Skip to main content
Glama

Ground Host-Authored TD Brain Draft

td_brain_ground
Read-only

Collects task features, corpus evidence, operator contracts, and live state to produce a grounding pack for drafting a visual programming task.

Instructions

Use this when the request is artistic, multi-domain, spatial, or implicit-architecture requests, and whenever td_brain_plan returns blocked: it returns a read-only grounding pack (task features, corpus evidence, candidate operators, parameter contracts, operator availability, live state, exemplars, and the draft authoring contract) so you can write a draft for td_brain_propose. Do not use it for trivial single-node edits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNoGrounding mode: 'auto', 'fast', 'production', or 'show_safe'.auto
intentYesNatural-language visual programming task to ground.
output_topNoOptional final TOP path required by the intent.
constraintsNoHard task constraints preserved into the grounded task.
target_rootNoAbsolute TD parent/root path the draft will build inside./project1
trace_levelNoTrace detail: 'none', 'summary', or 'debug'.summary
include_memoryNoInclude compact local technique, knowledge, and promoted-trace recall.
preferred_domainsNoPreferred TD data domains: TOP, CHOP, SOP, POP, DAT, COMP, MAT.
include_live_stateNoInclude existing node names/types at target_root when TD is reachable.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description confirms it returns a read-only grounding pack. It lists the contents of the grounding pack, adding behavioral context beyond annotations. No contradictions.

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 two sentences, front-loading the purpose and usage conditions, and including a clear don't-use case. Every sentence adds value, with no redundancy or fluff.

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?

The description explains the tool's role in a workflow (grounding before proposing) and when to use it. With schema covering all parameters and an output schema existing, the description provides sufficient conceptual completeness for an agent to select and invoke the tool correctly.

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 coverage is 100%, so all parameters have descriptions in the schema. The tool description does not add additional meaning or usage guidance for parameters beyond what is in the schema, meeting the baseline but not exceeding it.

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 clearly states the tool grounds host-authored TD brain drafts for artistic, multi-domain, spatial, or implicit-architecture requests, especially when td_brain_plan returns blocked. It distinguishes this tool from siblings like td_brain_plan and td_brain_propose.

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?

Explicitly states when to use this tool (for specific request types and when td_brain_plan returns blocked) and when not to use it (trivial single-node edits). Provides clear context.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/dreamrec/TDPilot'

If you have feedback or need assistance with the MCP directory API, please join our Discord server