Un servidor FastMCP stdio que expone la API de CiNii Research — la base de datos académica nacional de Japón, operada por el Instituto Nacional de Informática (NII) — como siete herramientas para usar con Claude Desktop y otros clientes MCP.
CiNii Research agrega metadatos de KAKEN, CiNii Articles, CiNii Books, IRDB, Crossref, DataCite, PubMed y NDL Search. No existe una herramienta MCP abierta establecida para ello, por lo que este servidor cubre esa carencia para investigadores que consultan la producción académica en japonés.
CiNii Research indexa la producción académica japonesa en cinco tipos de registro, y esto los pone todos dentro de una conversación de Claude: artículos de revistas, libros y monografías, tesis doctorales, proyectos de subvención KAKEN y perfiles de investigadores, además de la consulta de un único registro por CRID. Haz una pregunta en inglés y obtén producción académica en japonés, con el término japonés realmente enviado mostrado junto a los resultados.
KAKEN merece atención aparte: registra lo que se financió, por lo que saca a la luz proyectos en curso, colaboraciones en formación e investigaciones que llegaron a un informe de subvención antes de llegar a la imprenta.
Cada resultado lleva el término enviado, su escritura, cómo lo emparejó CiNii y un recibo que fija la consulta, de modo que una búsqueda que respalda una nota al pie pueda ser nombrada, citada y ejecutada de nuevo por otra persona.
Related MCP server: article-mcp
Herramienta | Propósito |
| Artículos de revistas (JALC, Crossref, PubMed, IRDB) |
| Libros y monografías (NACSIS-CAT, NDL Search) |
| Tesis doctorales de universidades japonesas |
| Proyectos de investigación financiados por KAKEN (科研費) |
| Búsqueda transversal en todos los tipos de contenido |
| Perfiles de investigadores y afiliaciones |
| Consulta de un único registro por URL o CRID |
Los resultados provienen de la API CiNii Research OpenSearch v2 como JSON-LD y se devuelven como un único sobre de respuesta JSON tipado; consulte Formato de respuesta más abajo. (Las versiones anteriores a la v2.0.1 devolvían texto markdown formateado; eso es un cambio disruptivo, no una preferencia de formato).
Cada herramienta devuelve un sobre de respuesta JSON, construido por mediation.py y definido en response-schema.json. Versión de esquema 2.3.0. El mismo módulo y esquema se incluyen byte-idénticos en toda la familia de servidores, de modo que un sobre de un servidor puede ser leído por un consumidor escrito para otro.
El sobre informa de cómo se realizó la búsqueda, no solo de lo que encontró:
searched_for — en las operaciones de búsqueda, el término realmente enviado, su escritura detectada y el modo de emparejamiento, elevados a la parte superior del sobre para que un cliente intermediario no pueda descartarlos. Las operaciones de recuperación (cinii_get_record) lo omiten: recibieron un identificador y no eligieron ningún término.
query — input_terms tal como se proporcionó, normalized tal como se envió y el script detectado. Este par es el registro de cualquier transformación realizada entre el idioma del llamante y el corpus.
matching_mode — metadata_conjunction para este servidor. Te indica cómo leer result.total.
result.breadth — none, narrow (1–50), broad (51–1000), very_broad (>1000). Los umbrales son bajos a propósito: unos cientos de resultados que parecen una bibliografía se marcan en lugar de dejarse pasar sin aviso.
items[].matched_in — en qué campo se realizó la coincidencia, por registro.
receipt — una marca de tiempo ISO 8601, un SHA-256 calculado sobre la consulta normalizada y sus parámetros, y los identificadores devueltos. El hash verifica un término que ya posees; no puede invertirse para producir uno, por lo que la unidad de depósito es el sobre, no el recibo.
attribution — la línea de atribución requerida, en cada respuesta.
Tipados y cerrados. Un diagnóstico nunca es prosa que el cliente tenga que analizar.
Código | Nivel | Significado |
| info | Registros devueltos; nada que señalar. |
| warning | Sin registros. CiNii empareja metadatos catalogados y aplica AND a una consulta de varias palabras, por lo que un compuesto no indexado devuelve cero incluso cuando existe trabajo relacionado. Varíe la transformación antes de concluir que la bibliografía está ausente. |
| warning | La consulta estaba en escritura latina, por lo que solo coincidió con metadatos romanizados y en inglés. La forma en escritura japonesa alcanza un corpus diferente y más amplio. |
| error | La API respondió, y respondió con un error. |
| error | La solicitud no se completó. Se mantiene distinto de |
| info | La respuesta no se escribió en el registro de consultas, porque no hay ningún destino de recibos configurado. La búsqueda no se ve afectada; no sobrevive ningún recibo de ella. |
| warning | Hay un destino de recibos configurado, se intentó la escritura y no se completó. Se distingue de la línea anterior porque una es una elección y la otra es un fallo. |
Cada sobre puede depositarse en un registro JSONL de solo añadido y encadenado por hash mediante ledger.py. Está desactivado a menos que se establezca MCP_RECEIPT_DIR (o el heredado MCP_RECEIPT_LOG), y un fallo de registro se traga en lugar de lanzarse: una búsqueda importa más que el registro de la misma. Los secretos se redactan antes de componer una línea.
Desde el esquema 2.3.0 el sobre lo dice. Cuando una respuesta no se deposita, emit() añade RECEIPT_NOT_DEPOSITED si la variable no está establecida, o RECEIPT_WRITE_FAILED si está establecida y la escritura no se completó. La brecha es entonces visible en el artefacto que se convierte en el registro, en lugar de solo en un archivo de configuración. mediation.deposit_enabled() informa del mismo hecho bajo demanda.
MCP_RECEIPT_DIR=C:\path\to\receipts # a folder, not a file
MCP_RECEIPT_SESSION=project-or-article-slug
MCP_RECEIPT_STRICT=1 # optional: make logging failure raise
MCP_RECEIPT_LOG=C:\path\to\receipts.jsonl # legacy single file; ignored when _DIR is setUna carpeta y un archivo por servidor. MCP_RECEIPT_DIR apunta a un directorio
y cada servidor escribe su propio <server>.jsonl dentro. Eso no es pulcritud.
Añadir es leer-el-último-hash-y-luego-escribir, y el bloqueo que lo rodea es un bloqueo
de hilos, que se mantiene dentro de un proceso y no entre varios: seis servidores son
seis procesos, y dos que respondan al mismo momento leerán ambos el mismo
predecesor y ambos lo reclamarán. Medido, no teorizado: seis procesos escribiendo 150
líneas en un archivo produjeron catorce bifurcaciones. MCP_RECEIPT_LOG sigue funcionando y
sigue siendo correcto para un solo servidor; es la forma equivocada para una familia.
install.ps1 configura esto para los seis y escribe un README en la carpeta.
Verifique una cadena, o toda la carpeta:
cinii-mcp-ledger verify receipts/cinii.jsonl
cinii-mcp-ledger verify-dir receipts
cinii-mcp-ledger manifest receipts # writes receipts/manifest.jsonverify sale con código distinto de cero en caso de fallo y dice qué tipo encontró: una bifurcación
(escritores concurrentes: un fallo de configuración, y cada línea sigue ahí), una línea
faltante, un reordenamiento o manipulación (una línea que no se corresponde con su
propio contenido). Solo la última es una afirmación sobre la honestidad, y reportarlas
por igual invitaría al lector a confundir una con la otra. El manifiesto es el
objeto a citar: una descripción de todo el depósito: recuentos de líneas por archivo,
primeras y últimas marcas de tiempo, hashes terminales y totales combinados por servidor,
escritura y sesión.
Python 3.10+ en PATH.
Un ID de aplicación de CiNii Web API (appid): gratuito; obligatorio.
La API de CiNii Research requiere un ID de aplicación registrado, enviado como parámetro en cada solicitud.
Regístrese en la página de Registro de desarrolladores de CiNii Web API y obtenga su ID de aplicación.
Acepte las regulaciones de la API de NII: las Regulaciones de Uso del Servicio de Contenido Académico, las Regulaciones Detalladas de Uso de CiNii Research y las Regulaciones Detalladas de Uso de la Web API del Servicio de Contenido Académico.
Para uso comercial, envíe un correo a ciniiadm@nii.ac.jp antes de solicitarlo.
El mismo ID de aplicación también funciona para la API de KAKEN, que usa cinii_search_kaken.
El paquete instala un script de consola cinii-mcp. Tiene espacio de nombres, por lo que puede
compartir un entorno con el resto de esta familia de servidores.
python3 -m venv .venv
.venv/bin/pip install .En Windows:
py -3.11 -m venv .venv
.venv\Scripts\pip.exe install .O directamente desde el repositorio, sin clonar:
uvx --from "git+https://github.com/ckgerteis/cinii-mcp" cinii-mcpVerifique la instalación:
.venv/bin/python -c "import cinii_mcp; print(cinii_mcp.__version__)"Eso falla de forma ruidosa si el paquete o uno de sus módulos incluidos falta. No
use cinii-mcp --help como comprobación: los argumentos desconocidos se ignoran, el
servidor se inicia, lee el fin de la entrada y sale con 0, por lo que informa éxito
cualquiera que sea el estado del código.
Seis paquetes independientes. Ninguno importa a otro, ninguno depende de otro, y
cada uno se instala y responde por sí solo: pip install . en este directorio es una
instalación completa de este servidor y nada más.
Sí comparten tres cosas: un sobre de respuesta, un registro de consultas y — si
ejecuta más de uno — una carpeta de recibos. install.ps1 se incluye byte-idéntico
en los seis y se encarga de eso. Instala este servidor por defecto, porque
clonar un repositorio no es una solicitud de cinco más.
.\install.ps1 # this server
.\install.ps1 -All # all six
.\install.ps1 -Servers cinii,cinii # a chosen subsetCualquier subconjunto que nombre se registra contra una carpeta de recibos, solicitada
una vez. El script prefiere una copia hermana a la red, traslada
las credenciales ya registradas en lugar de volver a preguntar, deja en paz a los servidores
sobre los que no se le preguntó y se detiene en lugar de adivinar donde los servidores
ya registrados discrepan sobre la carpeta o el slug de sesión. También verifica que
ledger.py y mediation.py son byte-idénticos en todo lo que
instaló, de modo que dos versiones de sobre no pueden terminar en un entorno sin ser notadas.
El servidor lee su ID de aplicación de la variable de entorno CINII_APPID. Copie el archivo de ejemplo y complételo (nunca confirme el valor real):
cp .env.example .envCINII_APPID=your_application_id_hereAñada una entrada a %APPDATA%\Claude\claude_desktop_config.json bajo
mcpServers, apuntando al script de consola en el entorno en el que instaló.
En macOS o Linux use la ruta absoluta a .venv/bin/cinii-mcp.
{
"mcpServers": {
"cinii": {
"command": "C:\\path\\to\\.venv\\Scripts\\cinii-mcp.exe",
"env": {
"CINII_APPID": "your_application_id_here"
}
}
}
}Cambiado en 3.0.0. Las versiones anteriores se registraban por ruta:
"command": "…\\python.exe", "args": ["…\\server.py"]. Esa entrada no
iniciará esta versión, porque server.py ahora es un módulo dentro de un paquete en lugar
de un script junto a sus importaciones. Reemplácela con el script de consola anterior.
Reinicie Claude Desktop. Las siete herramientas deberían aparecer bajo "cinii" en la lista de herramientas.
NII aplica reglas de uso; romperlas puede hacer que le bloqueen el acceso o le cancelen el registro. Este servidor envía su appid en cada solicitud (obligatorio) y está diseñado para respetar las reglas, pero usted sigue siendo responsable del uso:
No realice un gran volumen de solicitudes en un corto período de tiempo. El acceso excesivo que afecte a otros usuarios puede ser bloqueado sin previo aviso.
El appid es solo para solicitudes de API; no lo exponga en enlaces visibles para los usuarios a páginas de CiNii.
Respete los derechos de autor al utilizar los datos obtenidos, según las regulaciones de NII.
Si este software respalda su investigación, por favor cítelo. Consulte CITATION.cff o use el botón "Cite this repository" en GitHub.
MIT © 2026 Christopher Gerteis.
Esta licencia cubre únicamente el código del servidor. No otorga ningún derecho sobre los datos de CiNii ni sobre la API de CiNii, que siguen regidos por los términos de NII enlazados anteriormente.
Una herramienta de investigación, mantenida con el mejor esfuerzo posible y proporcionada "tal cual", sin garantía. No está afiliada ni respaldada por el National Institute of Informatics.
Dr Christopher Gerteis, SOAS University of London. Datos proporcionados por CiNii Research, National Institute of Informatics.
Fetch a single CiNii record by URL or CRID. Returns the unified envelope (operation 'get_record').
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint, covering the safety profile. The description adds value by stating the return envelope format (operation 'get_record'), which is not in annotations. No contradiction; it contextually enriches what the tool returns.
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 crisp sentences: the first states the action and input, the second the expected output. Front-loaded with the core purpose and no filler. Every word earns its place.
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?
For a single-record fetch with a straightforward input and an output schema provided, the description covers the essential behavior. It mentions the envelope and the operation. The only omission is potential error handling or edge cases, but given the output schema and annotations, it is sufficiently complete for an agent to use 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% because the tool description does not discuss parameters. The single parameter 'record_url' is described in the schema as 'Full CiNii URL or CRID', but the description does not compensate for the low coverage. It adds nothing beyond the schema, so the agent must rely solely on the schema's minimal description.
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 states a specific verb ('Fetch'), a resource ('single CiNii record'), and the two identifier forms ('by URL or CRID'), which clearly distinguishes it from the sibling search tools (cinii_search_*). It also notes the return envelope with operation 'get_record', making the tool's purpose unambiguous.
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: you need a specific URL or CRID, which differentiates it from the search siblings. However, it does not explicitly say 'use this when you have an identifier' nor name the alternatives. The context of siblings makes it clear enough, but explicit guidance is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Cross-type search across all CiNii content. Returns the unified envelope.
Records are emitted with record_type 'article' as a default; the cross search mixes types and CiNii does not always disambiguate them in the OpenSearch response.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavioral detail beyond the annotations: records default to record_type 'article', mixed types are not always disambiguated, and a unified envelope is returned. This is exactly the kind of caveat an agent needs before relying on the output.
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 compact and front-loaded: purpose first, then output envelope, then the critical record_type caveat. Every sentence earns its place with no filler.
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?
The output schema and annotations cover return shape and safety, and the description covers the important cross-type ambiguity. Parameter semantics are missing from the description, but the schema's property names, defaults, and constraints make invocation reasonably clear.
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%, and the description does not explain query, lang, count, start, from_year, or to_year. With no parameter guidance in the description, it fails to compensate for the low schema coverage.
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 identifies a specific operation: cross-type search across all CiNii content. 'All CiNii content' distinguishes this from the type-specific sibling tools without needing to open their schemas.
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 gives clear context: use this for cross-type/all-content searching. It does not explicitly name alternatives or state when not to use it, but the scope is sufficiently explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search CiNii Research for journal articles. Returns the unified envelope.
CiNii matches catalogued metadata and ANDs a multi-word query, so an un-indexed compound returns zero even when related work exists — a ZERO_CONJUNCTION diagnostic marks this; vary the rendering rather than concluding the literature is absent. A SCRIPT_LATIN_QUERY diagnostic means the query searched romanized metadata only. The same string may behave very differently on J-STAGE (full text). Foundational monographs sit in cinii_search_books, not the article index.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| 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 false, covering safety. The description adds substantial behavioral detail: it explains the ANDing of multi-word queries, the ZERO_CONJUNCTION diagnostic suggesting the query may be unindexed, the SCRIPT_LATIN_QUERY diagnostic for romanized-only searches, and the difference from J-STAGE full-text searching. This goes well beyond the annotations and gives the agent critical insights for interpreting results correctly.
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 two short paragraphs. The first sentence states the purpose and return envelope. The second paragraph packs three sentences of useful caveats. It is front-loaded with the core purpose and each subsequent sentence earns its place by clarifying search behavior or pointing to the right sibling tool. There is no fluff or repetition, making it highly concise 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?
The description covers the essential behavioral quirks that could lead an agent astray (zero results, romanized search, J-STAGE differences) and routes monographs to the correct tool. It does not explain the 'unified envelope' return format, but an output schema exists so that is acceptable. It also does not detail pagination or sorting semantics, but those are likely standard and inferable from the schema. The description is sufficient for effective use given the existing schema and annotations.
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 has a description for the 'query' parameter, but the overall schema coverage is low (0% per signals, though query has a description). The description compensates by explaining how the query is interpreted (ANDs multi-word queries, may hit romanized metadata), which directly affects how to construct the query. It does not explain other parameters like sort, count, or filters, but those are standard and have defaults. Given the low coverage, the description adds meaningful semantic value for the most critical parameter, so a 4 is warranted.
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 states 'Search CiNii Research for journal articles' — a specific verb and resource, clearly distinguishing it from the other CiNii tools. It also explicitly notes that monographs belong in cinii_search_books, reinforcing the boundary to sibling tools. This is unambiguous and immediately tells an agent what the tool does and what it does not cover.
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 when-to-use context: it tells the agent that the article index is for journal articles and that monographs should be searched in cinii_search_books. It also warns about behavioral differences from J-STAGE, which helps the agent decide if this is the right search. However, it does not explicitly name all alternatives (e.g., cinii_search_all) nor provide a comprehensive when-not-to-use list, so it slightly lacks in guiding against other nearby tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search CiNii Research for books and monographs. Returns the unified envelope.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide safety information (readOnlyHint=true, idempotentHint=true, destructiveHint=false). The description adds only the phrase 'Returns the unified envelope', which hints at the output format but is redundant given the output schema exists. It does not add behavioral context such as pagination limits, potential delays, or any special handling. Since annotations are present, the bar is lower, but the description still contributes almost nothing beyond the schema.
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, compact sentence that is easy to read. It is appropriately sized for a simple search tool, but it is overly sparse — it does not elaborate on scope or usage. It is concise without being informative, so it earns a middle score.
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 a rich schema with 10 parameters and is part of a family of similar search tools, the description is insufficient. It does not mention which parameters to use for common scenarios, does not clarify the 'unified envelope' output structure beyond the schema, and omits any guidance on how this tool differs from its siblings. The presence of an output schema covers return format but not usage context.
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% — the description does not explain any of the parameters (query, isbn, title, author, etc.). While some parameter names are self-explanatory, the description offers no guidance on how they interact or which are mutually exclusive. With low coverage, the description must compensate, but it does not, leaving the agent to rely on the schema alone.
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 states a specific verb ('Search') and a clear resource ('CiNii Research for books and monographs'). It implicitly differentiates from sibling search tools by specifying 'books and monographs', which is distinct from articles, dissertations, and researchers. However, it does not explicitly name a sibling or contrast them, so a 4 is appropriate rather than a 5.
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?
There is no guidance on when to use this tool versus alternatives. The description does not mention that this is the tool for book/monograph searches or that other tools are for different document types. No prerequisites, exclusions, or alternative tools are referenced, leaving the agent to infer usage solely from the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search CiNii Research for doctoral dissertations. Returns the unified envelope.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey readOnly, openWorld, idempotent, and non-destructive behavior, so the description need not repeat those. However, the only additional behavioral information, 'Returns the unified envelope,' is cryptic and unexplained, leaving the agent unsure about the actual output structure. This adds little transparent value.
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 short (a single sentence), so it is concise in word count, but that brevity comes at the cost of essential detail. It lacks any structure (e.g., bullets, sections) to organize information, and the sentence itself is too terse to be complete.
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?
Despite having an output schema and a 7-field nested input schema, the description provides almost no context. It does not explain how to form queries, what the 'unified envelope' contains, or how filters work. An agent cannot confidently call this tool without additional documentation, making it severely inadequate for the tool's 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?
Schema description coverage is 0%, so the description has the full burden of explaining parameters. It mentions none of the seven parameters (lang, count, query, start, author, to_year, from_year) nor their meaning. The agent must rely solely on field titles and defaults, which is insufficient for correct invocation.
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'), the resource ('CiNii Research'), and the specific scope ('doctoral dissertations'). It inherently distinguishes itself from sibling tools that target articles, books, researchers, etc., through the explicit mention of dissertations.
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 given on when to use this tool versus the alternative search tools (e.g., cinii_search_all, cinii_search_articles). The use case is only implied by the tool name and scope, with no explicit 'use this when' or 'for other content types use...' instruction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search KAKEN (科研費) research projects. Returns the unified envelope (record_type 'project').
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| 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, covering safety and side-effect expectations. The description adds that it returns the unified envelope with record_type 'project', which is a useful behavioral detail. However, it doesn't disclose pagination behavior, result ordering, or potential rate limits. With annotations covering the main traits, the added value is modest but non-trivial.
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, front-loaded sentence with no filler. It communicates the core purpose and the key return-type detail efficiently, which is ideal for an AI agent that needs to quickly parse tool intent.
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 multiple optional parameters and 0% schema coverage, the description is under-specified. It doesn't explain how to construct a valid query, how filters interact, or any constraints. An output schema exists but is not visible in the prompt; the description only hints at the return envelope. An agent would likely need to inspect the schema or make trial calls to use the tool effectively.
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 compensate for the meaning of parameters like query, lang, count, start, from_year, to_year, researcher, and institution. The description only mentions the search action and return type, providing no explanation of how to use the filters. Field names are self-explanatory to some degree, but without any description guidance, an agent may not know parameter formats or combinations.
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 states a specific verb ('Search') and a clear resource ('KAKEN research projects'), and it distinguishes itself from sibling search tools by specifying the record_type 'project' in the unified envelope. This makes the tool's purpose unambiguous even without reading the schema.
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 for KAKEN projects but does not explicitly contrast with alternatives such as cinii_search_articles or cinii_search_all. There is no 'use this when' or 'not for' guidance. The sibling list is provided in context but the description itself doesn't reference it, so an agent must infer when to choose this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for researchers in CiNii. Returns the unified envelope (record_type 'researcher').
Note: researcher affiliation is not carried by the record schema; the researcher name occupies the title field and the profile URL the ids.url_ja field.
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |
| 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 a useful, non-obvious note about field mapping (name in title, profile URL in ids.url_ja) that goes beyond the schema. No contradictions; the note clarifies result interpretation without repeating annotation information.
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 followed by a clearly separated note. The main purpose is front-loaded, and the note is relevant without bloating the text. 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?
Given the schema has no parameter descriptions and the tool has multiple parameters (query, institution, pagination controls), the description is incomplete. The field-mapping note is helpful, but it doesn't cover parameter semantics or usage context. An agent would need to infer most functional details from parameter names alone.
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%, and the description mentions none of the parameters (query, lang, count, start, institution). The tool requires more than one parameter in practice (via the nested 'params' object), yet the description provides no semantic help, leaving the agent to guess from names alone.
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 'Search for researchers in CiNii' with a specific verb and resource, and mentions the record_type 'researcher'. It differentiates from siblings like cinii_search_articles by resource type, though it doesn't explicitly name alternatives. The purpose is unambiguous.
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 cinii_search_all or other sibling search tools. There is no mention of scenarios, prerequisites, or exclusions, leaving the agent to infer usage context from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each search tool explicitly targets a distinct content type (articles, books, dissertations, KAKEN projects, researchers, and a cross-type search), with no overlap in purpose. The get_record tool is clearly separate as a single-record fetcher by URL or CRID.
All tools follow the identical pattern 'cinii_search_<type>' for searches, plus 'cinii_get_record' for retrieval, maintaining consistent snake_case and verb-noun ordering throughout.
Seven tools is well-scoped for a literature search MCP server, covering the major CiNii content types without redundancy or unnecessary bloat. Each tool earns its place.
The surface covers all primary search categories (articles, books, dissertations, KAKEN, researchers) plus an all-search and a record fetch, leaving no obvious gaps for the stated purpose of querying CiNii Research.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Academic research MCP server for paper search, citation checks, graphs, and deep research.
Federated search of books and papers, BibTeX/RIS citations, open-access retrieval and reading.
Read-only MCP tools for AI agent discovery, structured resources, and NIULAI information.
Citable retrieval across papers, books, patents, Wikipedia, and live social sources.
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