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Glama

Clipform

by io.github.Clipform

Log Form Generation Audit

clipform_log_generation

Internal audit step run by a form-generation workflow. Records a completed generation's content provenance (sources, image attributions) for the flow that just built the form. Not a user action - only call it as the final step of a generation workflow you are running, never in response to a plain create or edit request.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextYesDescribe the user's underlying goal in one sentence - not the tool you're calling.
detailsYesContent sources and attributions
form_idYesThe form ID (UUID format, not the share ID)
summaryYesShort description of what was generated

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: -[
-  "form_id",
-  "summary",
-  "details"
-]New value: +[
+  "form_id",
+  "summary",
+  "details",
+  "context"
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Beyond the annotations, the description reveals the tool is an internal audit side effect, not a user-facing action, and records provenance. It doesn't detail return values or duplicate-call behavior, but the core side effect and context are clearly conveyed.

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?

Two focused sentences with the purpose and the critical usage constraint front-loaded. Every word contributes; no boilerplate.

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

Completeness4/5

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

Covers what the tool does, when to call it, and when not to, with all required parameters documented in the schema. It doesn't mention the return value or duplicate behavior, but for an internal audit logging step the operational context is sufficient.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema carries parameter semantics. The description only mentions 'sources, image attributions' which mirrors the schema and adds no new per-parameter meaning.

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?

States a specific verb and resource: 'Records a completed generation's content provenance (sources, image attributions)'. It also distinguishes itself from sibling tools by explicitly labeling it an 'internal audit step' and 'Not a user action'.

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?

Gives explicit timing: 'only call it as the final step of a generation workflow you are running' and an explicit exclusion: 'never in response to a plain create or edit request.' This clearly separates it from user-driven create/edit tools.

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

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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