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Glama

Macro Snapshot

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)

TDQS

A4.4/5.0
Behavior4/5

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

Description adds behavioral details beyond annotations: key-value pair scoped by identifier, persistence for 24 hours for anonymous sessions. Annotations already indicate idempotent and non-destructive, but description adds important expiration and scoping context.

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 concise sentences: first states purpose, second gives usage guidance, third adds behavioral details. Front-loaded and no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with no output schema, description covers input and use cases well. Does not explicitly state return value, but typical save operation return (e.g., success) is implied. Slight gap for output description.

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 descriptions for both key and value. Description provides example keys but does not add significant meaning 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?

Description clearly states 'Save data the agent will need to reuse later', identifying a specific verb and resource. It distinguishes from siblings like recall and forget, and explains the scope across conversations or sessions.

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 states when to use: 'when you discover something worth carrying forward'. Provides context on persistent vs. anonymous sessions and pairs with recall and forget, offering clear usage 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

A3.9/5.0
Disambiguation3/5

Several clusters overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions, and ai_visibility_check duplicates scan_competitor_ai_presence at a smaller scale. The detailed descriptions do separate most of these by routing, mode, or output, but the currently identical beta router and the broad ask/research family create real ambiguity.

Naming Consistency3/5

Names are all lowercase snake_case and there are coherent prefixes like polymarket_ and pipeworx_, but the set mixes imperative verb_noun names (list_subscriptions, validate_claim) with descriptive noun phrases (macro_snapshot, entity_profile, polymarket_edge_tracker) and bare verbs. The inconsistency is readable but not a single predictable pattern.

Tool Count2/5

33 tools is well beyond the 25+ threshold for a coherent server, and the set spans many unrelated domains: data routing, prediction markets, memory, subscriptions, AI visibility, package scanning, and llms.txt generation. Even if each cluster has a purpose, the server is overloaded and several high-level wrappers could be consolidated.

Completeness4/5

The main clusters are well covered: memory has remember/recall/forget, subscriptions have subscribe/list/recent_alerts/unsubscribe, and company research has resolve_entity, entity_profile, recent_changes, and compare_entities. Minor gaps exist (no direct trade placement, no general web search, no account/profile management), but agents can complete most workflows without dead ends.

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