Tool-to-Agent Retrieval Framework

Updated 5 November 2025

Tool-to-agent retrieval frameworks are systems that select and route tools to LLM agents, ensuring functional compatibility beyond simple semantic matching.
They employ diverse methodologies including plan-execute-evaluate loops, generative token-based approaches, and graph-based expansions to optimize tool selection.
Empirical evaluations demonstrate improved retrieval performance using metrics like Pass Rate@K and Recall@K through combined semantic and execution-based validations.

A tool-to-agent retrieval framework orchestrates the selection and routing of tools and capabilities to agents—typically LLM-based—based on natural language queries or tasks. Its central purpose is to enable agents to accurately and robustly identify not just semantically related tools, but also those that are functionally suitable and executable within their operational context. This approach addresses challenges arising from the semantic-functional gap, scalability, context length, dynamic library growth, and the compositional complexity of multi-step agentic workflows.

1. Semantic-Functional Gap in Tool Retrieval

The semantic-functional gap refers to the disconnect between retrieving tools that are textually or semantically relevant to a user's request and ensuring that these tools are actually functional—that is, executable, compatible with agent requirements, and contextually appropriate for the sought action. Empirical studies, such as those in GRETEL (Wu et al., 10 Oct 2025), demonstrate that a large fraction (85% of top-5 tool candidates on ToolBench) of semantically retrieved tools either suffer from parameter mismatches, execution failures, or semantic ambiguities, with only 15% achieving functional success. This issue is formalized as

$P(\text{functional} \mid \text{semantic}) \ll P(\text{functional}),$

indicating that semantic similarity is a poor proxy for operational suitability. Failure to close this gap can lead to brittle, error-prone agent behavior in deployment.

2. Architectural Principles and Framework Classes

Tool-to-agent retrieval frameworks vary across a spectrum of architectural paradigms:

Plan-Execute-Evaluate Cycles: GRETEL (Wu et al., 10 Oct 2025) and related frameworks advocate an agentic loop of semantic retrieval → planning API calls → sandboxed execution → empirical validation and holistic re-ranking based on functional evidence. Only tools passing trial execution and parameterization are considered truly relevant.
Unified Generative Approaches: ToolGen (Wang et al., 4 Oct 2024) internalizes tool selection and invocation into next-token language modeling, mapping each tool to a unique virtual token and training the LLM to generate tool calls and arguments jointly, thereby subsuming retrieval into generation.
Knowledge Graph-based Expansion: Planning Agents on an Ego-Trip (Bansal et al., 7 Aug 2025) use structured KGs to explicitly model tool-tool, tool-parameter, and functional dependencies, enabling hybrid ego-graph ensemble expansion and contextually coherent retrieval for multi-step or compositional tasks.
Multi-Agent and Orchestration Strategies: Frameworks such as MetaAgent (Qian et al., 1 Aug 2025) and TUMIX (Chen et al., 30 Sep 2025) emphasize agent ensembles, tool routers, or mixture-of-agent strategies to distribute retrieval, enable diversity in tool-use policy, and perform iterative answer refinement.

The following table summarizes representative retrieval modalities:

Framework/Paper	Retrieval Modality	Unique Features
GRETEL (Wu et al., 10 Oct 2025)	Sandbox trial, LLM agentic loop	Execution evidence, re-ranking
ToolGen (Wang et al., 4 Oct 2024)	Generative (token-based)	One-token/tool, no retrieval module
Ego-Trip (Bansal et al., 7 Aug 2025)	Graph traversal + hybrid search	KG for multi-step composition
MetaAgent (Qian et al., 1 Aug 2025)	Help request routing	Contextual meta-tool learning
Toolshed (Lumer et al., 18 Oct 2024)	RAG pre/intra/post-fusion	Enhanced vector DB + query planning
TURA (Zhao et al., 6 Aug 2025)	Intent-aware, DAG planner	MPC tool unification, latency/batch
TUMIX (Chen et al., 30 Sep 2025)	Multi-agent, answer sharing	Ensemble agent/tool-use mixture

3. Retrieval Algorithms and Execution Pipelines

Semantic Retrieval and Execution Validation

A canonical pipeline, as formalized in GRETEL (Wu et al., 10 Oct 2025), comprises:

Semantic Embedding Retrieval:

$R(q) = \mathrm{Top}\text{-}k(\mathrm{Sim}(q, t_1), \ldots, \mathrm{Sim}(q, t_n))$

Initial candidate selection via embedding-based semantic similarity.

Plan-Execute-Evaluate: For each tool $t_i \in R(q)$ :

Planning (LLM generates call parameters)
Attempted execution in sandbox
On execution failure, optional LLM-simulated response for structural correctness

Re-ranking by Execution Evidence:

$R'(q) = \mathrm{Sort\,by\,Evidence}(\{(t_i, status_i, result_i, metadata_i)\}_{i=1}^k)$

Tools not amenable to valid parameter construction or failing execution are deprioritized or filtered. Aggregate trial evidence determines final ranking.

Dynamic and Multi-hop Retrieval

Advanced frameworks exploit graph-based expansion (ego-graph ensembles (Bansal et al., 7 Aug 2025)), or agentic pipelines that can handle multi-intent and multi-step queries. For example, hybrid KG methods combine dense embedding similarity and local graph traversal to model both direct and indirect tool dependencies, producing expanded candidate sets aligned with the structure of complex enterprise tasks.

Generative and Unified Representations

ToolGen (Wang et al., 4 Oct 2024) dispenses with external retrieval entirely. Virtual tool tokens augment the LLM's vocabulary. Tool retrieval and invocation reduce to next-token prediction, and constrained decoding ensures only valid, atomic tool calls are generated. This approach bypasses memory and latency bottlenecks in traditional retrieval pipelines.

4. Empirical Evaluation and Performance Metrics

Rigorous empirical benchmarking is standard. Metrics include:

Pass Rate@K: Fraction of queries with at least one functionally successful tool in the top-K retrieved.
Recall@K: Fraction of ground-truth tools present in top-K results.
NDCG@K: Discounted cumulative gain for rank-aware retrieval quality.

Example results (ToolBench G1 set, from GRETEL (Wu et al., 10 Oct 2025)):

Method	Pass Rate@10	Recall@10	NDCG@10
ToolBench-IR	0.690	0.841	0.807
GRETEL	0.826	0.867	0.857

In qualitative analysis, execution-based validation consistently reveals that semantic filtering alone yields high rates of non-functional matches, reinforcing the necessity of empirical tool trials. Toolshed's (Lumer et al., 18 Oct 2024) ensemble RAG-fusion methods realize up to 46–56% absolute improvements in Recall@5 over BM25 and strong dense retrieval baselines on standard benchmarks.

5. Advances, Trade-offs, and Scalability Considerations

Trade-offs

Execution Overhead: Integrating execution trials increases computational cost and latency, though it offers significant accuracy improvements and is justified for high-precision applications.
Autonomy vs. Generalization: Token-based generation (as in ToolGen) supports high autonomy and scalability, though generalization to unseen tools remains weaker than matching-based approaches.
Complexity/Resource Utilization: KG-based and graph-expansion approaches demonstrate superior recall in enterprise, multi-step workflows but entail added complexity in KG construction and maintenance.

Scalability

Very Large Toolsets: Generative approaches (ToolGen), vector store architectures (Toolshed), and external retriever+agent decompositions (TURA) scale to tens of thousands of tools with minimal context window or inference cost increase.
Dynamic Growth: Frameworks with recursive search and on-the-fly tool adaptation (Tulip Agent (Ocker et al., 31 Jul 2024), ToolMaker (Wölflein et al., 17 Feb 2025)) allow for CRUD (create, read, update, delete) operations and dynamic toolset evolution.

Robustness

Empirical/generative reranking and dynamic meta-learning (MetaAgent (Qian et al., 1 Aug 2025), GRETEL (Wu et al., 10 Oct 2025)) are particularly effective at bridging the semantic-functional gap and supporting agents in unseen or evolving tool environments.
Error mitigation: Post-retrieval simulations, memory folding (DeepAgent (Li et al., 24 Oct 2025)), and meta-level reflection reduce error accumulation and maintain robust retrieval performance under real-world constraints.

6. Applications and Impact

Tool-to-agent retrieval frameworks underpin robust, scalable LLM agent applications in:

Automated process engineering and scientific workflows (e.g., RAIT (Sakhinana et al., 28 Aug 2024), ToolMaker (Wölflein et al., 17 Feb 2025))
Enterprise task planning with multi-step reasoning (KG-ensembles (Bansal et al., 7 Aug 2025))
Production-grade AI search platforms unifying static RAG and dynamic tool access (TURA (Zhao et al., 6 Aug 2025))
Multi-agent orchestration for open-domain reasoning and answer verification (TUMIX (Chen et al., 30 Sep 2025), AgentScope (Gao et al., 22 Aug 2025))

Empirical results establish tool-to-agent retrieval frameworks as essential for realizing robust, functionally reliable, and context-adaptive agentic systems. Execution-based and generative approaches will likely remain foundational as agentic paradigms continue to scale in tool diversity, reasoning complexity, and deployment settings.