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Glama

Hdx

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

A4.5/5.0
Behavior4/5

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

Adds scoping and expiry details beyond annotations (idempotent, non-destructive). No contradictions.

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, no fluff, purpose front-loaded.

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?

Complete for a simple key-value tool: purpose, when to use, persistence behavior, and related tools.

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 covers both parameters well; description adds context about key-value pairing but not substantial new info 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 verb 'save' and resource 'data for later reuse', distinguishing it from siblings like recall and forget.

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 'when you discover something worth carrying forward' and mentions pairing with recall and forget, providing clear usage 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 tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates, while deep_research, discover_tools, and suggest_questions blur the line between routing, research, and discovery. The six polymarket_* tools plus bet_research also create a dense cluster where agents could easily select the wrong one. Individual descriptions are detailed, but the set boundaries are not crisp.

Naming Consistency4/5

Names are mostly consistent lowercase snake_case with a verb-first pattern such as list_locations, search_datasets, resolve_entity, and validate_claim. A few exceptions like dataset_details, entity_profile, and pipeworx_trending break the verb_noun convention, but the style is predictable and readable overall.

Tool Count2/5

35 tools is heavy for a single MCP server, especially when several are explicitly redundant (ask_pipeworx_beta) or near-overlapping meta-routers. The count feels inflated by duplicated capabilities and a large prediction-market family rather than by genuinely distinct operations.

Completeness3/5

The broad Pipeworx surface is fairly complete: entity profiles, comparisons, claim verification, memory, subscriptions, and grounded lookups are all represented. However, the server is named Hdx and the actual HDX-specific surface is thin — search_datasets and dataset_details exist, but there is no organization detail, no resource download flow, and no way to manage HDX data beyond browsing.

Resources