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chatlas currently ships two first-class built-in (provider-executed) tools: tool_web_search() and tool_web_fetch() (#248), plus the generic ToolBuiltIn escape hatch (#134). This is a tracking issue for expanding that lineup, based on a July 2026 survey of the provider-executed tools offered by Anthropic (Messages API), OpenAI (Responses API), and Google (Gemini API / Vertex AI).
What the survey found
The three first-class providers have converged on a similar set of server-executed tools — the kind that fit the existing ToolBuiltIn model, where the provider runs the tool and chatlas only needs to send a config and parse result content:
Tool
Anthropic
OpenAI (Responses)
Google Gemini
Web search
✅ GA (shipped)
✅ GA (shipped)
✅ GA (shipped)
Web fetch / URL context
✅ GA (shipped)
❌
✅ GA (shipped)
Code execution
✅ GA
✅ GA (code_interpreter)
✅ GA
Remote MCP connector
✅ beta
✅ GA (mcp)
⚠️ experimental (not Vertex)
File search (hosted RAG)
❌
✅ GA
✅ GA (not Vertex)
Tool search / deferred tools
✅ GA
✅ GA
❌
Image generation
❌
✅ GA
❌ (separate models)
Maps grounding
❌
❌
✅ GA
A second family is provider-defined but client-executed (Anthropic: memory / bash / text editor / computer use; OpenAI: computer use / apply_patch / local shell; Google: computer use). These have official schemas but the application must execute them, so they don't fit ToolBuiltIn — supporting them means shipping an execution harness, a much larger design.
Computer use, bash, text editor, apply-patch, shell — client-executed; chatlas would have to ship a screenshot/action or command-execution harness. Out of scope for a chat client.
Image generation (OpenAI-only) and Maps grounding (Gemini-only) — single-provider; the ToolBuiltIn escape hatch already handles them (image generation is the worked example in Using built-in tools #134). A docs recipe may be worth more than first-class wrappers.
Anthropic tool search / advisor tool — too new/niche; revisit if users accumulate large deferred-tool catalogs.
Anthropic memory tool — client-executed but cheap to back with a filesystem; interesting, but a different pattern from ToolBuiltIn. Revisit separately if there's demand.
Cross-provider caveats that affect all of the above
Gemini models before Gemini 3 can't mix built-in tools with custom function declarations in one request.
ellmer currently has no code execution or structured citation model, so this is an area where chatlas leads; worth coordinating naming/design with the ellmer team as these land.
chatlas currently ships two first-class built-in (provider-executed) tools:
tool_web_search()andtool_web_fetch()(#248), plus the genericToolBuiltInescape hatch (#134). This is a tracking issue for expanding that lineup, based on a July 2026 survey of the provider-executed tools offered by Anthropic (Messages API), OpenAI (Responses API), and Google (Gemini API / Vertex AI).What the survey found
The three first-class providers have converged on a similar set of server-executed tools — the kind that fit the existing
ToolBuiltInmodel, where the provider runs the tool and chatlas only needs to send a config and parse result content:code_interpreter)mcp)A second family is provider-defined but client-executed (Anthropic: memory / bash / text editor / computer use; OpenAI: computer use /
apply_patch/ local shell; Google: computer use). These have official schemas but the application must execute them, so they don't fitToolBuiltIn— supporting them means shipping an execution harness, a much larger design.Sub-issues
Roughly by priority:
tool_code_execution(): the only remaining server tool that is GA across all three providersregister_mcp_tools_*supportExplicitly not planned (for now)
ToolBuiltInescape hatch already handles them (image generation is the worked example in Using built-in tools #134). A docs recipe may be worth more than first-class wrappers.ToolBuiltIn. Revisit separately if there's demand.Cross-provider caveats that affect all of the above