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Glama

Uniprot

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Adds valuable context beyond annotations: scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous sessions. No contradiction with annotations (idempotentHint=true is consistent with storing same key-value pair safely).

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?

Concise at 4 sentences: purpose, usage guidance, scoping details, and pairing instructions. No fluff, front-loaded with key action.

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?

Fully covers the tool's behavior for a simple key-value store. Given good annotations and complete schema, no additional details are needed. No output schema, but description adequately implies a success confirmation.

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% with descriptions for both parameters. Description adds concrete examples for key naming conventions ('subject_property', 'target_ticker') and clarifies value type ('any text'), providing useful but not critical extra guidance.

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 uses a specific verb ('save') and resource ('data the agent will need to reuse later'), clearly distinguishes from sibling tools by mentioning 'recall' and 'forget', and defines the scope across conversations and sessions.

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?

Explicitly states when to use ('when you discover something worth carrying forward') and mentions related tools ('pair with recall to retrieve later, forget to delete'). While it doesn't explicitly state when not to use, the context makes it clear.

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

A3.6/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route to the same 5,440 tools, with the beta currently identical to the stable version. Additionally, discover_tools and suggest_questions both serve as capability discovery entry points, and the five polymarket_* tools share similar prefix and some functional overlap. Generic names like get and search further blur boundaries, especially when 'get' could be mistaken for a generic fetch rather than a UniProt accession lookup.

Naming Consistency2/5

Tool names mix single-word verbs (get, search, keyword), noun compounds (feature_summary, entity_profile), and prefixed families (ask_pipeworx_*, polymarket_*, scan_*). While most names use snake_case, the verb-noun pattern is inconsistent: some are action-first (ask_pipeworx, resolve_entity) and others are object-first (proteomes_search, taxonomy_search). There is no uniform convention, making it hard to predict tool names.

Tool Count1/5

The server is named 'Uniprot' but only 7 of 37 tools actually relate to UniProt protein data; the rest are a broad Pipeworx data platform covering prediction markets, AI visibility, memory, subscriptions, and more. This is an extreme mismatch between the stated product and the tool surface, far exceeding the expected scope for a protein database server.

Completeness2/5

For the apparent UniProt purpose, the surface has significant gaps: no batch retrieval, no ID mapping from gene names or other databases, no sequence alignment or BLAST, and no access to UniRef/UniParc. The Pipeworx tools, while extensive via the ask_pipeworx router, still lack dedicated tools for many advertised data categories and are not comprehensive for a standalone data platform. The overall surface feels incomplete for any single coherent domain.

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