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Glama

Temperature Random

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
Behavior5/5

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

Annotations indicate idempotentHint=true and destructiveHint=false, which description aligns with. Adds context: key-value scoped by identifier, persistence details (24 hours for anonymous sessions). 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 with no wasted words. Front-loaded with purpose, then usage, then pairing. Every sentence earns its place.

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 2-key tool with no output schema and rich annotations, the description fully covers behavior, persistence, and usage context. 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% with clear descriptions. The tool description reinforces purpose but doesn't add new parameter-level details beyond the schema. Baseline 3 is appropriate.

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/sessions, specifies concrete use cases (ticker, address, preference, research subject), and distinguishes from siblings like 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 discovering something worth carrying forward) and identifies alternatives: pair with recall to retrieve, forget to delete. Also covers persistence differences (authenticated vs anonymous).

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

A3.6/5.0
Disambiguation2/5

There are several overlapping clusters: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions through the same 5,743-tool catalog, and ask_pipeworx_beta is currently an exact duplicate of ask_pipeworx. The Polymarket opportunity tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) also have fuzzy boundaries despite detailed descriptions.

Naming Consistency2/5

There is no consistent naming pattern across the set: some tools are verb-first (ask_pipeworx, compare_entities, validate_claim), some are noun-first (entity_profile, recent_changes, polymarket_edges), and some are compound/multi-word oddities (temperature_random_generate, ai_visibility_check). Small internal clusters like remember/recall/forget and subscribe/unsubscribe/list_subscriptions show mini-consistency, but the overall convention is mixed and unpredictable.

Tool Count2/5

With 32 tools, the server is over-stuffed, and many of them serve the same broad Pipeworx data/research purpose while one unrelated temperature tool rides along. The count is above the 25-tool threshold where a set starts to feel unwieldy, and several tools could be merged or dropped without losing real capability.

Completeness4/5

Assuming the intended scope is the Pipeworx data/agent platform implied by 31 of the 32 tools, coverage is strong: lookups, grounded verification, deep research, entity profiles, comparisons, entity resolution, claim validation, tool discovery, memory, subscription lifecycle, prediction-market analysis, execution risk, and feedback are all represented. The temperature_random_generate tool is a domain misfit rather than a completeness gap, and there are few obvious missing operations for the stated workflows.

Resources