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Glama

Interpro

by io.github.pipeworx-io

Search Entries

search_entries
Read-onlyIdempotent

Search InterPro for protein families, domains, repeats and functional sites by keyword (text search over entry names/accessions). InterPro is EBI's integrated protein-signature classification (Pfam, PROSITE, SMART, CDD, PANTHER, ...). Returns matching entries with accession (IPRxxxxxx), name, type (family|domain|repeat|site|...), and the member databases the signature is built from. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax entries to return (default 20, max 100).
queryYesKeyword to search, e.g. "kinase", "kringle", "zinc finger".

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": "kinase"
+  },
+  {
+    "limit": 50,
+    "query": "zinc finger"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive. The description adds valuable context beyond annotations: return fields (accession, name, type, member databases), the type enum, and the fact that it is keyless. No contradictions.

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?

Three concise sentences: purpose, context, and return format. No filler, front-loaded with the main verb and resource. The background on InterPro is brief and useful for context.

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?

For a simple search tool with two well-documented parameters and no output schema, the description is quite complete. It lists the return fields and keyless requirement. It lacks explicit alternative routing, but that is covered by usage guidelines.

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 baseline is 3. The description adds meaning by clarifying that the query searches over 'entry names/accessions', which is not explicitly in the schema description. This helps the agent understand the semantic scope of the query parameter.

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 specifies a clear verb+resource+scope: 'Search InterPro for protein families, domains, repeats and functional sites by keyword'. It distinguishes from siblings like get_entry (specific entry lookup) and entries_for_protein (protein-centric) by emphasizing keyword text search over entry names/accessions.

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?

It clearly implies when to use this tool: when searching by keyword rather than by accession or protein. It also notes 'Keyless' for accessibility. However, it does not explicitly name alternatives or exclusions, so it lacks explicit 'when-not-to-use' guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Interpro toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tool clusters have fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap on 'find and query data,' while the five polymarket_* tools plus bet_research form a heavily overlapping prediction-market cluster. The three genuine InterPro tools are clear, but an agent would frequently struggle to pick the right meta-tool.

Naming Consistency3/5

Most tools follow a readable snake_case verb-first pattern like compare_entities, resolve_entity, and validate_claim. However, bare verbs (remember, forget, recall), product-prefixed nouns (pipeworx_feedback, pipeworx_trending), and variant suffixes (ask_pipeworx_beta, ask_pipeworx_grounded) break the pattern enough to feel inconsistent.

Tool Count2/5

34 tools is well above the typical well-scoped range, and many tools duplicate or partially overlap each other's functionality. The count is further inflated by unrelated domains—AI visibility, prediction markets, memory, subscriptions, package auditing—bundled into a server nominally named 'Interpro.'

Completeness2/5

The InterPro subset (search_entries, get_entry, entries_for_protein) is minimal and lacks obvious protein/proteome-level operations, while the rest of the server covers so many unrelated domains that no single domain has clear end-to-end coverage. The Pipeworx query side is broad, but the overall surface feels like several incomplete toolsets merged rather than one complete product.

Resources