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Glama

Data Va

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?

Adds valuable behavioral context beyond annotations: memory is 'scoped by your identifier', authenticated users get persistent memory, anonymous sessions retain memory for 24 hours. This clarifies storage semantics and complements the idempotentHint and destructiveHint annotations without contradiction.

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?

The description is concise (three sentences) and front-loaded with purpose, followed by usage guidance and storage details. Every sentence adds distinct value with no redundancy or fluff.

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 two-parameter write tool with no output schema, the description covers purpose, use cases, storage scope, persistence, and related tools. Combined with clear annotations, it provides complete operational guidance with no significant 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 description coverage is 100%, so the schema already documents both parameters with examples. The description reinforces the key-value pair structure but does not add significant new parameter-level semantics beyond what the schema provides, resulting in a baseline score.

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's function: 'Save data the agent will need to reuse later' with a specific verb and resource. It distinguishes from siblings by explicitly mentioning recall for retrieval and forget for deletion, making the tool's unique role obvious.

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?

Provides explicit when-to-use guidance: 'Use when you discover something worth carrying forward' with concrete examples. It also contrasts with alternatives ('Pair with recall to retrieve later, forget to delete') and explains persistence differences between authenticated and anonymous sessions, giving clear decision context.

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
Disambiguation2/5

Several tool clusters are near-duplicates or easy to confuse: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical (beta currently matches stable exactly), while polymarket_edges, polymarket_arbitrage, and bet_research all scan prediction-market opportunities with overlapping outputs. entity_profile/compare_entities/recent_changes and ai_visibility_check/scan_competitor_ai_presence add further redundancy. Despite detailed descriptions, the boundaries require careful reading, so an agent is likely to misselect.

Naming Consistency3/5

All names are snake_case and readable, with useful prefixes like ask_, polymarket_, pipeworx_, and scan_. However, conventions are mixed: verb_noun (ask_pipeworx, list_subscriptions, resolve_entity) coexists with bare nouns (datasets, metadata, query) and noun-first compounds (entity_profile, bet_research, deep_research). There is no single predictable pattern.

Tool Count2/5

34 tools is above the 25-tool threshold, and the set spans multiple unrelated domains such as data routing, prediction markets, memory, subscriptions, Virginia Open Data, AI visibility, and npm dependency checks. Several tools are effectively wrappers or near-overlaps that could be consolidated, e.g., ai_visibility_check vs scan_competitor_ai_presence and polymarket_edges vs polymarket_arbitrage. The surface feels bloated for a single server.

Completeness4/5

Within its main sub-domains the set is solid: memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, research has resolve_entity/compare_entities/entity_profile/recent_changes/validate_claim, and Polymarket has detection/arbitrage/fill-risk/edge-tracking. Minor gaps exist — no tool to fetch a raw pipeworx:// record, no write/update for Virginia Open Data, and no trade execution for prediction markets — but these do not create dead ends for a research-focused agent.

Resources