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Glama

Yesterdays Number

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?

Discloses scoping by identifier and persistence durations (authenticated vs anonymous), adding value beyond annotations which already indicate idempotence and non-destructiveness.

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?

Concise, front-loaded with action purpose, no redundant sentences; every sentence adds information while remaining brief.

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?

Thoroughly addresses usage, storage mechanism, scope, persistence, and companion tools; no output schema needed for this simple write operation.

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 covers 100% of parameters with descriptions; description adds context with example key patterns and value flexibility, exceeding baseline.

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?

Clearly states the tool saves data for reuse across conversations or sessions, distinguishing it from siblings recall and forget via explicit pairing mention.

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 'Use when you discover something worth carrying forward' with concrete examples, and directs to pair with recall/forget for complementary operations.

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

Many tools have overlapping purposes, such as multiple query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and prediction market analysis tools (bet_research, polymarket_arbitrage, polymarket_edges). While descriptions help differentiate, the clustering of similar functions may cause confusion.

Naming Consistency4/5

All tools use snake_case, but the pattern varies: some start with verbs (forget, remember, recall) while others start with nouns (entity_profile, pipeworx_trending). The naming is generally clear with minor inconsistencies.

Tool Count4/5

With 31 tools, the set is slightly large but appropriate given the broad scope covering company data, prediction markets, memory, subscriptions, and more. A few tools like yesterdays_number_get seem out of place, but overall the count is reasonable.

Completeness3/5

The tool set covers many domains comprehensively (SEC, FDA, FRED, prediction markets), but there are notable gaps such as no direct web search or stock price tool beyond routed queries. The server feels like a collection of capabilities rather than a unified domain.

Resources