mcp.film directory

mcp-film

The mcp.film directory as an MCP server. Give your agent a map of every MCP server worth knowing in AI filmmaking — searchable by capability, with copy-paste install configs and a full-pipeline planner.

claude mcp add mcp-film -- npx -y mcp-film

Or connect the hosted endpoint — same tools, no local install:

claude mcp add --transport http mcp-film https://mcp.film/mcp

Speaks MCP 2025-11-25, 2025-06-18 and 2025-03-26 (an unknown offered version gets the latest). initialize returns short instructions: start with recommend_film_mcps(brief).

Tools

Tool	What it does
recommend_film_mcps	Match a free-text brief to the best routes and first servers to connect. Hard constraints first (constraints object or phrases in the brief), then relevance; confidence says how well the brief matched
search_film_mcps	Tokenised, alias-aware search (lipsync = lip-sync = lip sync; tts = text to speech; 4k = upscale); filter by category, capability, official, remote; paged
get_film_mcp	Full entry for one server: install, auth, tools, caveats, status
list_film_categories	All categories with pipeline stages and agent hints
list_film_capabilities	Capability tags (aliases folded in) ranked by server count; min_count 2 by default; paged
get_film_capability	Ranked server cluster for one tag; aliases resolve (lipsync → lip-sync, says resolved_from); paged
get_install_config	Copy-paste config or hosted remote URL for a client from list_client_profiles; a bad client returns valid_clients
list_client_profiles	Supported MCP client setup profiles and install-shape hints
get_martini_setup	Executable runbook for connecting a user to Martini (the sponsor): resumable state model, human steps, agent steps per client / shell / browser, quote-first generation contract, structured cost, when not to
get_catalog_pulse	Operating pulse: freshness, verification queue, curator agenda, machine surfaces. demand_signals and martini_growth_checks are curator/ops sections served only by name
list_film_playbooks	Curated production stack recipes for common AI filmmaking jobs; paged
get_film_playbook	Full playbook with primary servers, setup order, auth requirements, steps, constraints, fallbacks
list_film_recommendations	Intent-routed MCP shortlists for common filmmaking jobs; paged
get_film_recommendation	Full recommendation with roles, reasons, fallbacks, and Martini handoff guidance
plan_film_stack	Recommended servers for every pipeline stage, biased by your brief (capabilities, taglines, descriptions)
submit_listing	Propose a new server: validated, deduped against the live registry, returned as a ready-to-file GitHub issue payload (a mcp.film maintainer verifies against primary sources before listing)

Response shapes

Every result is compact JSON in content[0].text and the same object in structuredContent.

Lists (search_film_mcps, get_film_capability, list_film_capabilities, list_film_playbooks, list_film_recommendations) carry count, total, limit, offset, next_offset and truncated. Nothing is cut silently: pass offset: next_offset for the next page. Default limit is 20 (50 for capabilities), maximum 100.

Compact server (search results, capability clusters, stack picks): slug, url, verified, caveat, name, vendor, official, category, tagline, pricing, remote, capabilities plus status when the listing carries one. caveat is the entry's caveats[0], else the first sentence of its notes that is not a verification log line. get_film_mcp is always the full entry.

Recommendation pick (recommend_film_mcps): primary_servers[] and the alias primary[] hold {slug, name, role, why, remote, official, verified, status?, caveats?}; fallback_servers[] hold {slug, name, remote, official, verified, status?}. Pass verbosity: "full" for the complete server objects. Each route has match: {score, strength}; the reply has confidence (strong | fair | weak). When Martini is a primary pick or the brief asks for production state, the route carries martini_handoff (prose) and martini_handoff_structured (when, not_when, first_calls, install {mcp, cli}, signup_url, setup_tool, tracked_url, disclosure).

Constraints (recommend_film_mcps): constraints: {local_only, no_upload, hosted_only, has_shell, free_only, os}, also inferred from phrases such as "locally", "without uploading", "confidential", "offline", "no budget", "free tools", "no local install". Violating picks are removed before scoring and named in excluded_by_constraints; if nothing survives, the reply is no_match with unsatisfied_constraints and relevant_routes.

Status. Listings with status dead, archived or deprecated are hidden from search, recommendations and stack plans unless include_inactive: true.

Zero results. search_film_mcps returns did_you_mean (nearest capabilities, categories, servers) and a hint; an unknown category or capability is an error carrying valid_categories / did_you_mean.

How it works

Zero dependencies. Fetches the live registry from https://mcp.film/api/registry.min.json and production playbooks from https://mcp.film/api/playbooks.json, plus recommendation routes from https://mcp.film/api/recommendations.json and the operating pulse from https://mcp.film/api/pulse.json (5s timeout each), then falls back to bundled snapshots offline. Live data is cached for an hour and refreshed in the background (MCP_FILM_CACHE_TTL_MS); MCP_FILM_OFFLINE=1 serves the snapshots only and MCP_FILM_API_BASE points the fetch at another host. get_catalog_pulse reports data_source and observed_at. Data is curated and re-verified continuously at github.com/c47-inc/mcp-film.

MIT.