interaction_countIntAct (EBI) — fast count of how many molecular interactions a gene/protein (by name or UniProt id) has. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Gene name, protein name, or UniProt accession (e.g. "EGFR_HUMAN", "BRCA2", "P04637"). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "P04637"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds 'fast' and 'keyless', providing performance and authentication context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the key information: purpose, input, and keyless nature. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one param, no output schema), the description covers the essential aspects. Could mention output format but not necessary for understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage with a clear description for the 'query' parameter. The description reinforces the input types but adds no new information beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool counts molecular interactions for a gene/protein, specifies input types (name or UniProt ID), and notes it is keyless. This distinguishes it from sibling tools like find_interactions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for a fast count and mentions keyless access. It does not explicitly state when not to use or mention alternatives, but the context is clear for a simple count tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research form a confusing cluster—especially since ask_pipeworx_beta is currently identical to ask_pipeworx. The Polymarket tools are highly specialized and mostly separable, and interaction_count/find_interactions have clear but overlapping scopes.
The dominant pattern is verb_noun snake_case (ask_pipeworx, compare_entities, resolve_entity, validate_claim), which is predictable and readable. There are some noun-style names like entity_profile, recent_changes, and interaction_count, plus brand-prefixed families like pipeworx_* and polymarket_*, but the conventions are consistent enough within families.
33 tools is beyond the 25+ threshold and the set spans several unrelated domains—molecular interactions, npm dependency scanning, llms.txt generation, AI brand visibility, and prediction-market arbitrage—making it feel like multiple servers merged into one. Several niche tools could be consolidated or split into separate MCP servers, and ask_pipeworx_beta adds redundancy.
Core workflows are very well covered: lookup/grounded answering/deep research, tool discovery, entity resolution, profiles and comparisons, claim validation, memory CRUD, subscription lifecycle, and prediction-market analysis from edge detection to fill-risk. Minor gaps include no direct fetch tool for pipeworx:// citation URIs and some soft-failing data sources, but agents can work around those.