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STRING: Get interaction network image (image URL)

string_visual_network

Retrieves a URL to a STRING interaction network image for one or more proteins, optionally overlaying numeric values as colored halos for visualization.

Instructions

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.

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.

Input Schema

TableJSON Schema

Name	Required	Description
`species`	No	NCBI/STRING taxon (e.g. 9606 for human, or STRG0AXXXXX).
`proteins`	Yes	One or more protein IDs, optionally followed by one numeric value per protein. Use newline (%0d) between entries. Tabs and spaces are accepted as separators.
`network_type`	No	Omit for the default functional network. Its typed view can include physical and directed regulatory attributes when STRING returns them; inspect `physical` and `regulatory.directions` before claiming those edge types. Set physical for binding, complex, or co-complex questions. Set regulatory for directed regulatory relationships between proteins.
`extend_network`	No	Add specified number of nodes to the network, based on their scores. Default: 0, or 10 for single protein queries.
`network_flavor`	No	Defaults are typed for functional networks, evidence for physical networks, and confidence for regulatory networks. Typed returns functional pairs with any physical and directed regulatory attributes that STRING reports; it does not make every pair physical or regulatory. Typed is available only for functional networks. Set evidence or confidence only when the user requests that edge display style.
`required_score`	No	Threshold of significance to include an interaction. Omit for STRING default filtering. Set only when a threshold is requested or a broader/narrower threshold is needed.
`center_node_labels`	No	Center protein names on nodes. Set only if the user asks to center labels.
`do_not_show_structures`	No	Remove small protein structure previews from inside the node bubbles. Set only if the user asks to remove or hide structure previews.
`hide_disconnected_nodes`	No	Hide proteins not connected to any other protein. Set only if the user asks to hide disconnected or unconnected proteins.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.13.0Sep 30, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does disclose meaningful behavior: output is an image URL, positive/negative numeric values map to blue/red halos with intensity scaled by magnitude, and parameter values should mirror related STRING tools. It omits things like rate limits or permission needs, but for a read-only retrieval tool this is largely sufficient.

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?

Front-loads the core purpose and scopes single- vs multi-protein behavior early, then uses bullets for the numeric-value rules and large-query advice. Slightly long and the color-mapping detail is a bit verbose, but every section maps to a real decision the agent must make.

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?

A 9-parameter tool with an output schema benefits from the description covering the query syntax, visualization semantics, scaling guidance, and cross-tool consistency. Combined with the schema's own parameter docs, an agent has everything needed to invoke it correctly.

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?

Schema coverage is 100%, so the baseline is 3, but the description adds genuine semantics beyond the schema: the numeric-value-per-protein syntax and its visualization meaning, the guidance to preserve those values, and tuning advice for required_score and network_flavor at scale. It doesn't cover every parameter, but it enriches the most agent-relevant ones.

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?

States a specific verb and resource ('Retrieves a URL to a STRING interaction network image') plus the scope (single vs. multiple proteins). It is distinguishable from siblings like string_network_link (interactive page) and string_enrichment_image_url, and it explicitly routes the user to the interactive-page sibling.

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

Usage Guidelines4/5

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

Gives concrete conditional guidance: lower required_score when few interactions appear, use network_flavor=confidence and higher required_score for >100 proteins, and preserve numeric values in the query. It stops short of an explicit when-not/alternative comparison against sibling tools, but the practical usage rules are clear.

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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