searchcode.com

A postal code API returns structured location data for a given ZIP Code or Canadian postal code. A typical response includes the code itself, city, state or province, county, latitude/longitude, and — where available — ZIP+4 detail. More complete services add time zone, area codes, boundary geometry, and demographic fields. You send a code (or an address), and the API sends back a machine-readable record you can store, validate, or display.

This article explains what those responses contain, how requests are usually shaped, and where postal code data fits into real applications.

First, clear up the word "code"

The keyword "code" is overloaded, and that causes real confusion for developers:

• Postal code — the ZIP Code (U.S.) or postal code (Canada) that identifies a delivery area.
• API key — the credential you use to authenticate your requests. It is not postal data.
• Source code — the program you write to call the API.

When someone searches for "postal code API code," they usually want example request/response code for a postal code service. The rest of this article treats it that way.

What a postal code API actually returns

Response fields vary by provider and endpoint, but the core set is fairly consistent. A single-code lookup commonly returns:

Richer datasets add 90+ fields: boundaries, population, income, elevation, and more. You rarely need all of them — request only what your application uses.

A representative JSON response

{
  "postal_code": "90210",
  "city": "Beverly Hills",
  "state": "CA",
  "county": "Los Angeles",
  "latitude": 34.0901,
  "longitude": -118.4065,
  "timezone": "America/Los_Angeles",
  "area_codes": ["310", "424"]
}

XML responses carry the same information in tag form. Choose based on what your stack parses most easily; JSON is the common default.

Common request patterns

Most postal code APIs support four patterns. Knowing which one you need prevents wasted calls.

1. Lookup by code

You have a code and want its details. This is the simplest and fastest call.

GET /lookup?code=90210

2. Reverse lookup by address

You have a street address and want to confirm or complete the code. This is the pattern behind checkout address validation.

GET /validate?street=...&city=...&state=...

3. Radius search

You have a center point and want all codes within a distance. Useful for store locators and delivery zones.

GET /radius?code=90210&miles=10

4. Batch validation

You have a file of addresses and want them cleaned in bulk. Batch endpoints trade latency for throughput and usually have their own limits.

Handling missing and ambiguous matches

Real data is messy. Plan for these cases:

• No match — the code doesn't exist or the address is malformed. Return a clear error rather than a silent empty object.
• Multiple matches — a city name may map to several codes, or a code may span several acceptable city names. Decide whether to pick the primary or return a list.
• Partial match — the street is valid but the ZIP+4 isn't. Fall back to the 5-digit code.
• Stale data — codes are added, retired, and reassigned. Refresh your dataset on a regular schedule.

A practical rule: validate at the point of entry, store the normalized result, and never re-derive it later from raw user input.

Practical use cases

• Checkout address validation — catch typos before shipping, reduce failed deliveries.
• Shipping zone lookup — map a code to a zone, carrier route, or rate table.
• Data enrichment — append county, coordinates, or demographics to existing records.
• Store and service locators — radius search to find nearby branches or coverage areas.
• Territory and tax logic — county and boundary data drive jurisdiction rules.

Licensing and data-source considerations

Postal code data originates with national authorities — USPS in the United States and Canada Post in Canada. Providers license and repackage it, which is why accuracy, update frequency, and field coverage differ between services. Before committing:

• Confirm the data source and how often it refreshes.
• Check whether ZIP+4 and boundary data are included or sold separately.
• Review usage limits and whether batch processing is allowed.
• Read the license terms for redistribution and storage.

Pricing and plan details change, so check the provider's current documentation rather than relying on secondhand figures.

Getting started

1. Decide which request pattern you need (lookup, reverse, radius, or batch).
2. Pick the fields you'll actually store.
3. Write a small test call and inspect the raw response.
4. Add error handling for no-match and ambiguous cases.
5. Cache results where the same codes repeat.

A postal code API is ultimately a translation layer: you give it a code or an address, and it gives back structured location facts. Understand the fields, match them to your use case, and handle the messy edges — that's most of the work.

searchcode replaces the "clone, cat, grep, hope" loop with a single structured call. Per the site's own comparison, understanding a new repo costs roughly 30+ tool calls, ~50,000 tokens, and ~$0.50 when your assistant reads the repo directly — versus 1 call, ~500 tokens, and ~$0.005 through searchcode. That's the same answer at roughly 100× lower cost, with no local clone and no setup. The trade-off is scope: this applies to public git repositories, and the numbers are searchcode's published estimates, not a guarantee for your specific repo or model.

Why the gap is so large

The savings don't come from a smarter model. They come from where the parsing happens.

When an AI reads a repo directly, it has to discover the structure itself: list directories, open files, grep for symbols, follow imports, re-read files it already saw. Every one of those steps pushes raw file content into the context window, and most of those tokens are noise — boilerplate, unrelated modules, formatting.

searchcode flips this. Its six tools return structured, pre-computed data — language breakdowns, complexity metrics, tech stack, findings — so the model spends tokens on reasoning instead of parsing. The site's framing is blunt: "Same answer. 100× cheaper."

The two paths side by side

The cost figures are searchcode's published comparison, so treat them as directional. The mechanism — fewer calls, less raw text — is what actually drives the reduction.

What the structured output contains

The value isn't just fewer tokens; it's that the tokens carry signal. code_analyze returns language breakdowns, complexity metrics, tech stack detection, code quality, and secret scanning (security_summary flags API keys, tokens, and private keys with file, line, and confidence). You can scope it to a subdirectory for large monorepos.

The other tools cover the follow-up questions an assistant would otherwise answer by reading files:

• code_search — full-text search with boolean queries, regex, and structural filters (declarations, usages, strings, comments)
• code_get_file — full files, line ranges, or specific symbols by name, with adjacent declarations for context in one call
• code_get_files — batch retrieve up to 10 files in a single call, for cross-file analysis
• code_file_tree — directory structure with language, path, and depth filters

Because each returns pre-computed structure, the model doesn't re-derive it from raw text.

When this is worth it

The reduction matters most when:

• You're investigating unfamiliar repos repeatedly. A one-off look at a small repo may not justify any tooling; a steady stream of "what is this codebase?" questions does.
• You're on a metered or token-budgeted plan. Cutting ~50,000 tokens to ~500 per investigation changes what fits in a budget.
• You want the assistant to stay inside the editor. Adding the MCP server lets Claude Code, Cursor, Codex, and Copilot use the same six tools without leaving the IDE.

It matters less when you already have the repo cloned and only need one file, or when the repo is private — the site describes the tools as working over public git repositories.

How to try it

Two entry points, both requiring no signup or API key during the beta:

1. Browse without installing. Paste a GitHub URL or owner/repo and get a dossier in seconds. The site shows examples like boyter/scc, fastapi/fastapi, and redis/redis, plus side-by-side comparison (e.g., redis vs memcached).
2. Give it to your AI via MCP. Add the server so your assistant gets the same six tools. For Claude Code:

claude mcp add searchcode --transport http https://api.searchcode.com/v1/mcp

For Cursor, add to .cursor/mcp.json:

{"mcpServers":{"searchcode":{"url":"https://api.searchcode.com/v1/mcp"}}}

For VS Code / Copilot, add to .vscode/settings.json:

{"mcp":{"servers":{"searchcode":{"url":"https://api.searchcode.com/v1/mcp"}}}}

For Claude Desktop:

{"mcpServers":{"searchcode":{"command":"npx","args":["-y","mcp-remote","https://api.searchcode.com/v1/mcp"]}}}

The site also links a full setup guide for OpenCode, Crush, Codex, Gemini CLI, Amp, and LM Studio.

Expected result: after adding the server, your assistant can call code_analyze, code_search, code_get_file, and the other tools against any public repo — returning structured data instead of raw files.

Common sticking points

• The cost figures are estimates. ~$0.50 vs ~$0.005 depends on your model's pricing and how the assistant would otherwise explore. Use them to judge the order of magnitude, not as a bill.
• Public repos only. The tools are described as working over public git repositories; don't assume private-repo access.
• Beta terms can change. The site states it's free during beta with no signup or API key — that's the current state, not a permanent pricing commitment.
• MCP config differs per client. The snippets above match the site's examples; other clients use the linked setup guide rather than the same JSON shape.

searchcode.com is a code intelligence service for working with public Git repositories. It has two entry points: a browser tool where you paste a GitHub URL or owner/repo and get a structured "dossier" in seconds, and an MCP server that plugs the same capabilities into AI coding assistants like Claude Code, Cursor, VS Code/Copilot, and Claude Desktop. Its stated purpose is to let an LLM understand a repository without cloning it or reading files one by one — the site claims roughly 99% token reduction versus manual exploration. It is free during beta, with no signup and no API key required.

What you get from a repository URL

Paste a public repo (for example fastapi/fastapi or redis/redis) and searchcode returns pre-computed, structured data rather than raw source. According to the site, the dossier covers:

• Languages — breakdown of what the repo is written in
• Complexity metrics
• Tech stack detection
• Code quality signals
• Secret scanning — API keys, tokens, and private keys with file, line, and confidence

You can also compare two repositories side by side (the site's example is redis vs memcached). No install is needed for this browser path.

The six MCP tools

When you connect the MCP server, your assistant gets six specialized tools. Each returns structured, pre-computed data so the model spends tokens on reasoning instead of parsing. The named tools include:

The site lists three more beyond the four named above, describing the set as "six specialized tools."

Why the token math matters

The site frames the value as a comparison between two ways of investigating an unfamiliar repo:

• Without searchcode: ~30+ tool calls, ~50,000 tokens, ~$0.50 per investigation — described as "clone, cat, grep, hope."
• With searchcode: 1 call, ~500 tokens, ~$0.005 per investigation, returning a structured response with languages, complexity, tech stack, and findings.

Same answer, roughly 100× cheaper by the site's own estimate. Treat those figures as vendor claims rather than measured benchmarks, but the underlying mechanism is real: pre-computing analysis server-side means the model never has to pull whole files into context just to learn what a repo contains.

How to connect it

Setup differs by client. The site provides copy-paste configs:

Claude Code (command line):

claude mcp add searchcode --transport http https://api.searchcode.com/v1/mcp

Cursor — add to .cursor/mcp.json:

{"mcpServers":{"searchcode":{"url":"https://api.searchcode.com/v1/mcp"}}}

VS Code / Copilot — add to .vscode/settings.json:

{"mcp":{"servers":{"searchcode":{"url":"https://api.searchcode.com/v1/mcp"}}}}

Claude Desktop:

{"mcpServers":{"searchcode":{"command":"npx","args":["-y","mcp-remote","https://api.searchcode.com/v1/mcp"]}}}

The site also points to a full setup guide for OpenCode, Crush, Codex, Gemini CLI, Amp, and LM Studio.

Who it's for, and when to skip it

It fits developers using an AI coding assistant who need to orient in an unfamiliar public repository — onboarding to a dependency, evaluating a library before adopting it, or answering "what is this codebase and is it healthy?" without a clone. The secret-scanning angle is useful for a quick credential-hygiene check on a repo you're considering.

It is less useful if you already have the repo cloned and indexed locally, if you need private-repository support (the site describes public repos), or if you want to edit code rather than understand it. Since it is free during beta with no signup, the cost of trying the browser dossier on one repo is essentially zero — that's the fastest way to judge whether the output matches how you actually work.

Add the searchcode MCP server by pointing your client at the HTTP endpoint https://api.searchcode.com/v1/mcp. Claude Code takes a one-line CLI command; Cursor and VS Code/Copilot take a small JSON block in a config file; Claude Desktop needs an npx bridge. The server is free during beta with no signup and no API key, so the only thing you supply is the URL. Once connected, your assistant gets six tools (code_analyze, code_search, code_get_file, and three more) that work over any public git repository.

What you need before starting

• A client that supports MCP servers: Claude Code, Cursor, VS Code/Copilot, Claude Desktop, or another listed client.
• The endpoint URL: https://api.searchcode.com/v1/mcp.
• No account, API key, or payment step — the beta is open and keyless.
• A public repo you want to inspect, in owner/repo form (e.g. fastapi/fastapi).

Claude Code

Run this from your terminal:

claude mcp add searchcode --transport http https://api.searchcode.com/v1/mcp

The --transport http flag tells Claude Code this is a remote HTTP server rather than a local process, and the trailing URL is the endpoint. After it registers, ask Claude Code something like "analyze fastapi/fastapi" and it should call code_analyze instead of cloning and grepping.

Cursor

Create or edit .cursor/mcp.json in your project:

{"mcpServers":{"searchcode":{"url":"https://api.searchcode.com/v1/mcp"}}}

The key name (searchcode) is just a label — it's how the server shows up in your client. The url field is what actually connects it. Restart or reload Cursor so it picks up the new server.

VS Code / Copilot

Edit .vscode/settings.json:

{"mcp":{"servers":{"searchcode":{"url":"https://api.searchcode.com/v1/mcp"}}}}

Note the nesting differs from Cursor: VS Code wraps servers under an mcp object with a servers key, while Cursor uses mcpServers at the top level. Copying one config into the other client won't work.

Claude Desktop

Claude Desktop can't hit the HTTP endpoint directly, so it runs a local bridge through npx:

{"mcpServers":{"searchcode":{"command":"npx","args":["-y","mcp-remote","https://api.searchcode.com/v1/mcp"]}}}

Here command and args describe a local process that mcp-remote uses to reach the remote server. The -y flag auto-confirms the package install so it doesn't stall on a prompt.

Other clients

For OpenCode, Crush, Codex, Gemini CLI, Amp, or LM Studio, the site points to a full setup guide rather than inline snippets. The pattern is the same across all of them — register a server named searchcode with the URL https://api.searchcode.com/v1/mcp — but the config file location and key names vary by client, so check that client's own MCP documentation for where the block goes.

Verifying it works

Ask your assistant to analyze a known public repo, for example redis/redis. A working setup returns a structured dossier: language breakdown, complexity metrics, detected tech stack, code quality signals, and findings. If the assistant instead starts cloning, running cat, or grepping, the server isn't connected — recheck the URL and that you reloaded the client.

Common snags

• Wrong config shape. Cursor uses mcpServers; VS Code uses mcp.servers. Mixing them silently fails.
• No reload. Most clients only read MCP config at startup, so restart after editing.
• Expecting a local install. This is a remote HTTP server — there's nothing to clone or run yourself except the npx bridge for Claude Desktop.
• Private repos. The tools operate on public repositories; don't expect access to private ones.

Why the setup is worth it

Without the server, understanding a new repo can take roughly 30+ tool calls and ~50,000 tokens (about $0.50 per investigation) as the model clones, cats, and greps. With it, the same question resolves in about 1 call and ~500 tokens (about $0.005) because the server returns pre-computed, structured data. Same answer, far fewer tokens — which is the whole point of wiring it in.

searchcode exposes six specialized tools through an MCP server, and each returns structured, pre-computed data rather than raw file contents. The point is that an LLM spends tokens on reasoning instead of parsing: the site claims roughly 1 call and ~500 tokens per investigation versus ~30+ tool calls and ~50,000 tokens when an assistant clones, greps, and reads a repo itself. You can use the tools either by pasting a public GitHub URL into the web dossier, or by adding the MCP server to an editor so Claude Code, Cursor, Codex, or Copilot calls them directly. The service is described as free during beta, with no signup and no API key required.

The six tools and what each returns

The page names code_analyze, code_search, code_get_file, and "three more," and explicitly lists code_file_tree and code_get_files in the capabilities section, which accounts for five of the six.

code_analyze

This is the entry point for understanding a repository you have never seen. It returns language breakdowns, complexity metrics, tech stack detection, and code quality signals in one structured response.

It also includes security_summary, which the page describes as the credential scanner: it reports API keys, tokens, and private keys with file, line, and confidence. For large monorepos you can scope the analysis to a subdirectory rather than the whole tree.

code_search

Full-text search across the code, with three layers of query control:

• Boolean queries for combining terms
• Regex for pattern matching
• Structural filters for declarations, usages, strings, and comments

That last layer is what separates it from plain grep: you can ask for a symbol's declaration sites specifically, or only its usages, or restrict matches to strings or comments.

code_get_file and code_get_files

code_get_file retrieves a full file, a line range, or a specific symbol by name. It can also include adjacent declarations so you get surrounding context in a single call instead of a follow-up round trip.

code_get_files does the same in batch, up to 10 files per call. This is the tool for cross-file analysis — for example, tracing how a type defined in one module is consumed across several others.

code_file_tree

Returns the directory structure with filters for language, path, and depth. Useful as a first call on a large repo to decide which subdirectories are worth analyzing or searching.

Connecting the tools to your editor

The MCP endpoint is https://api.searchcode.com/v1/mcp. Setup differs by client:

Claude Code — one command:

claude mcp add searchcode --transport http https://api.searchcode.com/v1/mcp

Cursor — add to .cursor/mcp.json:

{"mcpServers":{"searchcode":{"url":"https://api.searchcode.com/v1/mcp"}}}

VS Code / Copilot — add to .vscode/settings.json:

{"mcp":{"servers":{"searchcode":{"url":"https://api.searchcode.com/v1/mcp"}}}}

Claude Desktop — uses a local bridge:

{"mcpServers":{"searchcode":{"command":"npx","args":["-y","mcp-remote","https://api.searchcode.com/v1/mcp"]}}}

For OpenCode, Crush, Codex, Gemini CLI, Amp, or LM Studio, the site points to a full setup guide. Once connected, your assistant gets the same six tools over any public repo.

Choosing between the web dossier and the MCP server

If you just want to look at a repo yourself, paste a GitHub URL or owner/repo into searchcode.com and you get a dossier in seconds with no install. The page also supports side-by-side comparison of two repos (its example is redis vs memcached).

If you want your AI assistant to do the investigation inside your editor — and to avoid the token cost of it cloning and grepping — add the MCP server. The two paths use the same underlying tools; the difference is who is reading the output.