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Glama

Data Detroit

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
Behavior5/5

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

The description adds significant behavioral context beyond annotations: key-value pair scoped by identifier, persistence duration (24 hours for anonymous, persistent for authenticated). No contradictions with annotations.

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 efficient at 4 sentences, front-loaded with the core purpose, followed by usage and technical details. Every sentence serves a purpose 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 tool with 2 parameters and no output schema, the description covers all necessary aspects: purpose, when to use, technical behavior (scope, persistence), and relationships to sibling tools. No gaps.

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 coverage is 100%, but the description enhances meaning with example keys (e.g., 'subject_property', 'target_ticker') and clarifies the value as 'any text — findings, addresses, preferences, notes', adding value beyond the 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's purpose: 'Save data the agent will need to reuse later' and provides specific examples of what to store (resolved ticker, target address, etc.). It distinguishes from sibling tools 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use when you discover something worth carrying forward' and mentions pairing with recall and forget. While it doesn't include an explicit when-not-to-use, it gives strong contextual guidance for 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

A3.7/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, creating direct ambiguity. The five polymarket tools and the ask/research/entity family (ask_pipeworx, deep_research, entity_profile, compare_entities, recent_changes) also blur boundaries, making selection error-prone.

Naming Consistency3/5

Most names are snake_case with useful domain prefixes (detroit_*, polymarket_*, ask_pipeworx_*), but verb styles vary widely (ask, generate, scan, validate, suggest, remember) and some names like recent_changes vs recent_alerts are confusable. No strict pattern unifies the set.

Tool Count2/5

At 34 tools the server is overloaded; alongside the core universal-data and Detroit-query tools it also carries memory, subscriptions, feedback, AI-visibility, and npm-scanning tools that feel outside the stated scope. Many of these could be split into separate servers or trimmed.

Completeness3/5

The query surface is broad and the memory/subscription sub-domains have full lifecycle coverage, but the 'Data Detroit' identity is thin (only three city-specific tools) and the catalog-heavy design relies heavily on meta-tools rather than direct data operations. Some gaps remain, like a straightforward way to fetch a raw record by citation.

Resources