# Charter

> Updated 2026-09-28 · type: tool · category: mcp-agents · status: active · rev 1

Charter declares agent API tools as Pydantic schemas, auto-handling request construction, auth, wire format, and response trimming instead of hand-code.

- Open source: yes (Apache-2.0)
- Self-hostable: yes
- Pricing model: free
- Best for: A Python engineer wiring an agent to real APIs who would rather declare each tool once as a Pydantic schema — letting the library build the request, handle auth and wire format, and trim the response — than hand-implement a stack of bespoke tool functions.
- Not for: Non-Python stacks, or teams that need a mature, widely-adopted framework rather than a day-one library at 1 star.
- Last verified: 2026-09-28

- **Canonical:** https://gtmstacker.com/registry/tool/charter/
- **Source:** [r28ai · GitHub](https://github.com/r28ai/charter)
- **Tags:** mcp-agents, agent-tooling, tool-definition, pydantic, python, self-hostable
- **Repository:** https://github.com/r28ai/charter

## Is Charter open source?

Yes, Charter is open source under the Apache-2.0 license.

## How much does Charter cost?

Charter is free to use.

## Can I self-host Charter?

Yes, Charter can be self-hosted (the source is available under the Apache-2.0 license).

## Alternatives & related

- [Composio](https://gtmstacker.com/registry/tool/composio/)
- [MetaMCP](https://gtmstacker.com/registry/tool/metamcp/)
- [Klavis AI](https://gtmstacker.com/registry/tool/klavis-ai/)


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Charter is a Python library that lets you declare agent API tools as Pydantic schemas instead of hand-implementing them, auto-handling request construction, auth, wire format, and response trimming. Open source: yes (Apache-2.0); self-hostable; pricing free. It runs in-process with no telemetry, launched 2026-09-28, and has 1 star.

## What it does

Charter replaces the usual pile of hand-written tool functions with a declaration. You describe each API tool once as a Pydantic schema, and Charter does the plumbing: it constructs the request, handles authentication, deals with the wire format, and trims the response down before it reaches the model — so the agent both calls the API correctly and does not drown in raw payload. It is a plain Python library that runs in-process, with no telemetry phoning home. Open source: yes (Apache-2.0); self-hostable; pricing free.

## Provenance

- Apache-2.0 per repo; 1 star; launched 2026-09-28; Python library, in-process, no telemetry; tools declared as Pydantic schemas with auto request construction, auth, wire format and response trimming (github.com/r28ai/charter, verified 2026-09-28).
- Maker-reported over 534 runs: malformed GraphQL calls dropped 32->0, unauthorized endpoint calls 10->0, and forwarded bytes fell to ~1/4 of raw HTTP tools (7,382 vs 36,383 bytes/run). Stated here as the maker's own numbers (vendor-claim), not independently reproduced.
- Curated from the GTM Stacker signal registry (2026-09-28 pass); license/facts independently verified 2026-09-28.

## Why it matters for a GTM stack

A GTM agent's usefulness is mostly its tools — CRM writes, enrichment lookups, GraphQL queries — and each hand-built tool is code to get right and bytes of response to feed the model on every call. Charter attacks both: declare the tool as a schema and let the library enforce correct, authorized requests and trim the response, which directly cuts malformed calls and token spend. Open source: yes (Apache-2.0); self-hostable; pricing free. The honest read: the declarative-tool idea is sound and the maker's 534-run numbers (fewer malformed/unauthorized calls, ~4x fewer forwarded bytes) point the right way, but this is a Python-only, day-one project at 1 star — trial it on one integration and confirm the lift before standardizing your tool layer on it.