_ registry / mcp http-sse · checked 14m ago

MoSPI Data Server

https://mcp.mospi.gov.in

Registry code: cb5e4c2e864fb3df

api record

This API provides access to statistical datasets from the Ministry of Statistics and Programme Implementation (MoSPI), allowing users to list datasets, retrieve indicators, get metadata, and fetch specific data.

endpoint
https://mcp.mospi.gov.in
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
live
uptime
100%
latency
928ms

last good check

priced tools
0

of 4 tools

_ what it is for
topic
knowledge & reference datasets
used for
  • list available datasets
  • get indicators for dataset
  • get filter values for dataset
  • fetch statistical data
takes → gives
text → data
tools
4 reads
_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 4 tools
4 never probed 0 of 4 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • get_metadata reads unknown never probed

    Returns the valid filter values (states, years, quarters, etc.) for a given dataset and indicator. Filter codes are arbitrary and dataset-specific — for example, PLFS state_code 99 means "All India", and NAS frequency_code 1 means "Annual". These values cannot be inferred or guessed from parameter names alone. The returned filter_values and api_params should be used as-is when calling get_data. Step 3 of: list_datasets → get_indicators → get_metadata → get_data

    mcp-tool

    {
  "type": "object",
  "required": [
    "dataset"
  ],
  "properties": {
    "type": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "level": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Required for CPI (\"Group\"/\"Item\")."
    },
    "Format": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "module": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "For NSS77 only ΓÇö land_livestock (portal nss77) or aidis (portal nss77a).\n    Required when indicator_code overlaps both modules (16-19, 24, 26, 29, 32, 34, 36).\n    Auto-derived for codes unique to one module."
    },
    "series": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "For CPI and NAS only (\"Current\"/\"Back\")."
    },
    "dataset": {
      "type": "string",
      "description": "Dataset name (same values as get_indicators)."
    },
    "base_year": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Required for CPI (\"2024\"/\"2012\"/\"2010\"),\n       IIP (\"2011-12\"/\"2004-05\"/\"1993-94\"),\n       NAS (\"2022-23\"/\"2011-12\"),\n       WPI (\"2011-12\"/\"2004-05\"/\"1993-94\").\n       Not applicable for PLFS, ASI."
    },
    "frequency": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Required for IIP (\"Annually\"/\"Monthly\")."
    },
    "state_code": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "For NAS only — required when indicator_code is 23-34\n        (state-level GSDP/NSDP/GSVA family). Values 01-37, where\n        37 = All-India aggregate. Comma separated for multiple\n        states. Not applicable when frequency_code=2 (Quarterly) —\n        state-level indicators have Annual data only."
    },
    "survey_code": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "For NSS76 (1=Disability, 2=Housing & drinking water), NSS75E\n         (2=Education, indicators 43-55), NSS80 (1=Telecom (CMST), 2=Education (CMSE)),\n         and NSS77 AIDIS module (always 1)."
    },
    "frequency_code": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Required for PLFS and ASUSE.\n            PLFS: 1=Annual, 2=Quarterly bulletin, 3=Monthly.\n            ASUSE: 1=Annual, 2=Quarterly."
    },
    "indicator_code": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Required for: PLFS, NAS, ENERGY, AISHE, ASUSE, GENDER,\n            NFHS, ENVSTATS, RBI, NSS77, NSS78, NSS76, NSS75E, NSS79, CPIALRL, HCES, TUS, EC, UDISE, MNRE, NSS80, NSS73.\n            Not applicable for: CPI, IIP, ISP, ASI, WPI.\n            For RBI, this maps to sub_indicator_code internally."
    },
    "sub_indicator_code": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "classification_year": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Required for ASI (\"2008\"/\"2004\"/\"1998\"/\"1987\")."
    },
    "use_of_energy_balance_code": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "For ENERGY only (1=Supply, 2=Consumption)."
    }
  },
  "additionalProperties": false
}
    arguments 178 lines
  • get_indicators reads unknown never probed

    Returns the full list of available indicators for a given dataset. Datasets often have broader coverage than expected — for example, ASI covers 57 indicators (capital structure, wages, employment, GVA, fuel consumption), and GENDER covers 147 indicators across health, education, labor, and crime. For PLFS and ASUSE, indicators are grouped by frequency_code: - PLFS frequency_code=1 (Annual): all 8 indicators including wages - PLFS frequency_code=2 (Quarterly): indicators 1-3 only - PLFS frequency_code=3 (Monthly): indicators 1-3 only frequency_code selects the indicator set, not time granularity. Step 2 of: list_datasets → get_indicators → get_metadata → get_data

    mcp-tool

    {
  "type": "object",
  "required": [
    "dataset"
  ],
  "properties": {
    "level": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "Format": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "series": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "dataset": {
      "type": "string",
      "description": "Dataset name ΓÇö one of: PLFS, CPI, IIP, ISP, ASI, NAS, WPI,\n     ENERGY, AISHE, ASUSE, GENDER, NFHS, ENVSTATS, RBI,\n     NSS77, NSS78, NSS76, NSS75E, NSS79, CPIALRL, HCES, TUS, EC, UDISE, MNRE, NSS80, NSS73.\n     For CPI, IIP, ISP, WPI: returns available base years and frequencies."
    },
    "base_year": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "frequency": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "user_query": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "The user's original question. Captured for telemetry analytics; not echoed back in the response."
    },
    "frequency_code": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "classification_year": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    }
  },
  "additionalProperties": false
}
    arguments 102 lines
  • get_data reads unknown never probed

    Fetches statistical data from a MoSPI dataset. This is the final step of the workflow. It requires filter values from get_metadata — filter codes are arbitrary (e.g., indicator_code=3 means "Unemployment Rate" in PLFS but something different in other datasets). All filter parameters including limit and page go inside the filters dict, not as top-level arguments. Step 4 of: list_datasets → get_indicators → get_metadata → get_data

    mcp-tool

    {
  "type": "object",
  "required": [
    "dataset",
    "filters"
  ],
  "properties": {
    "dataset": {
      "type": "string",
      "description": "Dataset name (PLFS, CPI, IIP, ISP, ASI, NAS, WPI, ENERGY,\n     AISHE, ASUSE, GENDER, NFHS, ENVSTATS, RBI, NSS77,\n     NSS78, NSS76, NSS75E, NSS79, CPIALRL, HCES, TUS, EC, UDISE, MNRE, NSS80, NSS73).\n     CPI auto-routes to Group or Item endpoint based on\n     whether filters contain item_code.\n     IIP uses a single endpoint; pass frequency=\"Annually\" or\n     frequency=\"Monthly\" in filters."
    },
    "filters": {
      "type": "object",
      "description": "Key-value pairs from get_metadata filter_values.\n     PLFS requires frequency_code (1=Annual, 2=Quarterly, 3=Monthly).\n     NAS requires base_year (\"2022-23\" or \"2011-12\").\n     MNRE: indicator_code (1-5) is mapped to type_of_renewable_energy_code.\n     Pass limit (e.g., \"50\") to retrieve more than 10 records.",
      "additionalProperties": true
    }
  },
  "additionalProperties": false
}
    arguments 19 lines
  • list_datasets reads unknown never probed

    Returns an overview of all MoSPI statistical datasets with descriptions and coverage. This is the starting point ΓÇö call this first to identify the right dataset. The API covers 500+ indicators across employment, prices, industry, national accounts, health, education, disability, housing, environment, trade, and more. Each dataset has its own indicator codes, filter parameters, and valid values ΓÇö these are not standardized and cannot be inferred or guessed from parameter names alone. Four-step workflow (each step depends on the previous): 1. list_datasets() ΓÇö identify the dataset 2. get_indicators(dataset) ΓÇö list available indicators 3. get_metadata(dataset, indicator_code) ΓÇö retrieve valid filter values 4. get_data(dataset, filters) ΓÇö fetch the data Returns: dict with 'datasets' (name, description, use_for for each dataset) and 'workflow' (the four-step sequence).

    mcp-tool

    {
  "type": "object",
  "properties": {},
  "additionalProperties": false
}
    arguments 5 lines
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