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Glama

Data Dc

by io.github.pipeworx-io

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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, persistence differences between authenticated and anonymous sessions (24 hours). Does not contradict annotations. Some minor gaps (e.g., size limits) but overall good.

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?

Four sentences, front-loaded with purpose, no fluff. Every sentence adds value: purpose, examples, usage guidance, storage details, pairing instructions.

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?

Given no output schema, description sufficiently explains behavior, usage context, and pairing. Covers security (scoped by identifier) and retention. Complete for a store tool.

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 context about key-value pair and examples for key naming conventions, adding value beyond 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?

Clearly states 'Save data the agent will need to reuse later' and provides examples (ticker, address, preference). Distinguishes from sibling tools recall and forget by 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?

Explicitly says 'Use when you discover something worth carrying forward' and instructs to 'Pair with recall to retrieve later, forget to delete', giving clear when-to-use and alternatives.

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

A4.1/5.0
Disambiguation3/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical; multiple Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) share similar edge-detection and arbitrage goals, creating potential confusion for an agent.

Naming Consistency4/5

Most tool names follow a consistent verb_noun pattern using underscores (e.g., ask_pipeworx, compare_entities, resolve_entity). There are minor deviations like generate_llms_txt and scan_competitor_ai_presence, but overall the naming is predictable and clear.

Tool Count3/5

With 34 tools, the server is on the heavier side but still justified given its broad scope (data querying, entity profiles, monitoring, research, etc.). The count feels slightly high, but each tool serves a specific purpose; however, some consolidation could reduce redundancy.

Completeness4/5

The tool set covers a wide range of tasks: data lookup, entity profiling, comparison, monitoring, memory, research, and claim verification. Minor gaps exist, such as lack of explicit data source listing or user preference management, but the core workflows are well-supported.

Resources