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togoid_getAllRelation

Discover all available ID conversion routes between biological databases to bridge identifiers across sources.

Instructions

Discover all available ID conversion routes between databases.

⚡ PLANNING TOOL — Call this EARLY when a question involves 2+ databases that are on DIFFERENT SPARQL endpoints and you need to map IDs between them.

Returns a map of all source→target database pairs that TogoID can convert. Use this to plan your cross-database strategy BEFORE attempting SPARQL joins or manual ID lookups.

Common conversion routes include: - ncbigene ↔ uniprot (Gene IDs to/from protein accessions) - uniprot ↔ pdb (Protein accessions to/from 3D structure IDs) - ncbigene ↔ ensembl_gene (NCBI Gene to/from Ensembl gene IDs) - chembl_target ↔ uniprot (Drug targets to/from protein accessions) - ncbigene ↔ hgnc (Gene IDs to/from HGNC symbols) - pubchem_compound ↔ chembl_compound (Compound IDs across databases)

When to use: - Question references 2+ databases on different SPARQL endpoints - You need to bridge identifiers (e.g., "find UniProt proteins for these NCBI Gene IDs") - Before writing complex multi-step SPARQL to join databases manually

When NOT to use: - Both databases share a SPARQL endpoint (use a single SPARQL query) - You only need data from one database - NCBI esearch can already cross-reference what you need

Returns: Dictionary mapping database pairs to their relationship metadata. Each entry shows source, target, and the nature of the link.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite no annotations, the description fully discloses behavior: it returns a map of routing options, is a planning tool, and does not perform conversions (delegated to `togoid_convertId`). It also gives example routes, making the tool's purpose and limitations very clear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with headers, sections, and bullet points. It is front-loaded with the key purpose and usage, and every sentence adds value—no fluff. Despite length, it remains concise and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no parameters and no annotations but an output schema, the description excels. It covers the tool's role in the cross-database workflow, example routes, and return type. It fully equips the agent to decide when to invoke this tool versus siblings or alternatives.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so no additional semantics are needed. The description does not miss any parameter details, and the baseline for 0 parameters is elevated; the description perfectly compensates by explaining the output and usage context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Discover all available ID conversion routes between databases' and specifies the resource (TogoID conversion routes). It distinguishes from sibling tools like `togoid_getRelation` by focusing on discovery of all routes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly provides when-to-use ('2+ databases on different SPARQL endpoints'), when-not-to-use ('same endpoint', 'single database', 'NCBI esearch'), and includes alternative strategies (SPARQL query, NCBI esearch). This fully guides the agent in tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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