Skip to main content
Glama
biocontext-ai

BioContextAI Knowledgebase MCP

Official

bc_get_chebi_terms_by_chemical

Search ChEBI ontology for biological terms related to a chemical or drug name, returning identifiers, labels, descriptions, and synonyms.

Instructions

Search OLS for ChEBI (Chemical Entities of Biological Interest) terms for a chemical or drug name.

Returns: dict: ChEBI terms with chebi_terms array containing id, label, description, synonyms or error message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sizeNoMaximum number of results to return
exact_matchNoWhether to perform exact match search
chemical_nameYesChemical or drug name to search for (e.g., 'aspirin', 'glucose')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions return format and error messages but lacks behavioral details such as rate limits, authorization, or side effects. For a read-only search tool, this is minimal disclosure.

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 concise with two sentences: purpose and return format. It is front-loaded and efficient.

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

Completeness4/5

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

Given the existence of an output schema and full schema coverage, the description summarizes return values sufficiently. However, it lacks context about usage constraints and does not differentiate from related ontology search tools.

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

Parameters3/5

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

Input schema has 100% description coverage, so baseline is 3. The description adds no extra semantic information beyond the schema's parameter descriptions.

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 searches for ChEBI terms for a chemical/drug name, specifies the source (OLS), and outlines the return structure. This is specific and differentiates from sibling tools.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like bc_search_ontology_terms or other chemical search tools. No when/when-not criteria or context for choosing this tool.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/biocontext-ai/knowledgebase-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server