Indian court data, inside your AI assistant
The CourtMesh MCP server turns our twelve court data endpoints into tools that Claude, Claude Code and Cursor can call on their own. Ask a research question in the client you already use, and the assistant searches roughly 310 million Indian court records, opens the judgment and cites what it actually read.
Live. The hosted endpoint at https://mcp.courtmesh.ai/mcp is accepting connections and serves all twelve tools over Streamable HTTP. Your API key travels with the request, either as an Authorization Bearer header or as a token query parameter, and calls are billed against the same API credits as the REST API. Access follows your API plan, so if the REST API is not enabled for your account yet the tools will return the same 401 the API returns.
Background
What is the Model Context Protocol?
MCP is an open standard for connecting AI assistants to outside systems. A server publishes a set of tools, the client discovers them at connect time, and the model decides when to call them. Write one server, and every compatible client can use it.
Why it matters for lawyers
A general purpose model asked about Indian case law will sometimes produce a citation that looks immaculate and does not exist. Reported judgments are the one place in legal work where a plausible invention is worse than no answer at all. With the CourtMesh tools connected, the assistant stops guessing and starts retrieving. It runs a real search, opens the real record, and hands back a case identifier and a link to the judgment PDF you can open before anything reaches a draft. Research that used to mean twenty browser tabs becomes a conversation that ends with sources you can verify.
Why it matters for developers
You skip the integration entirely. There is no client library to pin, no pagination helper to write, no retry logic and no schema to keep in sync with our releases. Point Claude Code or Cursor at one URL, and your coding agent can query real judgments while it builds the feature that consumes them. When you are ready to ship, the same twelve operations are available as plain REST, so a prototype driven from a chat window turns into production code without a rewrite.
Tools
Twelve tools, one connection
Each tool maps one to one onto a documented CourtMesh REST endpoint, so anything an assistant can do here you can also do from your own backend. Credit costs are identical across both surfaces.
| Tool | REST endpoint | Credits | What it does |
|---|---|---|---|
| search_indian_court_cases | POST /search/cases | 1 | Keyword search across roughly 310 million Indian court records with filters for court, year, judge, case type and date, plus cursor pagination for long result sets. |
| semantic_search_cases | POST /search/cases/semantic | 10 | AI semantic search over the vector index. The assistant can ask in plain language, for example find judgments where a delay condonation application was allowed despite a two year delay. |
| get_case | GET /cases/{id} | 1 | Full structured case record: parties, bench, court, dates, citations, statutes referenced and judgment text. |
| get_case_analysis | GET /cases/{id}/analysis | 1 | Reads a stored AI analysis of a judgment if one already exists, so the assistant does not regenerate work you have already paid for. |
| find_related_cases | GET /cases/{id}/related | 1 | Related documents and the derived order timeline sharing the same case number, useful for tracing a matter through its appeals. |
| get_case_pdf_url | GET /cases/{id}/pdf | 5 | Returns a signed link to the judgment PDF so the assistant can cite or attach the original document. |
| analyze_case | POST /cases/{id}/analyze | 150 | Generates a full AI analysis: issues framed, arguments of both sides, statutes applied, precedent treatment and case strength signals. |
| analyze_consolidated_case | POST /cases/{id}/analyze-consolidated | 400 | Consolidated analysis across every document attached to a matter, for when one judgment is not the whole story. |
| search_judges | GET /judges/search | 1 | Looks up Supreme Court and High Court judges by name, the fastest way to resolve a bench reference before a search. |
| request_case_timeline | POST /request-timeline | 20 | Starts an asynchronous job that assembles the orders and hearing timeline for a case number. |
| get_case_timeline | GET /get-timeline/{requestId} | 0 | Polls a timeline job and returns the assembled timeline once ready. Free, so the assistant can wait without burning credits. |
| check_api_health | GET /health | 0 | Service availability probe. Free and unmetered. |
Full request and response schemas for each underlying endpoint are in the API documentation, and the credit cost of every call is published on the API pricing page.
Setup
Connected in three steps
The server is remote and stateless. Nothing runs on your machine except the client you already have, and there is no build step to maintain.
1. Issue an API key
Create a CourtMesh account and generate a key from the API settings screen. Keys look like cm followed by a random string. API access is on Enterprise accounts today. The self serve tiers, including the free credit grant, are rolling out, and the pricing page has the ladder in full.
2. Point your client at the server
Add the configuration below to Claude Desktop, Claude Code or Cursor. The server speaks streamable HTTP at a single URL and takes your API key as a bearer token, so there is nothing to install, host or keep updated.
3. Ask in plain language
Restart the client and the CourtMesh tools appear in its tool list. Ask a research question the way you would ask a junior, and the assistant picks the right tool, runs the search and cites the judgment it used.
Claude Desktop
Open Settings, then Developer, then Edit Config, and merge this into claude_desktop_config.json. Restart Claude Desktop and the CourtMesh tools appear in the tools menu.
{
"mcpServers": {
"courtmesh": {
"command": "npx",
"args": [
"-y",
"mcp-remote",
"https://mcp.courtmesh.ai/mcp",
"--header",
"Authorization: Bearer ${COURTMESH_API_KEY}"
],
"env": {
"COURTMESH_API_KEY": "cm-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx-xxxx"
}
}
}
}Claude Code
One command adds the server to your user scope. To share it with your team instead, commit the JSON below as .mcp.json in the repository root.
claude mcp add --transport http courtmesh https://mcp.courtmesh.ai/mcp \
--header "Authorization: Bearer cm-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx-xxxx"{
"mcpServers": {
"courtmesh": {
"type": "http",
"url": "https://mcp.courtmesh.ai/mcp",
"headers": {
"Authorization": "Bearer cm-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx-xxxx"
}
}
}
}Cursor
Add this to ~/.cursor/mcp.json for every project, or to .cursor/mcp.json inside a single repository. Then enable CourtMesh under Settings, MCP.
{
"mcpServers": {
"courtmesh": {
"url": "https://mcp.courtmesh.ai/mcp",
"headers": {
"Authorization": "Bearer cm-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx-xxxx"
}
}
}
}Other clients
Any client that supports remote MCP servers over streamable HTTP with a custom header will work, including ChatGPT custom connectors, Windsurf, Zed, VS Code agent mode and anything you build with an MCP SDK. Supply the server URL and an Authorization header carrying your key as a bearer token. The REST API accepts the same key in an X-API-Key header if you prefer that style in your own client.
Billing
No new bill, just your API credits
The MCP server is a different doorway into the same API. There is no MCP subscription, no per seat charge and no separate wallet to top up.
Same credits, same prices
A tool call costs exactly what the underlying endpoint costs. Search is 2 credits, semantic search is 10, a stored analysis read is 3. Health checks and timeline polling are free.
Free to start
Every CourtMesh account begins with 2,500 API credits that never expire, plus 500 more free credits every month for as long as the account exists. That is enough to run the tools daily at no cost.
Your balance is the ceiling
An assistant can never spend credits you have not already bought. When the balance is short, the call is refused before it runs and nothing is charged. There is no overage invoice.
Usage is logged per API key, so you can see what an assistant spent and separate it from your backend traffic. If a workflow starts consuming real volume, prepaid packs start at ₹1,000 for 10,000 credits and the Scale plan carries 1,00,000 credits a month with rollover. The full ladder is on the API pricing page, and application plans for the Research, My Cases and My Agreements products are on the pricing page.
FAQ
MCP server questions
Indian Court Cases API
The REST surface behind every tool on this page.
OpenAPI pricing
Credit cost of all 12 endpoints, free tier and packs.
OpenDocumentation
Authentication, schemas, pagination and error codes.
OpenContact us
Rollout access, enterprise volume and security review.
OpenGive your assistant a law library
Issue a key, paste one configuration block, and the next research question you ask Claude or Cursor comes back with real Indian judgments attached. Your first 2,500 credits are free and they never expire.