Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add meringlab/string-mcp --skill string-databasegit clone --depth 1 https://github.com/meringlab/string-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/meringlab/string-mcp/string-database)<a href="https://agentmods.dev/skills/meringlab/string-mcp/string-database"><img src="https://agentmods.dev/badge/skills/meringlab/string-mcp/string-database/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/meringlab/string-mcp/string-database"><img src="https://agentmods.dev/badge/skills/meringlab/string-mcp/string-database.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00060 | $0.01455 |
| Opus 5.5 | $0.00024 | $0.00582 |
| Sonnet 5.5 | $0.00012 | $0.00291 |
| Haiku 4.5 | $0.00006 | $0.00145 |
Grade A, and why
string-database scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 24d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STRING Database
Use the STRING MCP tools as the source of truth for protein interaction, network, enrichment, annotation, evidence, species, and homolog questions.
Grounding Rules
- Use STRING tool output for factual claims about interactions, functions, pathways, enrichment, annotations, homologs, and species availability.
- If no species is provided, use human, NCBI taxon ID
9606. - Do not guess, assemble, or derive STRING links or image URLs. Only show exact links and image URLs returned by tools.
- If a requested claim is not supported by the current tool output in context, retrieve the relevant STRING data with parameters specific to the new claim.
- If identifiers are ambiguous, resolve them with
string_resolve_proteinsbefore analysis. - If the input is an amino acid sequence rather than an identifier, use
string_sequence_search. - If a species name or taxon is uncertain, use
string_query_species. - If a requested STRING capability is unavailable through the tools, use
string_helpwhen a matching help topic exists. - Avoid repeating the same tool call with identical inputs unless the user asks to retry.
Tool Selection
Use string_resolve_proteins to map gene symbols, UniProt IDs, or other protein identifiers to STRING metadata.
Use string_interactions_query_set to retrieve interactions among the submitted proteins. Functional queries use the integrated typed network flavor by default, which can include physical and directed regulatory attributes when STRING reports them. Verify a physical relationship from a positive physical.association_confidence and a directed regulatory relationship from a nonempty regulatory.directions list; do not infer either from a typed network image alone. For binding, complex, co-complex, or physical-interaction questions, set network_type to physical. For directed regulatory relationships, regulators, targets, or signaling direction, set network_type to regulatory.
Use string_all_interaction_partners when the user asks what a protein interacts with, or asks for partners beyond the submitted set. For regulatory partners, network_type=regulatory returns both incoming and outgoing directed relationships.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 24d ago Changed a9e5edf0428f
- 25d ago Changed · +2 lines 9dcdb68da5c5
- 1mo ago First seen · 77 lines · 60 tokens per session scan A 432fb31f95fb
string-database is a skill published in the GitHub repository meringlab/string-mcp (7 stars, last pushed 23d ago), licensed MIT. It adds 60 tokens to every session and 1,455 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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