Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.bedrock.cv/llms.txt

Use this file to discover all available pages before exploring further.

For most queries, follow this progression:
  1. Discover — Query with entity: "project" to find the right project, then entity: "drawing" to see what’s uploaded
  2. Explore — Query with entity: "sheet" to browse sheets and their block structure
  3. Search — Search for natural language queries, Ask for direct questions
  4. Drill down — Query with entity: "block" for OCR, metadata, overlays; entity: "feature" for rooms, doors, symbols
  5. Visualize — ViewImage to convert storage URIs to viewable URLs
  6. Compare — Compare to diff revisions, Parse to extract features, PollJob to poll
If the user already provides a project ID or sheet number, skip the discovery step.

​
Prompt Tips

​
Be specific about sheet numbers

The agent performs better with explicit sheet references. Discipline codes and sheet numbers eliminate ambiguity.
PromptQuality
”What’s on the electrical drawings?”Vague — many sheets, unclear intent
”How many duplex receptacles are on E-201?”Specific — one sheet, one feature type, clear metric
”What changed on A-101 between Rev A and Rev B?”Specific — two revisions, one sheet

​
Use discipline codes

When referring to trades, use standard discipline codes. The agent maps these directly to the discipline filter.
CodeDiscipline
AArchitectural
SStructural
MMechanical
EElectrical
PPlumbing
FPFire Protection

​
Ask for one thing at a time

Multi-part questions (“count receptacles on E-201 AND check fire ratings on A-101”) often produce better results as separate prompts. Each focused question maps to a clean tool call sequence.

​
Context Management

​
Use progressive disclosure

Do not request heavy data unless needed. The include parameter controls token cost. 
{
  "entity": "block",
  "project_id": "proj_oak",
  "sheet_number": "A-101",
  "type": "Plan"
}
Returns block summaries with relations. Low token cost.
A good pattern: start with the default response, let the agent read relations and metadata to understand the data structure, then selectively request include fields only for the specific blocks that need them.

​
Let pass-through filters do the work

Instead of resolving entity IDs manually, use pass-through filters to skip round-trips: 
// Instead of: Query entity:"sheet" to get sheet ID, then Query entity:"feature" with sheet_id
// Do this:
{
  "entity": "feature",
  "project_id": "proj_oak",
  "sheet_number": "E-201",
  "type": "duplex_receptacle"
}
Available pass-through filters:
  • entity: "block": sheet_number, drawing_id
  • entity: "feature": sheet_number, block_type, block_identifier, grid_intersection

​
Error Handling

All tools return errors in a consistent shape: 
{
  "error": "not_found",
  "message": "Sheet E-999 not found in project proj_oak"
}
Error CodeMeaningRecommended Action
not_foundEntity does not exist or is not accessibleVerify the ID or filter values; check project scope
invalid_inputBad parameters (invalid filter combination, unknown type)Review the parameter values and types
credits_exhaustedInsufficient credits for vision operationsAlert the user; vision operations (Compare) require credits
job_failedAsync job encountered an errorCheck the error message; retry or report to the user
The credits_exhausted error only applies to the Compare and Parse tools. The Query, Search, Ask, ViewImage, and PollJob tools do not consume credits.

​
Performance Tips

​
Batch feature queries by sheet

When analyzing multiple feature types on the same sheet, combine them into fewer calls: 
// Query all features on E-201 at once
{
  "entity": "feature",
  "project_id": "proj_oak",
  "sheet_number": "E-201"
}
Then filter in the agent’s reasoning rather than making separate calls for each feature type.

​
Use grid_intersection for cross-discipline queries

Instead of querying each discipline separately and correlating results manually, use the Query tool’s grid_intersection filter: 
{
  "entity": "feature",
  "project_id": "proj_oak",
  "grid_intersection": "B-3",
  "include": ["parent_chain"]
}
This returns features from all disciplines at that grid location in a single call. The backend handles the spatial math.

​
Sort comparison results by score

After a comparison completes, the PollJob results include a score for each overlay. Lower scores indicate more changes. Focus on low-score sheets first: 
// From PollJob result:
{
  "overlays": [
    { "score": 0.82, "block_b": { "sheet_number": "A-101" } },  // Most changes
    { "score": 0.97, "block_b": { "sheet_number": "A-102" } }   // Minimal changes
  ]
}
Drill into the A-101 overlay first with Query with entity: "block" and include: ["overlays", "changes"].

​
Next Steps

Tools Reference

Full parameter documentation for all seven tools.

Workflow Examples

See complete tool call sequences for real construction queries.