Context7
by upstash
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Computer or cloud★ 64,224MITUpdated yesterday
Install
Agentcast
Overview
Description from the maker (original English)
Structured-output enforcer: extract and validate JSON from messy LLM text.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiNot measured yet★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Search & AI memory
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- npmNode.js installed (for the npx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"agentcast": {
"command": "npx",
"args": [
"-y",
"@mukundakatta/agentcast-mcp"
]
}
}
}Run this in your terminal
claude mcp add agentcast -- npx -y @mukundakatta/agentcast-mcpOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"agentcast": {
"command": "npx",
"args": [
"-y",
"@mukundakatta/agentcast-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"agentcast": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@mukundakatta/agentcast-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account MukundaKatta, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Search & AI memoryi
- More stars than 0% of the other 7281 servers in this category
- Published to the registry more recently than 10% of the other 7281 servers in this category
- Basic checks equal to or better than 100% of the other 7281 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Agentcast safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 6 months ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
What can I use instead?
In the same category: Context7, Codebase Memory, Exa
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Codebase Memory
by DeusData
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Runs on your computer★ 48,148MITUpdated 21 days ago
Install
Exa
by exa-labs
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Runs in the cloud★ 5,213MITUpdated last month
Install
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
agentcast-mcp
An MCP server that gives AI assistants the ability to enforce structured output: extract JSON from messy LLM text, gate it against a shape spec, and produce the retry feedback message when the model returns the wrong shape.
Built on top of @mukundakatta/agentcast. Works with Claude Desktop, Cursor, Cline, Windsurf, Zed, and any other MCP client.
Tools exposed
extract_json
Pull a JSON value out of messy LLM output. Tries the whole text, then a fenced json block, then the largest balanced {...} / [...] substring. Returns the parsed value plus which strategy succeeded.
{"text": "Sure, here you go:\njson\n{\"answer\": 42}\n\nLet me know!" }
→
{"value": { "answer": 42 }, "found": true, "source": "fenced_json" }
source is one of whole, fenced_json, fenced_plain, balanced_substring, or none.
validate_response
Validate a parsed JSON value against an agentcast shape spec. Spec maps field name to type: string, number, boolean, array, object. Suffix with ? for optional.
{"value": { "name": "ada" }, "shape": { "name": "string", "age": "number" } }
→
{"valid": false, "error": "missing required field 'age'" }
build_retry_prompt
Given an attempt history, produce the validation-error feedback message agentcast appends to the conversation when the model returned the wrong shape. Codifies the "validation error as feedback" pattern for non-Node MCP clients that want to drive the same retry loop manually.
{"attempts": [ { "text": "{\"name\":\"ada\"}", "error": "missing required field 'age'" } ], "expected_shape": { "name": "string", "age": "number" } }
→
{"feedback": "Your previous response did not match the required shape. Error: missing required field 'age'\n\nTry again. Respond with ONLY valid JSON that fixes the error above.\n\nExpected shape: {\"name\":\"string\",\"age\":\"number\"}" }
Install
Claude Desktop
Add to claude_desktop_config.json:
{"mcpServers": { "agentcast": { "command": "npx", "args": ["-y", "@mukundakatta/agentcast-mcp"] } } }
Cursor / Cline / Windsurf / Zed
Same shape, in the appropriate mcp.json for your client. Most clients auto-discover via npx -y @mukundakatta/agentcast-mcp.
Local install
npm install -g @mukundakatta/agentcast-mcpmcp-agentcast # listens on stdio
Why this matters
When an LLM is supposed to return structured data, it sometimes wraps the JSON in prose, fences, or hallucinated fields. Standard JSON.parse throws. Hand-rolled regex misses nested structure. This MCP server gives any model driving an agent a real handle on (1) pulling JSON out of the response, (2) checking it matches the expected shape, and (3) building the exact retry prompt that nudges the model to fix it on the next turn.
License
MIT.