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Data Denver

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?

Annotations already declare the tool as non-read-only, non-destructive, and idempotent. The description adds valuable context beyond annotations: scope ('scoped by your identifier'), persistence duration ('24 hours for anonymous sessions'), and that it stores key-value pairs. 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?

The description is brief (5 sentences) with the main action front-loaded. Every sentence contributes essential information: purpose, usage guidance, technical storage details, and companion tool references. No redundant or vague language.

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 2-parameter tool with no output schema and comprehensive annotations, the description covers purpose, usage, persistence, and collaboration with sibling tools. It fully equips an agent to decide when and how to invoke the 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 has 100% description coverage for both 'key' and 'value' parameters. The description adds meaning by providing concrete examples of keys ('subject_property', 'target_ticker', 'user_preference') and values ('findings, addresses, preferences, notes'), enhancing usability beyond the schema alone.

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 that the tool saves data for reuse across conversations or sessions, specifying key-value pair storage scoped by identifier. It distinguishes from siblings 'recall' and 'forget' by naming them as companions, ensuring no confusion.

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?

The description explicitly states when to use: 'when you discover something worth carrying forward... so you don't have to look it up again.' It also mentions alternatives: 'Pair with recall to retrieve later, forget to delete.' It provides context on persistence (authenticated vs anonymous) to guide appropriate usage.

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
Disambiguation3/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical (beta is currently exactly the same), and scan_competitor_ai_presence is a multi-entity wrapper around ai_visibility_check. The detailed descriptions help, but an agent could easily pick the wrong variant when a simple lookup is needed.

Naming Consistency3/5

Tool names mix verb-first patterns (ask_pipeworx, resolve_entity, scan_dependency, validate_claim) with noun-first patterns (denver_layers, entity_profile, recent_changes, pipeworx_trending, polymarket_edges). There are clear families (ask_pipeworx_*, denver_*, polymarket_*, pipeworx_*) but no single consistent verb_noun convention across the set.

Tool Count2/5

34 tools is a large surface for one server, exceeding the 25-tool threshold where coherence starts to degrade. Many tools are meta-routers or near-duplicates (ask_pipeworx family), and the mix of general data access, Denver-specific queries, prediction-market analysis, memory, and subscriptions feels sprawling rather than tightly scoped.

Completeness4/5

The tool surface covers the apparent domain well: universal data lookup, grounded evidence, deep research, entity resolution, company profiles, comparisons, claim validation, AI visibility, dependency scanning, prediction-market analysis, subscriptions, and memory. Minor gaps exist (e.g., no direct update tool for subscriptions, no way to inspect the full 5,798-tool catalog locally without routing through ask_pipeworx), but agents can accomplish most workflows without dead ends.

Resources