Tool-Embed: Scalable Tool Embeddings

• Tool-Embed is a method that converts external tools, APIs, and services into vector representations, enabling LLMs to perform efficient retrieval and orchestration.
• It leverages semantic, usage-driven, and hierarchical embedding techniques to capture both functional properties and operational structures of tools.
• This approach improves performance metrics like NDCG@10 and recall, facilitating scalable integration of thousands of heterogeneous tools in AI-powered systems.

A tool embedding is a representation—typically a vector, but sometimes a more structured object—that encodes salient features of an external tool, service, API, or callable function in a form compatible with automated retrieval, reasoning, or orchestration by machine learning systems, particularly LLMs and agents. Tool-embedding approaches address the scalability, efficiency, and compositionality challenges that arise when integrating very large collections of heterogeneous tools with LLM-centric architectures for AI-powered assistants and agents.

1. Conceptual Foundations of Tool Embedding

The integration of external tools into LLM-driven reasoning systems has become essential for augmenting LLM capabilities beyond what can be stored or computed within model parameters. Because tool collections may number in the thousands to millions, embedding each tool into a vector or hierarchical structure supports efficient retrieval and reasoning at scale. "Tool embedding" generalizes word/sentence/document embedding concepts but must capture both functional/semantic properties (e.g., input/output, usage scenario) and operational structure (dependencies, pre/post-conditions, relation to other tools).

Recent work emphasizes that tools are best modeled as compositional elements in a graph or hierarchy, rather than as flat entries in a list (Unlu, 2023). This perspective enables effective tool orchestration and retrieval for both straightforward function invocation and more complex multi-step workflows.

2. Methods and Frameworks for Tool Embedding

Approaches to tool embedding can be broadly divided as follows:

• Semantic Embedding via Textual Description: Encoding the natural language documentation, function signature, and typical usage instructions of a tool using sentence transformers or dense encoders, similar to query/document retrieval (Lu et al., 26 Oct 2025, Moon et al., 2 Sep 2024).
• Usage-Driven Embedding (Tool2Vec): Representing each tool as the average (or other aggregation) of the embeddings of real user queries or invocations involving that tool, aligning retrieval space with practical demand rather than sparse or noisy docs (Moon et al., 2 Sep 2024).
• Structural Embedding via Hierarchical Graphs: Arranging tools in a directed acyclic graph (DAG) or composition tree, with embeddings generated for both leaf (atomic) and internal (composite) tools by recursive aggregation, often via a hierarchical graph neural network (GNN). This supports both structure-aware retrieval and flexible tool composition (Unlu, 2023).
• Augmented Embedding with Expanded Documentation: Systematically expanding tool documentation with structured fields (e.g., “function_description”, “tags”, “when_to_use”, “limitations”) using LLM-driven document expansion prior to embedding, increasing the semantic signal available to the retriever (Lu et al., 26 Oct 2025).
• Contrastive / InfoNCE Learning: Training the embedding model with hard-negative mining and contrastive loss to fine-tune the retrieval space for discriminating between relevant and irrelevant tools for a given query (Lu et al., 26 Oct 2025, Moon et al., 2 Sep 2024).

3. Tool Embeddings for Retrieval and Orchestration

Tool embeddings enable critical capabilities:

• Efficient Tool Retrieval: Given a user query or an LLM-generated intermediate step, retrieve the most relevant tools by semantic or structure-aware similarity (typically via nearest-neighbor search in vector or compositional space) (Lu et al., 26 Oct 2025, Moon et al., 2 Sep 2024).
• Compositional Tool Chaining: Find or synthesize sequences or graphs of tools to solve complex multi-step tasks, leveraging the hierarchical or DAG representation for orchestration (Unlu, 2023).
• Scalability: Perform retrieval and reasoning efficiently regardless of the size of the tool library, avoiding the quadratic (or worse) scaling with the number of tools that occurs in list-based in-context approaches (Liu et al., 29 Feb 2024).

A summary table contrasting exemplar techniques:

Approach / Model	Embedding Basis	Structure Captured	Retrieval Target	Scalability
Tool-Embed (Lu et al., 26 Oct 2025)	LLM-expanded tooldoc	Flat/dense (dual encoder)	Query-tool	High
Tool2Vec (Moon et al., 2 Sep 2024)	Query usage aggregation	Flat/usage-driven	Query-tool(s)	High
Structural DAG Embedding (Unlu, 2023)	GNN on tool hierarchies	DAG/hierarchy	Subgraph or tool	High

4. Practical Algorithms and System Architectures

Dense Retriever (Dual Encoder) Paradigm

A dominant paradigm is the dual encoder: one encoder maps user queries or chain-of-thought (CoT) steps to vectors, another maps tool descriptions (often expanded with structured fields) to vectors. Retrieval is via nearest-neighbor search:

$$\mathcal{L} = -\log \frac{\exp(\operatorname{sim}(\mathbf{q}, \mathbf{d}^+))}{\exp(\operatorname{sim}(\mathbf{q}, \mathbf{d}^+)) + \sum_{i=1}^N \exp(\operatorname{sim}(\mathbf{q}, \mathbf{d}_i^-))}$$

where $\mathbf{q}$ is the query embedding, $\mathbf{d}^+$ and $\mathbf{d}_i^-$ are corresponding positive and negative tool embeddings.

Usage-Based Embedding (Tool2Vec)

For a tool $t$ with usage queries $Q_t$:

$$\mathbf{e}_t = \frac{1}{|Q_t|} \sum_{q \in Q_t} \mathbf{e}_q$$

This aligns the tool embedding with practical user intent, improving accuracy when documentation is poor or inconsistent (Moon et al., 2 Sep 2024).

Structural Hierarchical Embedding

Each node (tool or subtool) receives an initial embedding from its description, and these are recursively aggregated with edge features in a hierarchical GNN:

$$h_{l, i}^{(t)} = \sum_{j \in N_{l, i}} f(v_{l,i}', e_{i,j}', v_{l,j}', h_{l, j}^{(t-1)})$$

where $v_{l,i}'$ is the semantic embedding of node $i$ at level $l$, $e_{i,j}'$ is the edge feature, and $f$ is a recurrent or neural encoder (Unlu, 2023).

5. Empirical Findings and Benchmarks

Numerous studies consistently demonstrate large improvements in tool retrieval task metrics with dedicated tool embedding approaches over naïve list-based or unmatched-document approaches. For example, Tool-Embed achieves significantly higher NDCG@10 (by up to +6.69 points) than state-of-the-art open-source IR baselines on the Tool-DE and ToolRet datasets (Lu et al., 26 Oct 2025). Usage-driven tool embeddings (Tool2Vec) yield recall improvements of over 25–30 points in challenging multi-tool retrieval benchmarks compared to description-based retrievers (Moon et al., 2 Sep 2024). Ablation studies consistently show that semantic expansion (especially short function summaries and high-quality tags) is essential, and that overlong or noisy documentation can dilute retrieval quality if not properly filtered (Lu et al., 26 Oct 2025).

6. Extensions: Tool Embedding in Reasoning, Orchestration, and Unlearning

Tool embeddings underpin not only retrieval but also reasoning and orchestration:

• Hierarchical Task Decomposition: Representing chains of thought as a sequence or graph of tool-like nodes, enabling systematic decomposition and automated assembly of reasoning steps (Unlu, 2023).
• Structured Reasoning Retrieval: Explicitly retrieving or synthesizing multi-step computational plans from stored tool graphs.
• Tool Unlearning: Embedding methods also enable targeted removal (“unlearning”) of unwanted tool skills from LLMs, by aligning the parametric knowledge of an LLM with or against a given tool embedding (Cheng et al., 3 Feb 2025).

7. Limitations and Open Problems

Despite demonstrated gains, tool embedding approaches face several challenges:

• Documentation Quality: Effectiveness depends on the quality of the underlying documentation or usage data; many open datasets are incomplete or inconsistent (Lu et al., 26 Oct 2025).
• Semantic Gap: A persistent challenge is bridging the semantic gap between developer-facing documentation and user-facing queries, motivating usage-based methods (Moon et al., 2 Sep 2024).
• Coverage and Generalization: Embedding strategies must generalize across domains and tool types; overfitting to specific documentation templates or workflows can limit robustness.
• Dynamic Graphs and Orchestration: Capturing temporal, dynamic, or feedback-driven orchestration of tools within the embedding space remains a topic for ongoing research (Liu et al., 29 Feb 2024).

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Tool embedding is thus a central methodological advance for scalable, efficient, and robust integration of external functions and APIs into LLM-centric agents and assistants, enabling both powerful retrieval and complex chain-of-tool reasoning in practical systems (Lu et al., 26 Oct 2025, Moon et al., 2 Sep 2024, Unlu, 2023).