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togoid_getAllRelation

Read-only

Plan cross-database ID mappings by retrieving all available conversion routes between databases, enabling efficient joining of biological identifiers across separate SPARQL endpoints.

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?

The annotations already declare readOnlyHint=true and openWorldHint=true, and the description adds behavioral context without contradiction. It explains that this is a planning tool to call early, returns a map of all source→target pairs, and includes examples of common routes, which goes beyond the annotations to convey usage semantics.

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

Conciseness4/5

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

The description is relatively long but well-structured with headings, bullet lists, and clear sections. It is front-loaded with the main purpose, then provides examples and usage guidance. Every section earns its place, though it could be slightly tightened without losing information.

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 that the tool has no parameters and an output schema exists, the description is complete. It explains the return type (dictionary of database pairs and metadata), provides examples, and covers when to use/not use. There are no significant gaps for the agent to misunderstand.

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

Parameters4/5

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

The tool has 0 parameters, so per the rubric the baseline is 4. The description doesn't need to explain parameters, and nothing is missing. It adds value by describing the return structure, which is not covered by the schema since there are no parameters.

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 tool discovers all available ID conversion routes between databases. It uses a specific verb and resource, and distinguishes from siblings like togoid_getRelation and togoid_convertId by emphasizing it returns all routes for planning.

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?

The description includes explicit 'When to use' and 'When NOT to use' sections, providing clear conditions and alternatives such as using a single SPARQL query when databases share an endpoint or NCBI esearch for cross-referencing. This gives actionable guidance for selecting this tool vs alternatives.

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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