Apply suggestions from code review

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Signed-off-by: Nathan Randall <70299490+data-douser@users.noreply.github.com>

Author: data-douser

docs/ql-mcp/prompts.md

@@ -15,9 +15,9 @@ The server provides **11 prompts** that guide AI assistants through common CodeQ
 | `ql_lsp_iterative_development` | Iterative CodeQL query development using LSP tools for completion, navigation, and validation                        |
 | `ql_tdd_advanced`              | Advanced test-driven CodeQL development with AST visualization, control flow, and call graph analysis                |
 | `ql_tdd_basic`                 | Test-driven CodeQL query development checklist — write tests first, implement query, iterate until tests pass        |
+| `run_query_and_summarize_false_positives` | Run a CodeQL query and summarize its false positives                                                                 |
 | `sarif_rank_false_positives`   | Analyze SARIF results to identify likely false positives in CodeQL query results                                     |
 | `sarif_rank_true_positives`    | Analyze SARIF results to identify likely true positives in CodeQL query results                                      |
-| `run_query_and_summarize_false_positives` | Run a CodeQL query and summarize its false positives                                                                 |
 | `test_driven_development`      | Test-driven development workflow for CodeQL queries using MCP tools                                                  |
 | `tools_query_workflow`         | Guide for using built-in tools queries (PrintAST, PrintCFG, CallGraphFrom, CallGraphTo) to understand code structure |
 | `workshop_creation_workflow`   | Guide for creating CodeQL query development workshops from production-grade queries                                  |

server/src/prompts/run-query-and-summarize-false-positives.prompt.md

@@ -44,7 +44,7 @@ The tool `read_database_source` can be used to read the code of a particular fin
 3. Stop early.
    - Grouping the potential false positive cases is more important than exhaustively verifying every single finding.
    - A common false positive likely introduces some false positives that are very hard to verify, so it is usually better to focus on simple cases first.
-   - Truly hard-to-verify false positive cases are often in code that users don't expect to be condusive to static analysis, and query authors often don't expect their queries to work well in those cases.
+   - Truly hard-to-verify false positive cases are often in code that users don't expect to be conducive to static analysis, and query authors often don't expect their queries to work well in those cases.
    - Suggest a chainsaw approach rather than a scalpel - if a result may be a false positive, identify some simple heuristics to eliminate all such complex cases, even if such a hueristic could introduce false negatives.
 
 ### What Makes a Result Likely to be a False Positive?