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Glama

artic

by io.github.pipeworx-io

Remember

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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. Added

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (idempotentHint=true, destructiveHint=false, readOnlyHint=false), the description discloses key behavioral details: key-value storage, scoping by identifier, persistence differences between authenticated (permanent) and anonymous sessions (24 hours). It also tells how to retrieve/delete via sibling tools. No contradiction with 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?

Three sentences, logically ordered: purpose, when to use, storage semantics and pairing. Every sentence contributes unique value; no filler or repetition of schema or annotations.

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 simple two-parameter store with no output schema, the description is complete: it explains what to store, retention behavior, scoping, and how to use it with recall/forget. An agent can confidently invoke this tool without additional context.

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 key and value. The description adds meaning by framing them as 'key-value pair' and giving domain examples (e.g., 'subject_property', 'target_ticker'), reinforcing what to store. This exceeds the baseline without being redundant.

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 begins with a specific verb+resource: 'Save data the agent will need to reuse later' and clearly distinguishes itself from siblings by naming recall and forget as complementary tools. Concrete examples of keys/values (resolved ticker, target address, user preference) further clarify the purpose.

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?

The description provides explicit when-to-use guidance: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also names alternatives by instructing to pair with recall (retrieve) and forget (delete), making the usage context 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

A3.9/5.0
Disambiguation4/5

Most tools are carefully delineated with explicit use-case guidance; ask_pipeworx, ask_pipeworx_grounded, and deep_research are clearly separated by depth and grounding. The main weak spots are ask_pipeworx_beta, which is a current functional duplicate of ask_pipeworx, and the several prediction-market/company-research tools that still require careful reading to pick correctly.

Naming Consistency4/5

Names are uniformly snake_case and mostly command-like, with coherent families such as polymarket_*, ask_pipeworx_*, get_art*, subscribe/unsubscribe, and remember/recall/forget. The pattern is not strictly verb_noun throughout, since noun phrases like entity_profile, pipeworx_trending, and recent_changes appear, but the inconsistency is minor and readable.

Tool Count2/5

35 tools is well past the 25+ threshold for a single MCP server, and the set bundles Art Institute lookups, Pipeworx data research, prediction-market analysis, memory, subscriptions, and website utilities into one place. Many tools earn their keep individually, but the overall toolbox feels bloated and poorly scoped.

Completeness3/5

The Pipeworx, memory, and subscription subgroups have decent lifecycle coverage: remember/recall/forget and subscribe/unsubscribe/listsubscriptions/recent_alerts form coherent loops. But relative to the 'artic' server name, the Art Institute surface is thin—there is no artist search and no exhibition-detail tool—and the unrelated embedded domains prevent the set from feeling complete for any one clear purpose.

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