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Glama

Nba

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

A4.5/5.0
Behavior5/5

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

Description adds details beyond annotations: key-value pair scoped by identifier, persistence differences for authenticated vs anonymous users (persistent vs 24-hour retention). No contradictions with annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false).

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?

Single paragraph is well-structured, starting with purpose, then usage, then technical details. Every sentence adds value; no wasted words.

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?

Given no output schema, the description fully explains input, behavior (persistence rules), and pairing with other tools. No gaps remain for the tool's intended use.

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%, with key and value descriptions already clear. Description adds examples and context like 'any text' for value, but adds minimal meaning beyond schema. Baseline 3 applies.

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 across conversations/sessions, with specific verb 'Save' and resource 'data'. It distinguishes from siblings 'recall' and 'forget' by mentioning pairing, and provides concrete examples of use cases.

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 says 'Use when you discover something worth carrying forward' and gives examples. It doesn't include when not to use, but contrasts with recall and forget, providing sufficient guidance.

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

A3.5/5.0
Disambiguation2/5

Many tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_grounded, deep_research all provide factual lookups with similar boundaries. NBA-specific tools are mixed with generic Pipeworx utilities, making it hard for an agent to distinguish when to use which.

Naming Consistency2/5

Tool names mix conventions: snake_case (ai_visibility_check), camelCase (generate_llms_txt), and inconsistent verb usage (ask_pipeworx vs. search_players). No clear pattern emerges across the set.

Tool Count2/5

With 34 tools, the server is overpopulated, especially given its 'Nba' focus. Many tools (e.g., remember, forget, list_subscriptions) are generic utilities unrelated to NBA data, inflating the count beyond a coherent scope.

Completeness2/5

For an NBA server, tools are severely incomplete: missing player stats, team stats, season standings, and live game details. While the Pipeworx data tools are extensive, they diverge from the server's apparent purpose, leaving clear gaps.

Resources