Skip to main content
Glama
biocontext-ai

BioContextAI Knowledgebase MCP

Official

bc_get_string_interactions

Retrieve protein-protein interactions for a protein above a confidence score threshold, using species taxonomy ID and protein symbol.

Instructions

Retrieve protein-protein interactions for a given protein with scores above threshold. Always provide species parameter.

Returns: list or dict: Protein interactions array with stringId_A, stringId_B, preferredName_A/B, score, evidence channels or error message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
protein_symbolYesProtein name to search for (e.g., 'TP53')
speciesYesSpecies taxonomy ID (e.g., '10090' for mouse)
min_scoreNoMinimum combined score threshold (0-1000)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries full burden. It discloses the return format (list/dict with specific fields) and mentions possible error messages. However, it does not state that the tool is read-only, whether it calls an external API, or any side effects. The behavior is inferable but not explicitly transparent.

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 two sentences plus a returns section, all front-loaded. Every sentence provides necessary information without extraneous words. The structure is efficient and easy to parse.

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 tool has 3 parameters and no output schema, the description adequately explains the return fields (stringId_A, score, etc.) and usage requirement (species). It could be improved by naming the underlying database (STRING) to better distinguish from other protein interaction tools, but overall it is fairly complete.

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% coverage for all three parameters. The description adds little beyond the schema: it reiterates the species requirement and implies the threshold concept, but these are already covered. No new parameter meaning is added, so baseline score of 3 is appropriate.

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 retrieves protein-protein interactions for a given protein with a score threshold, which distinguishes it from siblings like bc_get_string_id (ID lookup) and bc_get_string_network_image (image generation). The verb 'retrieve' and resource 'protein-protein interactions' are specific and unambiguous.

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

Usage Guidelines3/5

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

The description instructs 'Always provide species parameter', which is a usage guideline. However, it does not explain when to use this tool versus alternatives like bc_get_string_similarity_scores or bc_get_string_network_image, nor does it mention conditions where it should not be used.

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