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Retrieves the interactions between the query proteins. Use this method only when you specifically need to list the interactions between all proteins in your query set. If user asks for 'physical' or 'complex' use 'physical' network type. - For a **single protein**, the network includes that protein and its top 10 most likely interaction partners, plus all interactions among those partners. - For **multiple proteins**, the network includes all direct interactions between them. - If the user refers to "physical interactions", "complexes", or "binding", set the network type to "physical". - STRING does not store or report information about self-interactions/homomers; if asked, explain the limitation. If few or no interactions are returned, consider reducing the `required_score`. For large query sets (>50 proteins), consider increasing the `required_score` (e.g. ≥700) to focus on high-confidence interactions and avoid overly dense networks. - Expand the names of score sources: `nscore` (neighborhood), `fscore` (fusion), `pscore` (phylogenetic profile), `ascore` (coexpression), `escore` (experimental), `dscore` (database), `tscore` (text-mining)

Retrieves all interaction partners for one or more proteins from STRING. This tool returns all known interactions between your query protein(s) and **any other proteins in the STRING database**. - Use this when asking **“What does TP53 interact with?”** - It differs from the `network` tool, which only shows interactions **within the input set** or a limited extension of it. - If the user refers to "physical interactions", "complexes", or "binding", set the network type to "physical". You can filter for strong interactions using `required_score`. - Evidence scores: `nscore` (neighborhood), `fscore` (fusion), `pscore` (phylogenetic profile), `ascore` (coexpression), `escore` (experimental), `dscore` (database), `tscore` (text mining)

Retrieves a URL to a **STRING interaction network image** for one or more proteins. - For a single protein: includes the protein and its top 10 most likely interactors. - For multiple proteins: includes all known interactions **within the query set**. - If the user asks for "physical interactions", "complexes", or "binding", set `network_type` to "physical". The input may include one numeric value per protein, such as fold change, effect size, or score. These values are visualized as colored halos around the nodes, allowing overlay of protein-level measurements on the network. Example: PTEN 2.1 SMO -1.3 If numeric values are provided: - positive values are shown in blue - negative values are shown in red - larger absolute values produce stronger halo intensity If the user provides numeric values together with the proteins, preserve them in the query. If few or no interactions are shown, consider lowering `required_score`. For large queries (>100 proteins): - use `network_flavor="confidence"` - increase `required_score` (e.g. 700) Always ask if the user also wants a link to the interactive STRING network page. Input parameters should match those used in related STRING tools (e.g. `string_interactions_query_set`), unless otherwise specified.

Retrieves a stable URL to an interactive STRING network for one or more proteins. - For a single protein: includes the protein and its top 10 most likely interactors. - For multiple proteins: includes all known interactions **within the query set**. - If the user asks for "physical interactions", "complexes", or "binding", set `network_type` to "physical". The input may include one numeric value per protein, such as fold change, effect size, or score. These values are visualized as colored halos around the nodes, allowing overlay of protein-level measurements on the network. Example: PTEN 2.1 SMO -1.3 If numeric values are provided: - positive values are shown in blue - negative values are shown in red - larger absolute values produce stronger halo intensity If the user provides numeric values together with the proteins, preserve them in the query. If few or no interactions are shown, consider lowering `required_score`. For large queries (>100 proteins): - use `network_flavor="confidence"` - increase `required_score` (e.g. 700) Always display the link as a markdown hyperlink (hide the raw URL). Input parameters should match those used in related STRING tools unless otherwise specified.

Retrieves pairwise protein similarity scores (Smith–Waterman bit scores) for the query proteins. - If no target species (`species_b`) is provided, results are intra-species (within the query species). - To retrieve homologs in other species or clades (e.g. vertebrates, yeast, plants), specify one or more NCBI taxon IDs in `species_b`. - Multiple target species are supported; ask the user to clarify if needed. - Always report species names together with their taxon IDs. - Bit scores < 50 are not reported. - Results are truncated to the top 50 proteins per input protein.

Retrieves direct links to STRING evidence pages for protein–protein interaction pairs. Use this tool only when a STRING evidence page/link is needed. To determine whether an interaction is supported, use `string_interactions_query_set`. It returns URLs linking to STRING’s evidence pages, which display the underlying data sources (experimental results, publications, and curated databases) supporting each predicted interaction. A URL can be generated even for unsupported pairs; the URL is not itself an interaction verdict. Parameters: - **identifier_a**: Query protein identifier (Protein A) - **identifiers_b**: One or more target protein identifiers (Protein B), separated by `%0d` - **species**: NCBI taxonomy ID (e.g. `9606` for human or `10090` for mouse) Typical user questions that should trigger this tool: - "Can you show me the STRING evidence for this interaction?" - "Show me the details supporting this interaction." - "What supports the interaction between TP53 and MDM2?" - "Where can I find the STRING evidence for this pair?"

Analyze a SQL query and return optimization suggestions including index recommendations and query rewrites.

Translate a plain-language question into a candidate SQL query using pattern-matching against the live schema (no AI model — simple questions only: counts, averages, filtered selects on a named table). Returns the SQL without executing it, with a confidence score; low confidence means the table was guessed. Review the statement and tables_used, then run it with scalix_db_query. For complex questions, read scalix_db_schema and write the SQL directly.

Execute a SQL query against the project database. Returns columns, rows, row count, and cost breakdown. Destructive statements (DROP/TRUNCATE/bulk DELETE) require a two-step confirmation: the first call returns code CONFIRMATION_REQUIRED with a confirmation_token — re-call with that value in confirm_token to execute.

Find likely public algae, disease, plant problem and medicine profiles from a symptom query.