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Glama

Emoji

by io.github.pipeworx-io

Remember

remember
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

A4.2/5.0
Behavior4/5

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

Discloses scoping by identifier, persistence differences between authenticated and anonymous users, and pairing with recall/forget. Annotations already indicate idempotent and non-destructive nature, but description adds useful context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is 5 sentences, front-loaded with purpose, and efficient. Slightly verbose in the middle but overall well-structured.

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 key-value tool with no output schema, description covers purpose, usage, behavior, and relationships with siblings. No gaps.

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%, parameters are well-described. Description adds value with example key formats, but the baseline is met without needing much extra.

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 saves data for reuse, provides examples (resolved ticker, target address, user preference), and distinguishes from siblings by referencing recall and forget.

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 how it pairs with other tools. Could be more explicit about when not to use.

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

All Emoji toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tool groups have heavy functional overlap: ask_pipeworx, ask_pipeworx_beta (explicitly identical to stable right now), ask_pipeworx_grounded, deep_research, validate_claim, discover_tools, and suggest_questions all route around the same data-querying core. The Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread) also blurs together, and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates.

Naming Consistency4/5

Most tools follow a clear lowercase snake_case convention with family prefixes like ask_pipeworx_*, polymarket_*, and pipeworx_*. Minor deviations exist — bare verbs like remember/recall/forget and the quirky generate_llms_txt — but the overall pattern is predictable and readable.

Tool Count2/5

33 tools is already on the heavy side, but the real problem is scope: the server is named 'Emoji' yet only 2 of 33 tools relate to emoji, with the other 31 forming a sprawling data-research/prediction-market platform. The count feels mismatched with the server's apparent identity.

Completeness2/5

The data-research side is impressively broad, but the stated domain (Emoji) is barely covered — only lookup and keyword search with no listing, metadata, or classification features. The toolset is a grab bag of unrelated domains, so the overall surface is not complete for any single coherent purpose.

Resources