# Gcf > The AI-native wire format for structured data. 100% comprehension on every frontier model. 50-92% fewer tokens than JSON. 43B+ lossless round-trips across 17 formats. Spec v3.2 Stable. - **Type:** MCP server - **Install:** `agentstack add mcp-blackwell-systems-gcf` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [blackwell-systems](https://agentstack.voostack.com/s/blackwell-systems) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [blackwell-systems](https://github.com/blackwell-systems) - **Source:** https://github.com/blackwell-systems/gcf - **Website:** http://www.gcformat.com/ ## Install ```sh agentstack add mcp-blackwell-systems-gcf ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About The AI-native wire format for structured data. > [!IMPORTANT] > **GCF: A Token-Optimized Wire Format for Structured LLM Interactions** > Dayna Blackwell, 2026. DOI: [10.5281/zenodo.20579817](https://doi.org/10.5281/zenodo.20579817) > > **Merge Barriers in BPE Tokenization: From Vocabulary Merges to Attention Collapse** > Dayna Blackwell, 2026. DOI: [10.5281/zenodo.20925910](https://doi.org/10.5281/zenodo.20925910) --- **100% comprehension on every frontier model. 50-92% fewer tokens than JSON. 91.2% on structurally complex code graphs (vs TOON 68.2%, JSON 53.4%). Proven lossless: `decode(encode(value)) == value` for every structured value, verified across 43,000,000,000+ round-trips in 5 formats and 17 serialization formats. Zero training required. JSON's grammar symbols are [hardcoded as merged vocabulary entries](https://gcformat.com/guide/tokenizer-analysis) in BPE tokenizers, creating irrecoverable structural boundaries. GCF's pipe delimiter has 0% merge rate with field names.** Encode any structured data as GCF before sending it to an LLM. JSON, YAML, TOML, CSV, MessagePack: GCF encodes them all. The model reads it natively with zero format instructions. `decode()` converts back to any format when a human needs to see it. Your existing schemas and validators work on the decoded output unchanged. ```bash pip install gcf-python # Python npm install @blackwell-systems/gcf # TypeScript go get github.com/blackwell-systems/gcf-go # Go cargo add gcf # Rust ``` Or wrap any existing MCP server with zero code changes: ```bash pip install gcf-proxy ``` --- ## Benchmarks 2,400+ LLM evaluations across 11 models, 3 providers, and 50+ independent test runs. | | Generic Profile (500 orders) | Graph Profile (500 symbols) | |---|---|---| | **GCF** | **100%** on every frontier model | **91.2%** (10 models) | | **TOON** | weakest format consistently | 68.2% | | **JSON** | GCF avg >= JSON on every model | 53.4% | | | GCF | TOON | JSON | |---|---|---|---| | **Token efficiency** (16 datasets) | **wins 15/16** | wins 1/16 | wins 0/16 | | **Generation** (28 runs, 11 models) | **5/5** | 1.0/5 | 5.0/5 | | **Token savings** | **50-92%** vs JSON | 30-60% vs JSON | baseline | | **43,000,000,000+ round-trips** | **0 failures** | | | Full results: [gcformat.com/guide/benchmarks](https://gcformat.com/guide/benchmarks.html) --- ### Encode any structured data (generic profile) ```python from gcf import encode_generic output = encode_generic({ "employees": [ {"id": 1, "name": "Alice", "department": "Engineering", "salary": 95000}, {"id": 2, "name": "Bob", "department": "Sales", "salary": 72000}, {"id": 3, "name": "Carol", "department": "Marketing", "salary": 85000}, ], }) ``` ``` GCF profile=generic ## employees [3]{id,name,department,salary} 1|Alice|Engineering|95000 2|Bob|Sales|72000 3|Carol|Marketing|85000 ``` One header declares field names. Rows are positional values only. No field names repeated per record. Lossless: `decode(encode(value)) == value` for every structured value, proven across 43,000,000,000+ random round-trips in 5 formats and 6 languages. ### Graph profile (code intelligence, knowledge graphs, MCP tools) For data with nodes, edges, and distance groups: ```python from gcf import encode, Payload, Symbol, Edge output = encode(Payload( tool="context_for_task", token_budget=5000, tokens_used=1847, symbols=[ Symbol(qualified_name="pkg.Auth", kind="function", score=0.78, provenance="lsp", distance=0), Symbol(qualified_name="pkg.Server", kind="function", score=0.54, provenance="lsp", distance=1), ], edges=[Edge(source="pkg.Server", target="pkg.Auth", edge_type="calls")], )) ``` ``` GCF profile=graph tool=context_for_task budget=5000 tokens=1847 symbols=2 edges=1 ## targets @0 fn pkg.Auth 0.78 lsp ## related @1 fn pkg.Server 0.54 lsp ## edges [1] @0` path columns: `"customer>name"` becomes a column, values go directly in the row. 20-48% fewer tokens on deeply nested API data. Variable-length arrays and irregular shapes use `^` attachment fallback. 3. **Primitive arrays.** Inlined: `tags[2]: admin,user`. Strings containing commas are quoted. 4. **Scalars.** `key=value` at the top level. Strings that collide with typed literals (`"true"`, `"123"`, `"-"`) are quoted automatically. 5. **Root values.** Objects, arrays, and scalars at the document root. Every JSON value has a GCF representation. ### Graph profile 1. **Positional fields.** One header declares field names. Rows are values only. 2. **Local IDs.** `@0`, `@1`. Edges reference by ID, not by repeating full identifiers. 3. **Hierarchical grouping.** Section headers (`## targets`, `## related`) replace per-record metadata. Both profiles share the same grammar (common scalar grammar, key grammar, header format). The savings are structural and grow with payload size. ## It gets cheaper over time **Session deduplication:** Symbols sent in prior responses become bare references (`@7` = 2 tokens vs 19 for full declaration). Over a 5-call session at production scale (500 symbols): 84.3% cumulative savings vs JSON (148K JSON tokens vs 23K GCF). By call 5: 91.9% savings. **Delta encoding:** When the context changes slightly between queries, send only the diff. 81.2% additional savings on re-queries. No other format has these. They compound across multi-turn agent interactions. ## Implementations | Language | Package | Repository | |----------|---------|-----------| | Go | `go get github.com/blackwell-systems/gcf-go` | [gcf-go](https://github.com/blackwell-systems/gcf-go) | | TypeScript | `npm install @blackwell-systems/gcf` | [gcf-typescript](https://github.com/blackwell-systems/gcf-typescript) | | Python | `pip install gcf-python` | [gcf-python](https://github.com/blackwell-systems/gcf-python) | | Rust | `cargo add gcf` | [gcf-rust](https://github.com/blackwell-systems/gcf-rust) | | Swift | Swift Package Manager | [gcf-swift](https://github.com/blackwell-systems/gcf-swift) | | Kotlin | JitPack | [gcf-kotlin](https://github.com/blackwell-systems/gcf-kotlin) | | MCP Proxy | `pip install gcf-proxy` | [gcf-proxy](https://github.com/blackwell-systems/gcf-proxy) (bidirectional, session dedup, HTTP frontend) | | Claude Code Plugin | `/plugin install` | [gcf-claude-plugin](https://github.com/blackwell-systems/gcf-claude-plugin) (one-command install, session stats hook) | | Codex Plugin | `codex plugin add` | [gcf-codex-plugin](https://github.com/blackwell-systems/gcf-codex-plugin) (one-command install, session stats hook) | | VS Code | `ext install blackwell-systems.gcf-vscode` | [gcf-vscode](https://marketplace.visualstudio.com/items?itemName=blackwell-systems.gcf-vscode) (syntax highlighting) | | n8n | `npm install n8n-nodes-gcf` | [gcf-n8n-nodes](https://github.com/blackwell-systems/gcf-n8n-nodes) (workflow encode/decode) | | JetBrains | Search "GCF" in Plugins | [gcf-jetbrains](https://github.com/blackwell-systems/gcf-jetbrains) (IntelliJ, PyCharm, WebStorm, GoLand) | | Zed | Search "GCF" in Extensions | [gcf-zed](https://github.com/blackwell-systems/gcf-zed) (tree-sitter syntax highlighting) | | Tree-sitter | `npm install tree-sitter-gcf` | [tree-sitter-gcf](https://github.com/blackwell-systems/tree-sitter-gcf) | **Zero runtime dependencies. Permanently.** All six implementations depend only on their language's standard library. No transitive dependencies. No supply chain risk. This is a permanent commitment: GCF will never take on external runtime dependencies. MIT licensed. All implementations support both generic profile (`encodeGeneric`) and graph profile (`encode`). CLI included in all 6 languages. Syntax highlighting via tree-sitter (Neovim, Helix, Zed). **Specification:** [SPEC v3.2 Stable](SPEC.md) with 173 conformance fixtures, 43,000,000,000+ lossless round-trips verified across 5 formats and 6 languages. All implementations at v2.2.0+ (Go v1.3.0). Cross-language 6x6 matrix verified. ## Documentation **[gcformat.com](https://gcformat.com/)** - [Getting Started](https://gcformat.com/guide/getting-started.html) - [Benchmarks](https://gcformat.com/guide/benchmarks.html) - [Benchmarks (Full Data)](https://gcformat.com/guide/eval-results.html) - [GCF vs TOON](https://gcformat.com/guide/vs-toon.html) - [Schema Validation](https://gcformat.com/guide/schema-validation.html) - [FAQ](https://gcformat.com/guide/faq.html) - [Tokenizer Analysis](https://gcformat.com/guide/tokenizer-analysis.html) (why JSON's grammar breaks at the BPE level) - [Independent AI Reviews](https://gcformat.com/reviews/) - [Playground](https://gcformat.com/playground.html) - [Specification](SPEC.md) ## Adopted by [Speakeasy](https://speakeasy.com) (API tooling, customers include Google, Verizon, Mistral AI, DocuSign, Vercel) · [OmniRoute](https://omniroute.online) (6.1K stars) · [NetClaw](https://github.com/automateyournetwork/netclaw) (556 stars) · [ctx](https://github.com/stevesolun/ctx) (510 stars) · [NeuroNest](https://neuronest.cc) · [Open Data Products SDK](https://opendataproducts.org/sdk/) (Linux Foundation) · [Raycast](https://raycast.com/blackwell-systems/json-to-gcf-converter) · [and more](https://gcformat.com/ecosystem/adopters.html) ## Use cases - **MCP tool responses.** Any MCP server returning structured data. 50-92% fewer tokens with 100% comprehension accuracy. - **Agent-to-agent communication.** 63% fewer tokens per handoff. 5/5 generation validity on every frontier model. - **LLM structured output.** LLMs produce valid GCF with a 3-line primer. No training required. - **Code intelligence.** Graph profile with local IDs, edges, and distance grouping. - **Multi-format interop.** Validated lossless across 17 serialization formats (JSON, XML, MessagePack, YAML, BSON, TOML, CBOR, Protobuf, CSV, JSON5, Avro, Arrow, Parquet, Pickle, INI, NDJSON, Plist). More links - [betterthanjson.com](https://betterthanjson.com) - [jsonalternative.com](https://jsonalternative.com) - [betterthantoon.com](https://betterthantoon.com) ## License MIT - [Dayna Blackwell](https://github.com/blackwell-systems) ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [blackwell-systems](https://github.com/blackwell-systems) - **Source:** [blackwell-systems/gcf](https://github.com/blackwell-systems/gcf) - **License:** MIT - **Homepage:** http://www.gcformat.com/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-blackwell-systems-gcf - Seller: https://agentstack.voostack.com/s/blackwell-systems - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.