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Why defer tool schemas?
A model needs a tool’s parameter schema to call that tool. When an assistant exposes dozens of tools at once, those definitions take up context on every request, including requests that need none of them. Verma frames the problem around tools such as web_search and git_diff: making all possible schemas available in advance has a context cost before the model knows what the user wants.
In his description of Ankita, the answer is not to remove capabilities but to delay most of their definitions. The assistant keeps a smaller default set and makes other tools available through a discovery step. Verma’s account was posted on September 26, but the page does not establish the year, and the implementation details here are attributed to that account rather than independently verified against a particular code revision.
How Ankita’s discovery flow works
Keep the default set small
Verma describes an inventory covering web search, fetching and scraping; Git; filesystem and process management; scheduling and page watches; project management and memory; GitHub notifications; MCP servers; image generation; and voice. The tools are described as ESM modules in tools/, each exporting a name, description, parameters, and run(). Instead of presenting every schema up front, the design reserves the default toolset for capabilities that genuinely need to be available on ordinary requests.
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Use one discovery tool to find the rest
The assistant exposes a single find_tools tool. A user can ask for a capability in ordinary language—for example, “search the web,” “remind me daily,” or “where does this project stand.” The discovery function returns the schemas it matches, and Verma says those tools can then be called in the same session while unmatched tools remain unloaded.
This creates a two-part interaction: first discover the relevant capability, then call it. The design’s usefulness depends on discovery matching the user’s intent well enough that the model is offered the right schema at the right time.
Match categories with curated terms
Rather than search every tool description semantically, the account describes organizing tools into categories, each with a short summary and a keyword list. For example, a process category includes terms such as port, process, pid, address in use, eaddrinuse, kill, listener, and taskkill.
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Resolve collisions explicitly
A query such as “github notifications” might plausibly point to a built-in GitHub inbox or to a broader connectors category. Verma’s account says the design handles this with an explicit disambiguation rule so the request selects the intended built-in category. The general lesson is practical: when a predictable collision matters, a documented rule can be easier to understand and test than a more elaborate but less transparent heuristic.
Skills are loaded separately from tools
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The suggested tools are hints rather than mandatory calls. This separates the question “What can the assistant do?” from “What procedure would help with this task?” and avoids placing all procedural guidance in the system prompt when it is not relevant.
What this approach gains—and what it gives up
Predictability without an embedding stack
Verma’s stated case for curated matching is that it is predictable, synchronous, and debuggable, with no embeddings, vector index, or additional runtime dependency. He also says the CLI has zero runtime npm dependencies and that the matching function is straightforward to test with Node’s built-in test runner. Those are claims in the author’s account; they are not independent test results or a measured comparison.
Keyword coverage needs maintenance
Hand-maintained terms can miss paraphrases and drift as tools are added or renamed. Verma acknowledges that embeddings could handle paraphrases better. One improvement he says he is considering is generating candidate keywords from tool descriptions at build time and reviewing the resulting diff. He also flags the need to periodically review always-on categories, because those schemas still carry a context cost.
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No measured token savings or performance result for Ankita is established in the account. The rationale is architectural: avoid supplying irrelevant schemas on every request, while accepting the work required to keep discovery rules accurate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How this compares with documented tool-search patterns
Other systems also defer tool definitions, but their activation and lookup models differ. These are platform-specific designs, not interchangeable implementations, and their availability does not establish what Ankita currently uses.
| System | Discovery and activation | What to consider |
|---|---|---|
| Ankita, as described by Verma | A plain-language find_tools query returns matched schemas; categories, keywords, tool names, and word-boundary matching drive selection. The matching tools become callable in the same session. |
Curated terms are inspectable and predictable, but paraphrase coverage and maintenance depend on the catalog rules. |
| OpenAI Responses API | OpenAI’s Tool search documentation covers deferred functions, namespaces, and MCP servers. Search may be hosted or client-executed; client lookup can depend on application state such as project or tenant context. | The guide recommends clear, high-level namespace descriptions and gives fewer than ten functions per namespace as a best practice. The lookup owner, what remains visible, and context or cache behavior are relevant design considerations. |
| Microsoft Foundry | Microsoft’s tool-search guide documents deferred functions, namespaces, and MCP servers, with hosted and client-executed search. For client search, the output must return complete trusted definitions for tools to become callable. | Namespace descriptions, control over lookup, continuity across calls, and validation of trusted definitions matter. The page states it was last updated July 23, 2026. |
| Docker Agent | Docker’s Deferred Tool Loading documentation describes deferring a whole toolset or selected tools. A fully deferred set exposes search_tool for keyword discovery and add_tool for activation. |
Its documented fuzzy match checks whether query characters occur in order in a tool name or description, not necessarily next to one another. Discovery and activation are separate steps. |
OpenAI describes its feature this way: “Tool search allows the model to dynamically search for and load tools into the model’s context as needed.” Docker’s documentation similarly presents deferred loading as a way to speed agent startup. These statements describe those vendors’ documented systems, not a performance result for Ankita.
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Choosing a discovery design for a large tool catalog
The useful comparison is not simply “embeddings or no embeddings.” Before choosing a retrieval mechanism, decide what the assistant must expose, who owns lookup, and how a discovered definition becomes trusted and callable.
- Context footprint: Which schemas or descriptions remain visible before discovery, and which are deferred?
- Match quality: How well does the lookup handle synonyms, paraphrases, and ambiguous requests?
- Interaction cost: Does discovery happen in one step, or must the system search and then explicitly activate a tool?
- Lookup ownership: Is retrieval performed by the platform or by the application, and does it depend on tenant, project, or other runtime state?
- Trust and correctness: How are complete schemas validated before they become callable, and what happens when a match is missing or ambiguous?
- Maintenance: Who updates descriptions, categories, keywords, and always-on tools as the catalog changes?
Curated keyword matching suits a team that values transparent rules and can maintain them. A semantic or hosted search feature may suit a catalog where paraphrase coverage or managed retrieval matters more. The vendor documentation describes different runtimes and configurations, so verify the behavior and constraints of the platform actually in use.
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