codeitall
Provides deterministic tools for querying Rust package API behavior, including signature search, behavior lookup, and cross-implementation comparisons.
README
codeitall — Rust API behavioral oracle
Version-pinned behavioral oracle for Rust package APIs, built for AI coding
agents. Five deterministic tools backed by a Postgres substrate of
66,549,370 pattern_records and 107 successful probe runs across
7 task families (datetime, duration, base64, JSON strict-vs-permissive,
JWT verify, AEAD encrypt/decrypt, constant-time compare).
Hosted, free, no signup: https://api.codeitall.dev
This repository is codeitall's public surface — the API reference, OpenAPI spec, the "For AI Agents" page, and the per-family pages. The ingest/indexer and server source are not redistributed; you consume codeitall by querying the hosted API, not by running it yourself.
What it answers
The differentiator from a docs RAG: the substrate captures runtime behavior (what a function actually does on a canonical input set, observed empirically), not just documentation (what it claims to do). It targets the documentation-vs-runtime gap — yank status, RustSec advisories, deprecation renames, and cross-implementation behavior tables.
Tools
| Tool | Use it for |
|---|---|
answer_api_question |
Open-ended Rust API questions; routes deterministically (rule-based, no LLM) and falls back to bge-m3 cosine retrieval. |
signature_search |
You know the signature shape (fn(&str) -> Result<Vec<u8>, _>); get ranked candidate functions + crate, downloads, yank status. |
behavior_lookup |
A specific (crate, fn_name); get the probe-observation table verbatim — each row (input, outcome=ok|err|panic, value_or_error). |
compare_implementations |
A task category (e.g. "base64 crates"); get the side-by-side cross-implementation behavior table. |
find_modern_equivalent |
A suspected-deprecated call; get the current canonical alternative + yank/advisory rows. |
Determinism contract
No generative LLM sits between a request and its response. Every response is SQL + deterministic templating; the only model in the hot path is bge-m3, a deterministic text→vector embedder used for the cosine fallback (same input → same vector → same hits). This is a regression-pinned invariant.
Quickstart
# Mint a free anonymous key (no email, no signup):
curl -sX POST https://api.codeitall.dev/v1/keys \
-H 'content-type: application/json' -d '{}'
# Ask a question (HTTP REST):
curl -sX POST https://api.codeitall.dev/v1/answer_api_question \
-H 'content-type: application/json' \
-d '{"question":"how do I parse a duration string in rust"}'
GET aliases exist for every tool (browse-only clients); signature_search
also accepts a shape_b64 (base64url) parameter so URL re-encoding can't
mangle type-signature characters.
Access modes
- MCP — SSE/HTTP at
https://api.codeitall.dev/mcp(Bearer key), or stdio for self-hosters. - HTTP REST —
POST/GET /v1/<tool>. - OpenAPI 3.1 —
openapi.json(live:https://api.codeitall.dev/openapi.json).
Docs in this repo
for-ai-agents.html— positioning, tool list, key snippet (live:https://api.codeitall.dev/for-ai-agents.html).api-reference.html/api-reference.md— generated tool reference + scope/coverage.agents/— one page per probe family.
Cost & limits
Free. Per-IP key issuance cap (10/day) and per-key quota (10,000/day). No paid tier, no email capture, no signup.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
Neon Database
MCP server for interacting with Neon Management API and databases
E2B
Using MCP to run code via e2b.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.