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Get bridge logs and cook errors

get_bridge_logs
Read-only

Collect cook errors and warnings from a TouchDesigner project to debug failures, walks the operator tree and probes textport/log DATs.

Instructions

Read-only: collect recent cook errors and warnings from the running TouchDesigner project for debugging. Walks the operator tree under scope and gathers each operator's current cook errors and warnings (guaranteed). Also attempts a best-effort probe of textport/log DATs if they exist in the project. Use this when a script or cook fails and you need more context than the immediate error string — it surfaces the real Python traceback or operator cook errors without requiring a new REST endpoint. Returns {lines[], count, probe} where probe reports which log sources were reachable in this TD build.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoNetwork path to collect cook errors/warnings from (default whole project). Must be an existing operator path./
max_linesNoCap how many log lines to return (1–500).
include_cook_errorsNoInclude current operator cook errors/warnings across the scope.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeYesThe network path that was scanned, echoing the request.
linesYesCollected log lines, newest-first within each source.
countYesTotal number of lines returned (after capping at max_lines).
probeNoDiagnostic info about which log sources were reachable in this TD build (cook_errors always present; textport availability varies by build).
warningsYesNon-fatal issues during collection (e.g. truncation notes).
Behavior5/5

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

Beyond annotations (readOnlyHint, destructiveHint, openWorldHint), the description explains the tool walks the operator tree under `scope`, guarantees operator cook errors/warnings, and performs a best-effort probe of textport/log DATs. It also details the return object structure with probe reporting log source reachability.

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 appropriately sized with no fluff. It front-loads the core purpose ('Read-only'), then explains behavior, usage, and return structure. Every sentence is informative and earns its place.

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?

Given the tool's complexity (3 parameters, full schema coverage, output schema), the description is complete. It covers purpose, behavior, usage, and return format comprehensively, enabling an agent to decide when and how to invoke it correctly.

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

Parameters4/5

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

Schema description coverage is 100%, so baseline is 3. The description adds context by explaining the tool walks the operator tree under `scope`, linking to that parameter. It also mentions 'guaranteed' for cook errors, though not parameter-specific. This adds some value beyond the schema's documentation.

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 collects recent cook errors and warnings from a TouchDesigner project for debugging, with specific verb 'collect' and resource 'cook errors and warnings'. It distinguishes from sibling tools like 'summarize_td_errors' and 'get_td_node_errors' by mentioning it walks the operator tree and provides context without requiring a new REST endpoint.

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?

Explicit usage guidance: 'Use this when a script or cook fails and you need more context than the immediate error string — it surfaces the real Python traceback or operator cook errors'. This clearly tells the agent when to use this tool and implies when not to.

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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