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Glama

Interpro

by io.github.pipeworx-io

Get Entry

get_entry
Read-onlyIdempotent

Get full details for a single InterPro entry by accession (e.g. "IPR000001"). Returns the entry name, type, a plain-text description, member-signature/protein counters, and associated GO terms (molecular function / biological process / cellular component). Use after search_entries or entries_for_protein to learn what a family/domain actually is. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accessionYesInterPro accession, e.g. "IPR000001".

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: +[
+  {
+    "accession": "IPR000001"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond these annotations by specifying the exact return fields (name, type, description, counters, GO terms) and the 'Keyless' access requirement, which is valuable operational information. It doesn't conflict with any annotation.

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?

The description is compact and front-loaded: the first sentence names the action and target, the second lists return contents, the third gives usage context, and the fourth notes keyless access. Every sentence adds value, with no redundancy.

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?

For a single-parameter lookup tool with no output schema, the description is complete: it states what the tool does, what it returns, when to use it, and a key access constraint. The annotations cover safety and idempotency, so no additional behavior disclosure is required.

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?

The schema provides full coverage of the single parameter (accession) with an example and type. The description echoes the example and notes it's for a single entry but doesn't add substantive semantic details (e.g., regex format, case sensitivity). Since schema coverage is 100%, the baseline of 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?

The description clearly states the tool's specific purpose: fetching full details for a single InterPro entry by accession. It distinguishes from related tools like search_entries and entries_for_protein by emphasizing it retrieves comprehensive details for one known entry, rather than searching or listing.

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

Usage Guidelines5/5

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

Explicitly says 'Use after search_entries or entries_for_protein to learn what a family/domain actually is,' giving clear context for when to use this tool versus alternatives. No exclusions are needed beyond this because the tool's role is well-scoped.

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