Skip to main content
Glama

Dst Dk

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?

Description adds context beyond annotations: storage as key-value pair scoped by identifier, persistence details (authenticated users permanent, anonymous 24h). Annotations indicate idempotent and non-destructive, and description aligns 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?

Concise and well-structured: first sentence defines core purpose, followed by usage context, then storage details and persistence differences. Every sentence adds value without redundancy.

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 key-value store with 2 required parameters, description covers all necessary context: when to use, how it stores, persistence, and sibling tools. No output schema needed for such a basic operation.

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 for key and value. Description adds examples of typical keys (e.g., 'subject_property') which provide context but are not critical. 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 the tool saves data for reuse across conversations/sessions, with specific use cases (resolved ticker, address, preference, research subject). It distinguishes from siblings (recall, forget) and provides clear verb+resource purpose.

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?

Explicit guidance on when to use: 'when you discover something worth carrying forward' to avoid re-lookup. Mentions pairing with recall and forget, and differentiates persistence for authenticated vs anonymous sessions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Dst Dk toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Several tools have overlapping functionality, especially in the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the polymarket group (bet_research, polymarket_edges, etc.). Descriptions acknowledge these overlaps, making it difficult for an agent to choose the correct tool without deep understanding.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use snake_case (ask_pipeworx, bet_research), others use camelCase or PascalCase (ai_visibility_check, generate_llms_txt, polymarketArbitrage?). Also, tools like 'list_subjects' and 'get_data' have no clear pattern with the rest.

Tool Count2/5

With 35 tools, the server feels overloaded and tries to cover too many distinct domains (data queries, prediction markets, subscriptions, memory, national statistics). This broad scope reduces coherence and makes the tool set harder to navigate.

Completeness3/5

The tool set covers a wide range of capabilities, from data retrieval to prediction market analysis and subscription management. However, the inclusion of Denmark-specific statistics tools (e.g., list_subjects, get_data) seems out of place and creates a niche gap for users interested in other national datasets.

Resources