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Glama

Intact

by io.github.pipeworx-io

Find Interactions

find_interactions
Read-onlyIdempotent

IntAct (EBI) molecular-interaction database — find protein-protein and other molecular interactions for a gene/protein (by name or UniProt id), with detection method, interaction type, organism, PubMed ref, and MI confidence score. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number, 0-indexed (default 0).
limitNoMax interactions to return (default 20, max 100).
queryYesGene name, protein name, or UniProt accession (e.g. "EGFR_HUMAN", "BRCA2", "P04637").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "EGFR_HUMAN"
+  },
+  {
+    "limit": 50,
+    "page": 0,
+    "query": "BRCA2"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds valuable context: data source (IntAct), 'keyless' access, and output fields. No contradictions. Adds appropriate behavioral context beyond annotations.

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?

Single sentence efficiently conveys source, purpose, input, and output. Front-loaded with 'IntAct (EBI) molecular-interaction database'. No redundancy.

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?

Though no output schema, description lists key output fields. Lacks mention of pagination behavior or result format, but schema covers page/limit. Adequate for a simple retrieval tool with good annotation support.

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?

Schema coverage is 100% with descriptions for all parameters (page, limit, query). Description repeats parameter examples but does not add new semantics beyond schema. Baseline 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?

Description clearly states the tool finds molecular interactions from IntAct database, specifies input types (gene/protein name or UniProt id), and lists output fields (detection method, interaction type, organism, PubMed ref, MI confidence). It distinguishes from sibling 'interaction_count' by indicating it returns detailed results and is 'keyless'.

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?

No explicit when-to-use or when-not-to-use guidance. The term 'keyless' hints at no authentication needed, but alternatives like 'interaction_count' are not mentioned. Usage context is implied but not clarified.

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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TDQS

A4/5.0
Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources