Intent-Aware MCP Server Retrieval

Updated 3 September 2025

The paper introduces an intent-aware architecture that integrates semantic processing and tool orchestration for dynamic MCP server discovery.
It leverages dual-encoder models and weighted TDWA embeddings to efficiently match server metadata with implicit user or agent intents.
The framework supports robust agentic planning and multi-modal, real-time applications while mitigating security threats such as metadata manipulation.

Intent-aware MCP Server Retrieval is a multifaceted paradigm unifying Semantic Retrieval, Tool Orchestration, and Security in LLM agent environments. It centers on enabling agentic systems to autonomously and robustly discover, select, and invoke Model Context Protocol (MCP) servers and their tools in a manner that faithfully aligns with specific, often implicit, user or system intent. This demands precise interpretation of ambiguous requests, dynamic coordination across heterogeneous servers, adaptive agent planning, and advanced mitigation of security threats such as metadata manipulation and adversarial server attacks.

1. Theoretical Foundations of Intent-aware Retrieval

Intent-aware MCP server retrieval is underpinned by architectures that explicitly model and leverage retrieval intent—a semantic abstraction of the user’s or agent’s goal, often encoded as a natural language instruction or decomposed sub-task. The I3 system formalizes intent-introspective retrieval via dual-encoder models augmented by a pluggable, parameter-isolated introspector:

A query $q$ and instruction $\mathcal{I}$ are jointly encoded; $\mathcal{I}$ is projected through zero-initialized layers and combined elementwise at early token layers, then re-integrated into deeper layers via skip connections. The final, intent-conditioned query embedding $E'_Q(q, \mathcal{I}; \Theta'_q)$ is evaluated against document embeddings for relevance:

$s(q, \mathcal{I}, d) = \langle E'_Q(q, \mathcal{I}; \Theta'_q), E_D(d; \Theta_d) \rangle$

Such designs ensure task-specific context is injected without degrading pre-trained capabilities (Pan et al., 2023).

In ScaleMCP, intent awareness is realized by integrating a tool retriever directly into the agent workflow, enabling iterative, on-demand MCP server discovery. The TDWA embedding mechanism further weights components of a tool document (name, description, parameters, synthetic queries) to optimize semantic matching:

$z_{ToolDocument_{WA}} = \frac{\sum_{i=1}^N w_i \cdot \text{Embed}(c_i)}{\|\sum_{i=1}^N w_i \cdot \text{Embed}(c_i)\|_2}$

where $c_i$ is a component and $w_i$ is its weight (Lumer et al., 9 May 2025).

2. Protocol Architecture and Server Discovery

MCP implements a standardized client-server architecture built on persistent session management and JSON-RPC 2.0 messaging (Hou et al., 30 Mar 2025, Chhetri et al., 26 Aug 2025). Core components:

MCP Host: AI application environment embedding the MCP client, orchestrating agentic tasks.
MCP Client: Intermediary for synchronous and asynchronous communication, intent analysis, tool/resource discovery.
MCP Server: Exposes tools, resources, and prompt templates, responding to contextual requests and supporting real-time dynamic operation.

Lifecycle phases (creation, operation, update) include rigorous server registration, integrity verification, dynamic tool execution, sandboxing, version control, and privilege management. Intent-aware retrieval is realized by passing context-rich requests through the client, which then queries available resources/tools matching the semantic intent. Each server’s attributes (type, tags, interface config, GitHub activity) are indexed—MCPCorpus provides an ecosystem-scale corpus with normalized signals facilitating metadata-based retrieval and ranking (Lin et al., 30 Jun 2025).

3. Agentic Planning and Tool Orchestration

Recent architectures such as TURA and MCP-Zero extend retrieval into full agentic planning:

Query Decomposition: An LLM decomposes complex $q$ into atomic sub-queries ( $SQ = \{sq_1, sq_2, \ldots, sq_k\}$ ), each mapped to a specific semantic intent (Zhao et al., 6 Aug 2025).
Augmented Documentation and Embedding: Servers are enriched with synthetic example queries ( $Q^{syn}_i$ ) and multi-vector embeddings to maximize semantic coverage.
Hierarchical Routing: In MCP-Zero, proactive request blocks ( $<$ tool_assistant $>$ ) are mapped first by server, then ranked intra-server by operation, combining similarities $(s_{server} \times s_{tool}) \times \max(s_{server}, s_{tool})$ to strengthen domain and operation alignment (Fei et al., 1 Jun 2025).
DAG-based Task Planner: Task dependencies are modeled as $G = (\mathcal{V}, \mathcal{E})$ , with vertices $v_k = (sq'_k, M_k)$ enabling parallel execution if data dependencies permit (Zhao et al., 6 Aug 2025).
Iterative Invocation: The agent iteratively revises requests in multi-turn scenarios, dynamically refining and assembling toolchains until all subtasks are covered.

Benchmarks such as MCP-Bench operationalize these concepts, requiring agents to infer intent from fuzzy instructions and coordinate across servers in multi-hop workflows, with performance measured by correctness, schema adherence, and trajectory planning (Wang et al., 28 Aug 2025).

4. Security, Fairness, and Ecosystem Threats

Effective intent-aware retrieval presupposes robust defense against vulnerabilities intrinsic to open MCP ecosystems. Threat vectors include:

Preference Manipulation Attacks (MPMA): Malicious MCP servers bias LLM selection through manipulative metadata ( $D_b = D_m \oplus D_{raw}$ , $N_b = N_m \oplus N_{raw}$ ), with genetic algorithms enhancing stealth in the description (Wang et al., 16 May 2025). Economic impacts and fairness deterioration are substantial without defenses such as cryptographic verification, adversarial training, or intent labels.
Tool Poisoning, Puppet, and Rug Pull Attacks: Covert prompt injections, obfuscated malicious code, and metadata corruption can trigger harmful agent actions. User studies show low detection, further exacerbated by security fatigue and ambiguous accountability (Song et al., 31 May 2025).
Mitigation Strategies: Security-first middleware (MCP Guardian) employs authentication, rate-limiting, WAF scanning, and logging/tracing at the request interception layer, blocking unauthorized or malicious invocations with minimal performance overhead ( $\Delta \approx 0.1L-0.15L$ added latency) (Kumar et al., 17 Apr 2025). Multi-layered sandboxing and auditing are recommended for both agentic and platform workflows.

Intent-aware MCP server retrieval is instrumental in heterogeneous, real-world domains:

Multi-modal Data Analytics: TAIJI introduces semantic operator hierarchies, where the NL2Operator agent parses ambiguous queries into structured operator pipelines distributed among foundation models on MCP servers, allowing efficient, scalable analytics across structured, semi-structured, and unstructured data (Zhang et al., 16 May 2025). Updating mechanisms balance data freshness and inference overhead via deep research and machine unlearning.
Autonomous Wireless Space Networks: The Space-O-RAN extension employs MCP servers at lunar assets and mission control. Distributed cognitive agents coordinate via A2A protocols, leveraging semantic, intent-driven context retrieval for telemetry, locomotion, and real-time mission adaptation. Operations span real-time, near-real-time, and non-real-time layers, integrating delay-adaptive reasoning and bandwidth-aware semantic compression (Baena et al., 12 Jun 2025).
Telemetry-Aware Development: MCP-driven IDE architectures (Opik server) centralize telemetry, prompt traces, agent logs, and support real-time, intent-based querying for debugging, prompt optimization, and CI-integrated refinement. Autonomous agents continuously monitor metrics and propose context-sensitive remediation (Koc et al., 14 May 2025).

6. Benchmarking, Ecosystem Analysis, and Future Directions

MCP-Bench provides a rigorous benchmark for tool-using agentic LLMs with multi-step, cross-domain tasks requiring precise intent inference. Tasks are constructed via dependency scaffolds and POMDP-based planning, challenging models to navigate ambiguous instructions and coordinate tool invocations (Wang et al., 28 Aug 2025). Analysis across 20 LLMs reveals strong schema compliance but persistent difficulties in cross-server coordination and dependency resolution.

Ecosystem-scale analyses (MCPCorpus) offer insights into adoption trends, maintenance health, and codebase diversity, enabling retrieval systems to prioritize robust, actively maintained servers. Utility tools synchronize, normalize, and inspect metadata for precise, intent-aligned selection. Future directions include extended metadata enrichment, integrated security/vulnerability assessment, interoperability benchmarking, and dynamic, agent-driven toolchain assembly (Lin et al., 30 Jun 2025).

7. Taxonomies, Interoperability, and Transport Applications

In adaptive transport systems, MCP’s persistent client-server architecture and JSON-RPC structure enable semantic interoperability and dynamic capability exchange (Chhetri et al., 26 Aug 2025). Taxonomies categorize MCP-enabled architectures as unification models bridging adaptive protocols and context-aware frameworks. AI-driven transport infrastructures leverage persistent intent-aware sessions for context fusion, multimodal situational awareness, and real-time decision making, with ongoing research into federated adaptation, edge computing, and secure context negotiation.

Intent-aware MCP Server Retrieval thus fuses semantic intent modeling, dynamic tool orchestration, secure ecosystem management, and benchmark-driven evaluation. It drives modern agentic LLM applications to achieve robust, scalable, and adaptive integration with diverse external services, enabling new paradigms in AI-powered data analytics, software development, real-time automation, and cyber-physical systems.