Plexara
Connecting is the easy twenty percent
Giving an agent the ability to call an external API is a short demo. You hand it a base URL and a credential, and it makes a request. The hard part is everything after that first successful call: what happens when the API has hundreds of operations, when its description is imprecise, when it changes next month, or when it returns something the agent did not expect.
An agent that rediscovers an API shape on every request is both slow and fragile. It spends tokens relearning what is available and breaks the moment the API drifts from what it assumed. Reliability comes from giving the agent a stable, structured understanding of the API to reason against, rather than making it improvise each time.
A versioned catalog the agent can reason about
Plexara gives each connected API a versioned catalog: a structured record of its operations and their inputs that the agent can consult instead of reverse-engineering the API live. Because it is versioned, the agent reasons against a known shape, and changes to the API are something the catalog absorbs rather than something that quietly breaks behavior.
The catalog also accommodates the realities of production APIs. It handles operations that require a second credential header beyond the primary authentication, and it tolerates the imperfections and inconsistencies common in real-world API descriptions, rather than assuming every provider published a flawless specification.
This is what lets the agent keep a small, fixed set of tools while reaching an unbounded number of operations. The tools traverse the catalog; the catalog holds the detail. Adding an API adds entries to the catalog, not weight to the agent.
Search operations by intent, not exact words
A large API can expose hundreds of operations. Finding the right one by exact keyword is the same losing game as finding a tool by exact name. The agent should be able to ask for the capability it needs and get the operation that provides it, even when the naming does not match.
Plexara ranks API operations by intent, so the agent locates the operation that does what it means rather than the one whose name happens to contain the search term. The agent describes the goal; the catalog returns the operation that meets it.
Reliability is what makes it production-grade
Alongside the catalog, sign-in became more dependable: automatic token refresh before expiry, a clear history of authentication events, and better errors when something is wrong. These are the details that separate a connection that works once from one you can leave running.
A versioned catalog, intent-based operation search, and reliable sign-in all serve the same goal: making the agent use of an external API predictable enough to run against real systems unattended.
Further Reading
Code execution with MCP: Building more efficient agents
Anthropic (Adam Jones and Conor Kelly) · 2025
Anthropic's engineering team documents that loading all MCP tool definitions upfront occupies context-window space, increases cost and latency (hundreds of thousands of tokens before reading a request), and that loading only needed tools on demand cut one example from 150,000 to 2,000 tokens.
Gorilla: Large Language Model Connected with Massive APIs
Shishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. Gonzalez · 2023
NeurIPS 2024 paper showing LLMs tend to hallucinate incorrect API usage, and that retriever-aware training lets a model adapt to test-time API changes such as version evolution and argument changes.
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Yujia Qin, Shihao Liang, Yining Ye, et al. · 2023
ICLR 2024 paper that collects 16,464 real-world RESTful APIs and equips the model with a neural API retriever that recommends appropriate APIs per instruction instead of exposing the full catalog, demonstrating semantic/intent-based retrieval over large real-world API sets.
Semantic Tool Discovery for Large Language Models: A Vector-Based Approach to MCP Tool Selection
Sarat Mudunuri, Jian Wan, Ally Qin, Srinivasan Manoharan · 2026
Describes a vector-based retrieval architecture that indexes tools with dense embeddings capturing the relationship between tool capability and user intent, selecting only the few most relevant tools rather than exposing the entire catalog, with large reductions in tool-related token consumption.