을 추가하세요]
Available Tools
3 toolsadd_blogC
Add a new blog to the mock API.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| body | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states 'Add a new blog' which implies a write/mutation operation, but doesn't disclose behavioral traits such as authentication needs, rate limits, idempotency, or what happens on success/failure. This is inadequate for a mutation tool with zero annotation coverage.
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 a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, stating the core purpose without unnecessary elaboration.
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 a mutation tool with no annotations, 0% schema coverage, no output schema, and 2 required parameters, the description is incomplete. It lacks parameter explanations, behavioral context, and usage guidelines, making it insufficient for effective tool invocation.
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 schema provides no parameter descriptions. The description doesn't mention parameters at all, failing to add any meaning beyond the input schema. For a tool with 2 required parameters, this leaves semantics completely undocumented.
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 ('Add a new blog') and target resource ('to the mock API'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'get_blogs' or 'search_blogs', which would require mentioning this is for creation versus retrieval/search operations.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for creation, or compare to sibling tools like 'get_blogs' or 'search_blogs', leaving the agent without explicit usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_blogsB
Fetch all blogs from the mock API.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions fetching all blogs but does not specify aspects like rate limits, authentication needs, pagination, or error handling. This leaves significant gaps in understanding how the tool behaves in practice.
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 a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded and wastes no space, making it easy to parse quickly.
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 (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose but lacks details on behavior, usage context, or output format, which could be helpful for an agent despite the low complexity.
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 tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description does not need to add parameter details, so it meets the baseline expectation for a parameterless tool by not introducing confusion or redundancy.
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 ('Fetch') and resource ('all blogs from the mock API'), making the purpose evident. However, it does not explicitly differentiate from the sibling 'search_blogs', which might also retrieve blogs but with filtering capabilities, leaving some ambiguity about when to choose one over the other.
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?
No guidance is provided on when to use this tool versus alternatives like 'search_blogs' or 'add_blog'. The description implies it fetches all blogs without filtering, but this is not stated explicitly, leaving the agent to infer usage context without clear directives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_blogsC
Search for blogs by title using the mock API.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the search action and API source but omits critical details such as whether this is a read-only operation, expected response format, error handling, or any rate limits. The description is minimal and lacks depth for a tool with behavioral implications.
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 a single, efficient sentence with zero waste. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration.
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 lack of annotations and output schema, the description is incomplete. It covers the basic action but misses essential context like return values, error cases, and behavioral traits. For a search tool with no structured support, this leaves significant gaps in understanding.
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 schema description coverage is 0%, so the description must compensate. It only vaguely implies that the 'query' parameter is for title searches, without detailing syntax, format, or constraints. This adds minimal meaning beyond the bare schema, failing to adequately document the single required parameter.
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 ('Search for blogs') and the resource ('blogs'), specifying the search criterion ('by title') and the data source ('using the mock API'). It distinguishes from sibling 'get_blogs' by indicating a search rather than a general retrieval, but doesn't explicitly differentiate from 'add_blog' beyond the verb difference.
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?
No explicit guidance is provided on when to use this tool versus alternatives like 'get_blogs' or 'add_blog'. The description implies usage for title-based searches but lacks context on prerequisites, exclusions, or comparative scenarios with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
- First observed
add_blog - First observed
get_blogs - First observed
search_blogs
TDQS
Each tool has a clearly distinct purpose: add_blog creates new blogs, get_blogs retrieves all blogs, and search_blogs finds blogs by title. There is no overlap in functionality, making tool selection straightforward for an agent.
All tool names follow a consistent verb_noun pattern with snake_case: add_blog, get_blogs, and search_blogs. This predictability enhances readability and usability across the tool set.
With only 3 tools, the set feels thin for a blog API that typically requires CRUD operations. While the tools cover creation and retrieval, the absence of update and delete tools limits the server's scope, making it borderline appropriate.
The tool set is significantly incomplete for a blog API domain. It lacks update_blog and delete_blog tools, preventing full lifecycle management. This gap will likely cause agent failures when attempting to modify or remove blogs.
Maintenance
Resources
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