Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Beschreibung vom Entwickler (englisches Original)
Content-addressed semantics: fail-closed handshakes over 617 cognitive patterns.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"semahash": {
"command": "uvx",
"args": [
"semahash",
"mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add semahash -- uvx semahash mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"semahash": {
"command": "uvx",
"args": [
"semahash",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"semahash": {
"type": "stdio",
"command": "uvx",
"args": [
"semahash",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto emergent-wisdom bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.10.0) kam vor 26 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv) auf deinem Computer.
In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, Zendesk
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Zendesk MCP Pack — tickets, users, organizations via OAuth.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
Content-addressed semantics for multi-agent coordination.
Sema is a content-addressed reference system for reasoning and communication. Participants encode and hash information under an agreed representation, then reuse the content address as a verifiable reference, optionally paired with a human-readable handle. Matching full references establishes identity of the resolved hashed content; semantic equivalence, correctness, and enforcement remain separate questions.
Web: semahash.org · Discord: Join
Add to any MCP client (Claude Code, Cursor, VS Code, Windsurf, Claude Desktop):
{
"mcpServers": { "sema": { "command": "uvx", "args": ["--from", "semahash[mcp]", "sema", "mcp"] } } }
Or via Claude Code CLI:
claude mcp add sema -- uvx --from "semahash[mcp]" sema mcp
This uses uv to download, install, and run sema in an isolated environment on first invocation, then caches it for subsequent calls.
Sema also ships as a Claude Code plugin — MCP server plus a skill that teaches the agent the search/resolve/mint/handshake workflow:
# One-time: add the Emergent Wisdom marketplace
claude plugin marketplace add emergent-wisdom/marketplace
claude plugin install sema
This gives you the MCP server and the sema-usage skill (auto-loaded), which teaches when to search vs mint, how to embed handles in text, and how to verify meaning at boundaries. The skill is a Claude Code convenience — the MCP server works with any client.
For local development:
claude --plugin-dir /path/to/sema
pip install "semahash[mcp]"
For CLI-only use (no MCP server):
pip install semahash
Already covered above via the JSON config or pip install path. For development against this repo:
git clone https://github.com/emergent-wisdom/sema.git
pip install -e "./sema[mcp]"
Your agent now has access to sema_search, sema_lookup, sema_handshake, and 9 more tools. Any MCP-compatible client works — Sema exposes a standard stdio server.
Verify it works — ask your agent: *"Search sema for coordination patterns and handshake on StateLock"*
Sema exposes a standard MCP stdio server — any MCP-compatible client works, including OpenClaw (openclaw mcp set sema '{"command":"uvx","args":["--from","semahash[mcp]","sema","mcp"]}').
# Search the vocabulary
sema search "coordination"
sema resolve StateLock
sema show StateLock
sema skeleton
sema serve --open
Build a private registry from scratch — no PR or maintainer in the loop:
sema init ./mylib.db
sema use ./mylib.db sema apply --add path/to/MyPattern.json sema search "..."
Subsequent sema commands (including sema mcp) read from your private registry. (SEMA_DB_PATH, if set, overrides sema use.) See CONTRIBUTING.md for the canonical contribution path and docs/specification/versioning.md for the refinement and supersession policy.
Package a project database as a verified, standalone library release:
sema package ./mylib.db \
--name mylib \ --version 1.0.0 \ --output-dir dist/mylib-1.0.0 \ --github-repo acme/sema-mylib
Publish the generated library.json and versioned ZIP as assets on the corresponding published GitHub Release. Consumers install the Release asset URL for library.json—not the repository URL or a branch:
sema install https://github.com/acme/sema-mylib/releases/latest/download/library.json
sema use mylib sema list sema root
It installs one verified snapshot at a time rather than merging vocabularies; the bundled vocabulary remains the offline default. Use sema update mylib to follow the installed library's recorded release pointer. See Publishing and Installing Vocabulary Libraries for the complete DeFi authoring, dependency-closure, packaging, GitHub Release, and update workflow.
Libraries published on semahash.org can be found and installed by name, without an account:
sema registry search defi
sema install mylib
To list a release there so other people and agents can find it, log in once and publish from the command line:
sema login # approve the short code in your browser, once
sema registry import https://github.com/acme/sema-mylib/releases/latest/download/library.json
An agent can log in with a key that you create on your semahash.org profile page instead: it pipes the key to sema login --key - or calls the sema_library_login MCP tool.
The login follows the OAuth 2.0 device authorization flow, so it also works from an agent or a machine without a browser. To use another deployment, run sema login --registry https://registry.example. A successful login remembers that registry for subsequent commands. --registry and SEMA_REGISTRY_URL override the remembered choice; https://semahash.org is the first-use default.
from sema.core.registry import RegistryManager
registry = RegistryManager() pattern = registry.get_pattern("StateLock")
print(pattern["sema_ref"]) # StateLock#c9c2
assert pattern["sema_ref"] == "StateLock#c9c2"
python experiments/demos/local_handshake.py
See the handshake in action: matching hashes PROCEED, mismatched hashes HALT, and unknown patterns HALT. Cooperative mode accepts short prefixes for drift detection; strict mode requires the full hash. Takes 2 seconds.
word = hash(canonical(definition))
Take any concept (a coordination protocol, a reasoning pattern, a trust mechanism), express it in canonical form, hash it. That hash IS the word. Change one byte in the definition, get a different word.
Cooperative: sema_handshake("StateLock#c9c2")
-> PROCEED with assurance="prefix", or HALT
Strict: sema_handshake("StateLock", " ", strict=true) -> PROCEED with assurance="full_hash", or HALT
This is the Anti-Postel principle: strict mode proceeds only on full-hash identity; cooperative mode uses compact prefixes as a non-adversarial drift signal. Mismatches fail closed in both modes.
The bundled vocabulary spans 4 layers:
Each pattern is a content-addressed behavioral definition. Concrete cards may add machine-verifiable contracts, invariants, failure modes, parameters, and typed dependencies where those fields are identity-defining.
When running as an MCP server (sema mcp), these tools are available:
| Tool | Description |
|---|---|
sema_search | Search patterns by name, description, or meaning |
sema_lookup | Get a pattern by its reference (e.g., StateLock#c9c2) |
sema_resolve | Get a pattern with dependencies expanded |
sema_handshake | Fail-closed semantic verification between agents |
sema_mint | Create a new pattern (validate, hash, add to vocabulary) |
sema_propose_context | Compute a context digest for a multi-agent definition set (drift detection) |
sema_verify_context | Verify a context proposal from another agent |
sema_tree | Browse vocabulary by layer and category |
sema_validate | Validate a pattern JSON for correctness |
sema_stats | Vocabulary statistics |
sema_graph_skeleton | Ultra-minimal graph overview (~150 tokens) |
sema_reset_session | Clear session cache so searches return full results again |
sema_library_search | Find libraries published on semahash.org |
sema_library_install | Verify and install a library by name or library.json URL |
sema_library_login | Store a semahash.org key that the user gives the agent |
sema_library_publish | Publish a GitHub release on semahash.org, after the user agrees |
pip install "semahash[api]"
sema serve --open
The local view shows the vocabulary selected with sema use, the same one a connected agent writes to. It follows later switches, and its 3D graph grows while the agent mints: new patterns light up and appear in a live list. The pattern browser has card, list, and JSON views. Development stays on your computer; semahash.org hosts finished libraries, and a writable vocabulary's page lists the optional steps to publish one there.
The experiments/ directory contains reproducible evaluations of Sema's claims and implementation boundaries.
The public v0.3.0 to v0.4.0 reconstruction experiment checks whether the current graph and hash-cascade algorithm can rebuild a target vocabulary from an earlier release. It compares both aggregate roots, semantic pattern content, the logical dependency graph, unhashed metadata, and the complete normalized read model rather than treating root equality as complete release equality. It also demonstrates the safe reconstruction path: integrate the delta into a complete staged card snapshot, compile a fresh database, verify the result, and only then activate it.
.venv/bin/python experiments/delta_reconstruction/reproduce.py
The experiment and its expected results are documented in experiments/delta_reconstruction/README.md. Small synthetic versions of its addition, removal, rename, cascade, metadata, and failure cases run in the regular test suite.
The controlled multi-agent design challenge compares three conditions:
| Condition | Sema | Turns | Outcome |
|---|---|---|---|
| A: Natural language only | No | 4 | Design rejected |
| B: Sema vocabulary | Yes | 11 | SAD Engine approved |
| C: Sema + protocol | Yes | 25 | SAD Engine with exhaustive vetting |
Agents with Sema patterns produced physics-grounded designs that survived adversarial scrutiny. Agents without Sema produced shallow designs that failed safety review.
To reproduce:
`bas
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