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Glama

Numbers

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

A4.9/5.0
Behavior5/5

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

Annotations include idempotentHint: true and destructiveHint: false. The description adds critical context: scoped by identifier, 24-hour retention for anonymous sessions, and persistent for authenticated users.

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?

Four sentences with no fluff. Purpose is front-loaded, and each sentence adds value: purpose, usage context, storage details, and sibling references.

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 write tool with full schema annotations, the description covers scope, persistence, and relationships with recall/forget. No output schema is needed.

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% for both parameters. The description adds examples of value types ('findings, addresses, preferences, notes'), providing extra context beyond schema.

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 verb 'Save data' and the resource 'data the agent will need to reuse later', with specific examples (resolved ticker, target address). It distinguishes from sibling tools by naming 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use ('when you discover something worth carrying forward') and provides alternatives ('Pair with recall to retrieve later, forget to delete'). Also notes session vs persistent memory.

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

A3.8/5.0
Disambiguation3/5

Most tools have detailed descriptions that clearly delineate their purposes, but ask_pipeworx_beta is currently functionally identical to ask_pipeworx, and deep_research overlaps with ask_pipeworx for multi-part questions. The six polymarket_* tools also share related territory, though their boundaries are well-documented.

Naming Consistency3/5

All names use snake_case, and domain prefixes like polymarket_, pipeworx_, and ask_ add predictability. However, the set mixes verb-led names (compare_entities, resolve_entity, search_within) with noun-led names (entity_profile, recent_changes, bet_research), so there is no single consistent verb_noun convention.

Tool Count2/5

35 tools is well above the 15-25 'heavy' band and feels like a kitchen sink: the server bundles a data-research platform, prediction-market analysis, memory, subscriptions, number conversions, and web-dev utilities into one surface. Many of these are unrelated to the server's 'Numbers' identity.

Completeness4/5

The main subdomains are thoroughly covered: data lookup (ask_pipeworx variants, deep_research, validate_claim), prediction-market analysis (arbitrage, edges, fill risk, kalshi spread), memory (remember/recall/forget), and subscriptions (subscribe/list/unsubscribe/recent_alerts). Minor gaps exist, such as no subscription update operation, but core lifecycle coverage is strong.

Resources