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Glama

Digimon

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?

Discloses scoping, persistence (24 hours for anonymous), and pairing with recall/forget. Annotations (idempotentHint=true, destructiveHint=false) align with description—no 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?

Single, well-structured paragraph. Every sentence adds value—purpose, usage, behavior, pairing. 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 write tool with two required params and no output schema, the description covers purpose, storage behavior, scoping, persistence, and related tools completely.

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 both parameters with descriptions. Description adds examples (key patterns like 'subject_property') and clarifies value can be any text, enhancing 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 tool saves data for reuse, with concrete examples (ticker, address, preference). It distinguishes itself from sibling tools 'recall' and 'forget' by explicitly naming them.

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 guidance on when to use ('discover something worth carrying forward'), scoping by identifier, and persistence differences for authenticated vs anonymous users. Also mentions sibling tools for retrieval and deletion.

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

Multiple tools overlap significantly: the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are near-identical routers, and the six polymarket_* tools plus bet_research cover overlapping prediction-market territory. The server name 'Digimon' adds confusion since almost all tools are unrelated to Digimon, making it hard for an agent to tell what this server is actually for.

Naming Consistency3/5

All names use snake_case, which is consistent, but the pattern is mixed: some tools start with verbs (get_digimon, search_digimon, list_subscriptions, validate_claim), others with nouns (entity_profile, polymarket_arbitrage, pipeworx_trending), and a few are bare verbs (remember, recall, forget). This irregularity makes the naming less predictable than a uniformly verb-first set.

Tool Count2/5

With 33 tools, the server exceeds the 'too many' threshold and feels bloated. The tools span Digimon data, Pipeworx research, Polymarket betting, memory, subscriptions, and even llms.txt generation—an incoherent grab-bag that doesn't form a focused, well-scoped toolkit.

Completeness4/5

For the broad data-research and prediction-market domain implied by the majority of the tools, the surface is impressively comprehensive: universal routing, grounded answers, deep research, entity resolution, company/drug profiles, comparisons, claim validation, memory, subscriptions, discovery, and multiple specialized Polymarket tools. Minor gaps exist (e.g., no direct order execution on Polymarket), but ask_pipeworx routes to thousands of sources, covering most needs.

Resources