CiNii MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@CiNii MCP Serverfind articles about quantum computing published after 2020"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CiNii MCP Server
Setup
uv sync --no-devPlease set the directory path and the CINII_APP_ID.
{
"mcpServers": {
"cinii-mcp-server": {
"command": "uv",
"args": [
"--directory",
"<YOUR_MCP_SERVER_DIRECTORY>",
"run",
"server.py"
],
"env": {
"CINII_APP_ID": "<YOUR_CINII_APP_ID>"
},
}
}
}Related MCP server: cinii-mcp
API Key Registration
Access CiNii API Registration
Find and access the link for "CiNii Web API Developer Registration".
Upon accessing, a registration form will appear. Fill in the necessary information and submit your application.
After submission, your Application ID will be sent to you via email.
Usage
Python API
from lib.cinii import CiNiiClient, SortOrder, SearchRange
client = CiNiiClient(appid="your_app_id")
# Basic search
results = client.advanced_search({
"query": "機械学習",
"max_results": 10
})
# Advanced search with filters
results = client.advanced_search({
"query": "Deep Learning",
"max_results": 20,
"sortorder": SortOrder.PUBLICATION_YEAR_DESC, # Publication year, descending
"range": SearchRange.HAS_BODY_IN_CINII, # The full text is available on CiNii.
"year_from": 2020,
"year_to": 2024
})
# Or you can also use numeric values for the search Options
results = client.advanced_search({
"query": "Deep Learning",
"max_results": 20,
"sortorder": 1, # Publication year, descending
"range": 1, # The full text is available on CiNii.
"year_from": 2020,
"year_to": 2024
})Sort Order Options
SortOrder.PUBLICATION_YEAR_DESC(1): Publication year descendingSortOrder.PUBLICATION_YEAR_ASC(2): Publication year ascendingSortOrder.TITLE_DESC(3): Title descendingSortOrder.TITLE_ASC(4): Title ascendingSortOrder.JOURNAL_DESC(5): Journal descendingSortOrder.JOURNAL_ASC(6): Journal ascendingSortOrder.CITATION_DESC(7): Citation count descending
Search Range Options
SearchRange.ALL(0): AllSearchRange.HAS_BODY_IN_CINII(1): Body exists in CiNiiSearchRange.HAS_BODY_OR_LINK(2): Body exists in CiNii, or link exists in linked services
Available Tools
1 tooladvanced_search_detailedC
CiNiiで高度な検索を行い、論文情報を取得する
| Name | Required | Description | Default |
|---|---|---|---|
| issn | No | ||
| lang | No | ||
| page | No | ||
| issue | No | ||
| query | No | ||
| range | No | ||
| start | No | ||
| title | No | ||
| author | No | ||
| volume | No | ||
| journal | No | ||
| year_to | No | ||
| authorid | No | ||
| publisher | No | ||
| reference | No | ||
| sortorder | No | ||
| year_from | No | ||
| affiliation | No | ||
| max_results | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of disclosing behavioral traits. It only states the action and provides no information about read-only status, pagination, rate limits, authentication, or any side effects, leaving the agent completely uninformed.
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 concise sentence that wastes no words and front-loads the core action. However, it is so brief that it borders on under-specification, though that is better captured in completeness.
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?
With 19 optional parameters, no schema descriptions, and no annotations, a one-sentence description is severely inadequate. The output schema exists but does not help the agent understand when or how to use the tool, making the description far from 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?
The schema contains 19 parameters with zero description coverage, and the description mentions none of them. Without any explanation of parameters like issn, lang, sortorder, or max_results, an agent cannot correctly construct a valid search query.
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 performs an advanced search on CiNii and retrieves paper information, using a specific verb (検索を行い) and resource (CiNii). However, with no sibling tools listed, it does not differentiate beyond the basic action.
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, nor are there any use cases, prerequisites, or exclusions. The phrase 'advanced search' implies a general purpose but offers no concrete context for an agent to decide when to invoke it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Only one tool exists, so there is no possibility of confusion between tools. The tool's purpose is clear from its name and description.
The single tool uses a descriptive snake_case name that aligns with common MCP naming conventions. With only one tool, there are no inconsistencies to evaluate.
One tool is on the low end and feels thin for a server named CiNii, which typically implies a richer API surface. However, the tool itself is comprehensive enough to perform advanced searches, so it's borderline.
The server only offers a single search tool. It lacks dedicated operations for fetching paper details by ID, managing result pagination, or accessing related resources like authors or references, leaving significant gaps for a full CiNii integration.
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