nlweb-tools-framework

name	nlweb-tools-framework
description	Design and implement NLWeb tools — the per-Schema.org-type handlers that turn a query into a specialized response (search, item_details, compare_items, ensemble, recipe_substitution, accompaniment, conversation_search, etc.). Covers `tools.xml`, the ToolSelector router, builtin handlers in `methods/`, writing a custom tool with a `<returnStruc>` contract, and disabling tool selection for raw retrieval. Use when extending NLWeb beyond the default query → results flow.
allowed-tools	Read, Write, Edit, Bash, Grep, Glob, WebSearch, WebFetch

NLWeb Tools Framework

Before writing code

Fetch live docs:

1. Fetch https://github.com/nlweb-ai/NLWeb/blob/main/docs/tools.md for the canonical tools framework reference.
2. Fetch https://github.com/nlweb-ai/NLWeb/blob/main/config/site_types.xml for the per-type tool inheritance tree.
3. Read AskAgent/python/core/router.py::ToolSelector for how routing actually picks a tool.
4. Read existing handlers in AskAgent/python/methods/: generate_answer.py, item_details.py, compare_items.py, ensemble_tool.py, recipe_substitution.py, accompaniment.py.
5. Fetch https://github.com/nlweb-ai/NLWeb/blob/main/docs/nlweb-prompts.md for the <returnStruc> JSON contract that handlers must satisfy.

Conceptual Architecture

What a "Tool" Is in NLWeb

Confusingly, "tool" means two different things in NLWeb depending on context:

1. Internal tool / handler — a Python module in methods/ that the ToolSelector routes a query to (e.g., compare_items.py). This is the meaning used in this skill.
2. MCP tool — the JSON-RPC tool exposed at /mcp (ask, list_sites, who). See the nlweb-mcp-server skill for that meaning.

When NLWeb's docs say "tools framework," they mean (1).

The Tool Routing Flow

For every /ask request:

1. ToolSelector (core/router.py) inspects the decontextualized query + detected Schema.org type.
2. It consults site_types.xml / tools.xml for the candidate tools for that type.
3. It makes an LLM call (with a strict <returnStruc> JSON output schema) asking "which tool fits?"
4. The selected handler in methods/<tool>.py is invoked.
5. The handler runs retrieval + ranking + any tool-specific logic, then emits results.

Built-In Handlers

Handler	Purpose
generate_answer.py	RAG synthesis — used for mode=generate
item_details.py	Deep-dive on a single result
compare_items.py	Side-by-side comparison of 2+ results
ensemble_tool.py	Multi-tool composition (e.g., "find a recipe and pair a wine")
recipe_substitution.py	Suggest ingredient swaps in a Recipe
accompaniment.py	"Goes with" suggestions (wine for food, sides for entrée)
multi_site_query.py	Query that spans multiple sites
conversation_search.py	Search within prior conversation context
statistics_handler.py	Aggregations over indexed data

There are also demo-specific handlers like cricketLens.py / cricket_query.py showing how to build a deeply specialized domain tool.

The <returnStruc> Contract

Every LLM call NLWeb makes is paired with a <returnStruc> block in prompts.xml defining the exact JSON shape expected back. Example for tool selection:

<returnStruc>
{
  "selected_tool": "compare_items",
  "confidence": 0.92,
  "reasoning": "User explicitly asked to compare two products"
}
</returnStruc>

This is mixed-mode programming in action — the LLM output is parsed as JSON and drives Python control flow. Handlers themselves use <returnStruc> for their own LLM calls (rank results, generate summary, extract key fields).

Tool Inheritance via site_types.xml

site_types.xml maps Schema.org @type values to allowed tools, with inheritance:

<site_type name="Recipe" extends="CreativeWork">
  <tool>search</tool>
  <tool>item_details</tool>
  <tool>recipe_substitution</tool>
  <tool>accompaniment</tool>
</site_type>

Tools inherit from parent types; specific overrides take precedence. The default site_type catches everything not enumerated.

Disabling Tool Selection

For debugging or raw retrieval, set in config_nlweb.yaml:

tool_selection_enabled: false

This bypasses the router entirely — every query goes through plain retrieval + ranking. Useful for:

• Diagnosing whether bad results come from retrieval or tool routing
• Reducing LLM call count on a budget
• Sites where every query has the same shape

Tool vs Mode

Don't confuse these:

• mode (request param) = list / summarize / generate — controls the output style
• "Tool" = which handler module processes the request

A mode=generate query may be routed through compare_items, recipe_substitution, or generate_answer depending on what the router picks.

Implementation Guidance

Writing a Custom Tool

Add a new handler in methods/<your_tool>.py:

# Sketch — verify base class signature in current methods/*.py files
class YourToolHandler:
    name = "your_tool"
    description = "Handles queries of pattern X for type Y"

    async def handle(self, query, site, schema_type, context, stream):
        # 1. Retrieve relevant items
        items = await context.retriever.search(query, site=site)
        # 2. Rank
        ranked = await context.ranker.rank(items, query)
        # 3. Run any tool-specific LLM call(s)
        # 4. Stream results back
        await stream.send({"results": ranked[:5]})

Register the tool:

1. Add to tools.xml (or config_tools.yaml if that's where the registry lives in current code).
2. Add the tool name to relevant site_type entries in site_types.xml.
3. Add a <promptString> entry in prompts.xml if your tool needs an LLM call with a <returnStruc>.

When to Build a Custom Tool vs Use Built-Ins

Build a custom tool if:

• Your domain has a specific query pattern not covered (e.g., "compatibility check" for hardware parts).
• Results need post-processing beyond ranking (e.g., merging two records into one).
• You need to call an external API as part of the response (e.g., live pricing lookup).

Use a built-in if:

• It's a vanilla "find + summarize" — generate_answer.py handles it.
• You want comparison or details — compare_items / item_details.

Crafting a Good <returnStruc>

• Be strict about field names and types — the parser is unforgiving.
• Include reasoning fields (reasoning, confidence) — helps debugging and lets you log model decisions.
• Use enums for categorical fields — reduces hallucinations.
• Keep it small — every extra field is more LLM tokens and more parsing failure surface.

Testing a Custom Tool

# Force the router to pick your tool:
curl 'http://localhost:8000/ask?query=test&site=X&streaming=false&forced_tool=your_tool'

(Verify forced_tool param name in current code — may be a different name or only available in mode: development.)

Tool Ordering and Conflicts

If multiple tools could fit a query, ToolSelector picks one. To bias selection:

• Make your tool's description more specific
• Adjust site_types.xml to put your tool earlier in the list for relevant types
• Increase the <returnStruc> confidence threshold in prompts.xml

Common Pitfalls

• Tool registered but never picked — its <promptString> description is too vague; the router can't tell when to use it.
• Tool runs but returns nothing — handler is using the wrong retriever or filtering too aggressively.
• LLM returns invalid JSON — <returnStruc> is too complex or the model tier is too low; bump to high for that call.
• Inheritance not applying — site_types.xml extends attribute typo'd or the parent type not defined.

Always cross-reference methods/ and site_types.xml in the live repo — both move fast.