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Data Cityofchicago

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

A4.4/5.0
Behavior4/5

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

Annotations already provide idempotentHint=true and destructiveHint=false. The description adds valuable context about key-value scoping by identifier, persistence for authenticated users vs. 24-hour retention for anonymous sessions, and the ability to recall/forget. This goes beyond what annotations offer.

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 concise (4 sentences) and front-loaded with the main purpose. Every sentence adds value: purpose, usage guidance, storage mechanism, persistence rules, and tool pairings. No redundant or unnecessary text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with full schema coverage and good annotations, the description is complete. It explains when to use, how storage works, and how to retrieve/delete. No output schema is needed as the tool is write-only. Minor: could mention overwrite behavior for duplicate keys, but overall sufficient.

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?

The input schema covers both parameters with full descriptions (100% coverage). The description does not add new parameter-level meaning beyond the examples in the schema, but it provides behavioral context. Given high schema coverage, 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?

The description clearly states that the tool saves data for later reuse, with specific examples (resolved ticker, target address, etc.) and distinguishes it from siblings like recall and forget. It uses a specific verb ('save') and resource ('data'), and explains the key-value pair scoping.

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 says when to use ('when you discover something worth carrying forward') and when not to (avoid looking it up again). It also mentions pairing with recall and forget, providing guidance on alternatives. Authentication and session duration are clearly stated.

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

All Data Cityofchicago toolsInspect
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TDQS

C2.9/5.0
Disambiguation2/5

Many tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve query/research; polymarket_edges, polymarket_arbitrage, bet_research, and polymarket_edge_tracker all analyze prediction markets; entity_profile, compare_entities, and recent_changes overlap on company research. An agent could easily select the wrong one.

Naming Consistency3/5

All names are lowercase snake_case, but the verb-noun convention is inconsistent: some are verb-first (ask_pipeworx, generate_llms_txt, validate_claim), some noun-first (entity_profile, polymarket_edges, recent_changes), and some are bare nouns (query, metadata, datasets). The pattern is readable but not uniform.

Tool Count2/5

34 tools is excessive for the apparent scope, especially given the server name suggests a Chicago city-data focus while the vast majority of tools are generic data-research, prediction-market, and memory utilities. This feels like a kitchen-sink bundle rather than a focused, well-scoped toolset.

Completeness2/5

The tool surface is a disjointed collection covering querying, research, memory, subscriptions, and prediction markets, but it lacks coherent lifecycle coverage for any single domain. For the named 'Cityofchicago' purpose, there is almost no city-specific functionality, and even as a general data tool, obvious gaps remain (e.g., no direct dataset management or update/delete operations).

Resources