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Glama

Espn

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

A4.7/5.0
Behavior4/5

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

Annotations already indicate idempotentHint=true and readOnlyHint=false. Description adds valuable context: scoped by identifier, persistence details (24 hours for anonymous), and what is stored. 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 sentences, front-loaded with primary purpose, no redundant words. Every sentence adds unique value.

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 storage tool with full schema coverage and no output schema, the description fully covers usage, scoping, persistence, and related tools. Highly complete.

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 description covers both parameters (100%). Description adds typical key naming patterns (e.g., 'subject_property', 'target_ticker') and clarifies value is any text, enhancing schema meaning.

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?

Description clearly states verb+resource: 'Save data the agent will need to reuse later.' Explicitly lists example use cases (resolved ticker, target address, user preference) and distinguishes from siblings 'recall' and 'forget' by mentioning them directly.

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?

Explicit when-to-use: 'when you discover something worth carrying forward.' Provides context on persistence (authenticated vs anonymous) and pairs with recall/forget, offering clear guidance on alternatives.

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
Disambiguation3/5

Several tools cluster around similar purposes—the three ask_pipeworx variants, the five polymarket_* analysis tools, and the meta/discovery tools (discover_tools, suggest_questions, pipeworx_trending)—so an agent could plausibly call the wrong one. However, the descriptions are exceptionally detailed with explicit 'use this when' guidance, which mitigates most confusion.

Naming Consistency3/5

Names are almost all snake_case, but there is no consistent verb_noun or resource_action pattern: ask_pipeworx, entity_profile, remember, get_scoreboard, polymarket_fill_risk, etc. Pairs like remember/recall/forget and subscribe/unsubscribe are consistent, but the broader set mixes verbs, nouns, and prefixes (ask_, pipeworx_, polymarket_, get_, scan_) without a unified scheme.

Tool Count2/5

36 tools is well over the 25-tool 'heavy' threshold, and the set spans multiple unrelated domains: sports data, a general structured-data router, prediction-market analysis, memory storage, and user feedback. Many tools are meta or auxiliary (suggest_questions, pipeworx_feedback, remember/recall/forget) that don't clearly belong to the server's core purpose, making the set feel bloated.

Completeness3/5

For a sports-data server, core score/news/standings/team/schedule operations exist, but player stats, game details, injuries, and playoff brackets are missing. For the broader Pipeworx data platform the surface is extensive, but the mix of domains makes it hard to declare the set complete for any single stated purpose.

Resources