List mcp.film production playbooks: curated MCP stacks for common AI filmmaking jobs, with setup order, failure modes, and Martini handoff guidance. Use get_film_playbook for full steps and auth gates.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Optional free-text search over title, summary, best_for, constraints, and server names. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It discloses that playbooks include setup order, failure modes, and handoff guidance, giving useful content context. However, it does not describe return format, pagination, or any side effects, though as a list operation the risk is low. The mention of auth gates in the alternative hints at a limitation but is not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, followed by content detail and sibling pointer. Every clause earns its place, no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with one optional parameter and no output schema, the description provides sufficient context: what the list contains, and where to go for deeper details. The explicit sibling reference covers the main need. A minor gap is absence of return shape, but it is not critical for a list operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single optional 'query' parameter is fully described in the schema with 100% coverage. The description does not add parameter-specific meaning, which is acceptable since the schema already explains the behavior. Baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists production playbooks, specifies what they contain (setup order, failure modes, Martini handoff guidance), and explicitly distinguishes it from get_film_playbook. The verb 'List' combined with the resource 'playbooks' makes the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance: use get_film_playbook for full steps and auth gates, implying this tool is for overview/listing. This directly tells the agent when to choose this tool versus the alternative, exceeding mere context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct entities (capabilities, playbooks, recommendations, servers, install configs). The only potential confusion is between plan_film_stack and recommend_film_mcps, but their descriptions clarify different scopes: full pipeline vs intent-routed brief.
All tools follow a consistent verb-first snake_case pattern: get_ for singular resources, list_ for plural, plus search_, plan_, recommend_, and submit_. No mixed conventions or vague verbs.
15 tools is at the upper end of the ideal range but each earns its place: search, retrieval, listing, planning, recommendation, install config, and submission. No redundant tools.
Covers the full directory lifecycle: discovery, retrieval, contextual listings, planning, recommendations, install config generation, and submission. Minor gap: there's no explicit list-all-servers endpoint, but search_film_mcps can fulfill that role.
