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When an AI agent cannot find a documented pattern it should be able to use, treat the miss as a documentation bug: record the failed search, add the missing how-to, then rerun the same search to verify the repair. That turns an agent’s dead end into a concrete, testable improvement instead of leaving the next developer to rediscover the solution.
What a documentation bug looks like
In a case reported by InfoWorld contributor and XMLUI co-maintainer Jon Udell, an agent working on Bram—a desktop app used with XMLUI—needed to answer a straightforward question: “How do I convert a select widget to a radio group?” The XMLUI MCP server was expected to surface verified how-to examples, but the relevant guide was absent. The agent fell back to the RadioGroup component documentation and reached a solution only after some fumbling.
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Udell reviewed the MCP search logs and found two unsuccessful searches. One was “radio group mutually exclusive choice change value form input”; the other was “choose one of two options horizontal selector.” The first returned form-related results without radio content. Reframing the need in terms of the user experience did not find the guide either. When a tool is expected to retrieve a working pattern and cannot, the missing coverage is a product defect—not merely an awkward interaction.
How to turn the miss into a testable fix
- Capture the failure. Keep the original task, the exact queries, and the results the agent received. This makes the gap specific enough to investigate and supplies a reproducible retrieval check.
- Write the missing how-to. Add a practical guide for the pattern the agent needed—in this case, converting a select widget to a radio group—rather than assuming that a component reference will answer a task-oriented question.
- Repeat the searches. Run the original queries again against the updated documentation and check whether the new guide is retrieved and ranks appropriately. Also try a reasonable alternate phrasing, such as “radio buttons for a small set of options,” which Udell reports found the guide.
- Keep the comparison controlled where possible. Udell says version pinning in the MCP server enabled a direct before-and-after comparison. Pinning helps isolate the documentation change from other changes to the search setup.
Udell reported that the earlier searches had a top relevance score around 0.7, while the new guide ranked first at 4.2, which he described as “six times higher than anything else.” Those figures are observations from one case study, not general thresholds or a benchmark that can be carried across search systems. The practical check is whether the repaired documentation is findable for the task—not whether another system produces the same number. InfoWorld’s Aug. 26, 2026 account describes the example and its results.
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Where to look for more documentation gaps
A single failed search is a useful signal. Teams can look for recurring gaps through three different evidence sources, each of which answers a different question.
| Evidence source | What it reveals | What it cannot establish on its own |
|---|---|---|
| MCP search logs | The queries agents made and the results they received; repeated misses or weak matches can expose retrieval or content gaps. | Whether a developer struggled outside the recorded searches. |
| Application code | Patterns teams actually implemented, which can suggest how-to topics worth adding and testing against the documentation catalog. | Whether an agent currently fails to retrieve those topics. |
| Session logs | Potential episodes where an agent or developer hit a dead end, especially when correlated with searches by timestamp. | Reliable, automatic detection of struggle; Udell says surfacing such episodes remains difficult. |
Use search logs to find retrieval failures
Logs provide the most direct signal: they show what was searched and what came back. Look for repeated queries that return no useful result, or for results that address a nearby concept but not the task. A miss does not automatically prove the document is missing; it can also point to a search or indexing problem. The follow-up retrieval check helps distinguish whether adding or improving content resolved the problem.
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Use application code to find undocumented patterns
A portfolio of real applications can reveal solutions developers had to discover and implement. For example, an agent can identify recurring patterns across five apps, turn those patterns into likely how-to questions, and run the corresponding searches against the documentation catalog. This finds coverage candidates even when nobody has yet reported a failed query. It is evidence of implemented needs, not proof that a particular documentation gap has already caused a search failure.
Use session logs cautiously
Session logs may help connect a developer’s dead end with an MCP search made at about the same time. But struggle is harder to identify reliably than a query and its result. Treat session-derived signals as leads to inspect rather than automatic verdicts about a documentation defect.
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Make retrieval part of the documentation workflow
The useful change is not just a new page; it is a page with a check that shows whether the intended audience can find it. As Udell puts it, “That new doc is a testable fix.” Keep the failed query and a successful follow-up search with the documentation work so a later edit to the catalog or retrieval setup can be checked against the same task.
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