Skip to main content
Glama

Clipform

by io.github.Clipform

Get Build Workflow

clipform_get_workflow
Read-onlyIdempotent

Retrieve a step-by-step build workflow for creating a specific form type. Returns the exact tool sequence, form settings, node configuration, scoring setup, and end screen config as a build recipe.

Does NOT return craft knowledge (question psychology, difficulty curves, narration style) - use clipform_get_guide for that.

Available types: quiz, survey, interview, testimonial, application, booking, intake, lead-capture. Aliases also accepted: trivia → quiz, test → quiz, exam → quiz, feedback → survey, poll → survey, nps → survey, questionnaire → survey, case-study → interview, callout → interview, lead-gen → funnel, qualification → funnel, lead-magnet → funnel, story → testimonial, review → testimonial, job-application → application, admission → application, enrollment → application, grant → application, registration → booking, signup → booking, event → booking, rsvp → booking, workshop → booking, client-intake → intake, onboarding → intake, enquiry → intake, inquiry → intake, new-client → intake, lead → lead-capture, lead-form → lead-capture, enquiry-form → lead-capture, waitlist → lead-capture, get-a-quote → lead-capture. Quiz variants (optional): personality, comprehension, composition - returns the variant-specific workflow instead of the base quiz workflow.

Optional args by type:

  • quiz: topic, question_count (8)

  • quiz (variant: personality): topic, categories, question_count (8)

  • quiz (variant: comprehension): youtube_url, question_count (8), audience

  • interview: purpose, response_format (all), needs_consent (true)

  • survey: topic, anonymous (true)

  • funnel: outcomes, criteria, needs_contact (true)

  • testimonial: use_case

  • application: role

  • booking: event_name, event_type

  • intake: use_case, response_format (all)

  • lead-capture: use_case

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleNoWhat the application is for (application)
typeYesForm type to get the build workflow for (accepts aliases like 'feedback' → survey)
topicNoTopic or theme (quiz, survey)
contextYesDescribe the user's underlying goal in one sentence - not the tool you're calling.
purposeNoWhat you're collecting (interview)
variantNoQuiz sub-variant. Only used when type is 'quiz'. Omit for a standard scored quiz.
audienceNoTarget audience (quiz variant: comprehension)
criteriaNoRouting criteria (funnel)
outcomesNoSegments or recommendations (funnel)
use_caseNoWhat stories to collect (testimonial)
anonymousNoAnonymous survey (survey)
categoriesNoComma-separated outcome categories (quiz variant: personality)
event_nameNoEvent or offering name (booking)
event_typeNoType of event (booking)
youtube_urlNoYouTube video URL (quiz variant: comprehension)
needs_consentNoInclude consent statement (interview)
needs_contactNoInclude contact capture (funnel)
question_countNoNumber of questions, default 8 (quiz types)
response_formatNoHow respondents reply (interview)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed3 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Describe the user's underlying goal in one sentence - not the tool you're calling.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "type"
-]New value: +[
+  "type",
+  "context"
+]
  2. First observed

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already signal readOnly and idempotent, and the description reinforces this as a retrieval operation. It also discloses what the tool will and will not return, including the negative boundary around craft knowledge and the variant-specific workflow behavior, which goes beyond the annotations and helps set expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every section earns its place: core behavior first, exclusions second, type list, aliases, variants, then type-specific args. The structured layout makes the large amount of necessary information scannable, and there is no filler or redundancy with the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 19 parameters and no output schema, the description is remarkably complete: it states return value shape, supported types, aliases, variants, and per-type optional parameters. The only element not expanded on is the required 'context' parameter, but the schema already documents it clearly, so the description does not need to repeat it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds substantial meaning beyond the schema: default values (question_count 8, response_format all, needs_consent true), which optional args apply to which type, and how variants change the meaning of parameters. The alias list also clarifies how non-canonical type values map to supported types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Retrieve a step-by-step build workflow for creating a specific form type.' It then itemizes exactly what the returned recipe contains, and explicitly contrasts itself with clipform_get_guide, so an agent can distinguish it from siblings without ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use guidance and names the alternative: 'Does NOT return craft knowledge... use clipform_get_guide for that.' It also provides all accepted aliases, variant conditions, and type-specific optional arguments, so the agent knows exactly which parameters apply for each form type.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Clipform toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes: form CRUD, node management, media upload/attach, rendering, search, and guidance retrieval are all separable. The main overlap is among the three render tools (clipform_generate_video, clipform_render_video_template, clipform_render_composition), but their descriptions include explicit disambiguation guidance, so an agent can correctly choose. clipform_get_guide and clipform_get_workflow are also similar but clearly differentiated.

Naming Consistency4/5

Tool names follow a consistent clipform_<verb>_<noun> pattern throughout, e.g., clipform_create_form, clipform_add_node, clipform_update_node, clipform_delete_node. Minor deviations exist: clipform_whoami is not verb_noun, and get_more_tools lacks the clipform_ prefix, but these are edge cases and the overall convention is highly predictable.

Tool Count3/5

34 tools is on the heavy side for a single MCP server. The server covers a broad domain (form creation, node editing, media management, video rendering, TTS, search, guidance, imports, responses), so the count is defensible, but it is above the typical well-scoped range and may add navigation overhead.

Completeness5/5

The tool surface covers the full lifecycle: create/read/update/delete forms and nodes, media upload/attach/delete, multiple render paths with status checking, TTS generation, music/image/video search, form import, response retrieval, and workflow/guide knowledge. The main gap is lack of a direct branching-logic editor (option-based branching is only in the dashboard), but the API consciously documents that limitation and the rest of the lifecycle is complete.

Resources