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Glama

Data Cincinnati

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds valuable context: returns top-N relevant tools with full input schemas and examples, ready to call directly, with no second lookup needed. No contradiction 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise given the complexity. It front-loads the purpose and then adds usage guidance and return format details. Every sentence adds value, though could be slightly tightened.

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?

There is no output schema, but the description thoroughly explains that the tool returns top-N tools with names, descriptions, schemas, and curated examples, and that results are directly callable. This covers the essential output context for a discovery tool.

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 description coverage is 100%, so the baseline is 3. The description mentions query aliases (task, q, description, search) but this is partially redundant with schema. No additional semantic depth beyond what the schema provides.

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 explicitly states the tool discovers tools by describing data or task, lists many domains (SEC filings, FDA drugs, etc.), and distinguishes from siblings by advising to call it first to see options.

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?

Clearly indicates when to use: 'Use when you need to browse, search, look up, or discover what tools exist' and includes a strong directive: 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).'

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 tool clusters have unclear boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly identical), ask_pipeworx_grounded, and deep_research all route the same queries, and the five polymarket_* tools overlap in opportunity scanning. entity_profile, compare_entities, and recent_changes also pull similar company data, making tool selection genuinely ambiguous.

Naming Consistency4/5

All tool names are snake_case with a mostly verb-first convention (ask_pipeworx, scan_dependency, validate_claim, resolve_entity). A few noun-first names like polymarket_edges, entity_profile, and recent_alerts deviate slightly, but the pattern is predictable and readable throughout the 34-tool set.

Tool Count2/5

At 34 tools, the surface is heavy for what the server name (Data Cincinnati) implies, and only 3 tools actually relate to Cincinnati open data. The rest spans prediction markets, npm analysis, AI visibility, memory, and subscriptions, suggesting either scope creep or a misleading server name.

Completeness3/5

The research workflow is fairly covered: discovery (discover_tools, suggest_questions), query (ask_pipeworx), grounding (ask_pipeworx_grounded), verification (validate_claim), profiling (entity_profile), comparison (compare_entities), and monitoring (subscribe, recent_changes). However, notable gaps exist — no direct single-source raw query, no export/visualization, no subscription or alert management details beyond basic CRUD, and the Cincinnati-specific surface is thin (no geospatial or full-catalog access).

Resources