Seiche, LiquiLens and Undertow source data
Funding histories, bank filings and settlement data with source receipts, units and capture clocks.
Should I use this
Quality & Safety
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"financial-source-data": {
"url": "https://api.seiche.info/api/v2/research-data/mcp"
}
}
}Remote endpoints
https://api.seiche.info/api/v2/research-data/mcpstreamable-httpWhat it can do
Tool inventory
Tools (6)
🟢research_tracked_institutions(scope, q, limit, offset)
Search the existing Indian dossier and RBI registry populations, retaining financial-evidence gaps and source clocks. Populations overlap and are not a global census.
Input Schema
{
"type": "object",
"properties": {
"scope": {
"type": "string",
"enum": [
"dossiers",
"rbi_registry"
]
},
"q": {
"type": "string",
"maxLength": 150
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 500
},
"offset": {
"type": "integer",
"minimum": 0,
"maximum": 10000000
}
},
"additionalProperties": false
}🟢research_catalog(product)
Discover scoped funding, institution and chain-settlement datasets with coverage and rights.
Input Schema
{
"type": "object",
"properties": {
"product": {
"type": "string",
"enum": [
"seiche",
"liquilens",
"undertow"
]
}
},
"additionalProperties": false
}🟢research_series(dataset, q, limit, offset)
Search the metric catalog and native unit definitions in a dataset.
Input Schema
{
"type": "object",
"properties": {
"dataset": {
"type": "string"
},
"q": {
"type": "string"
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 10000
},
"offset": {
"type": "integer",
"minimum": 0,
"maximum": 10000000
}
},
"required": [
"dataset"
],
"additionalProperties": false
}🟢research_entities(dataset, q, limit, offset)
Find regulator identifiers for institutions; names are not fuzzy-joined across regimes.
Input Schema
{
"type": "object",
"properties": {
"dataset": {
"type": "string"
},
"q": {
"type": "string"
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 10000
},
"offset": {
"type": "integer",
"minimum": 0,
"maximum": 10000000
}
},
"required": [
"dataset"
],
"additionalProperties": false
}🟢research_observations(dataset, series, entity, start, end, ...)
Read cited observations or a captured historical vintage. Earlier point-in-time availability is not assumed.
Input Schema
{
"type": "object",
"properties": {
"dataset": {
"type": "string"
},
"series": {
"type": "string"
},
"entity": {
"type": "string"
},
"start": {
"type": "string"
},
"end": {
"type": "string"
},
"as_known_at": {
"type": "string"
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 10000
},
"offset": {
"type": "integer",
"minimum": 0,
"maximum": 10000000
}
},
"required": [
"dataset",
"series"
],
"additionalProperties": false
}🟢research_analysis(product)
Read descriptive funding, institution concentration and settlement analysis with input references and limits.
Input Schema
{
"type": "object",
"properties": {
"product": {
"type": "string",
"enum": [
"seiche",
"liquilens",
"undertow"
]
}
},
"additionalProperties": false
}Community
Evidence