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Glama

Arcgis Longview

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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context by explaining the return format (top-N tools with names, descriptions, and full input schemas) and that results are directly callable, going beyond what annotations provide.

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 well-structured and front-loaded: it states the core purpose in the first sentence, then covers usage scenarios, supported domains, output details, and priority guidance in subsequent sentences. Every sentence adds distinct value with no filler.

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?

The description is complete for this tool's complexity. It explains what the tool does, when to use it, what it returns (including that schemas are included with examples), and how to prioritize its use. With a simple input schema and no output schema, this fully covers the context an agent needs.

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 coverage is 100%, so the baseline is 3. The description reinforces the query parameter's purpose with domain examples, but the schema already describes it thoroughly. No significant additional parameter semantics are added.

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 opens with 'Find tools by describing the data or task,' clearly identifying the tool's verb and resource. It also lists specific domains and explains that it returns relevant tool schemas, which distinguishes it from data-query tools like search_datasets.

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?

It explicitly states 'Use when you need to browse, search, look up, or discover what tools exist' and advises 'Call this FIRST when you have many tools available.' This provides clear when-to-use guidance, though it does not explicitly name alternatives or state when not to use the tool.

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

A4/5.0
Disambiguation3/5

Descriptions are unusually explicit about when to use each tool (single lookup vs grounded vs deep research), but the set still contains several genuinely overlapping tools: ask_pipeworx_beta is explicitly identical to ask_pipeworx, scan_competitor_ai_presence wraps ai_visibility, and six polymarket_* tools share the same 'edge/arbitrage' conceptual space. An agent can usually pick the right tool but faces real ambiguity in several clusters.

Naming Consistency4/5

Names are uniformly snake_case and readable, and there are coherent prefix families (ask_pipeworx_*, polymarket_*, pipeworx_*). However the verb placement is inconsistent: verb_noun (ask_pipeworx, validate_claim, discover_tools) coexists with noun-first names (recent_changes, entity_compare, layer_info, polymarket_edges), and bet_research sits outside the polymarket_* family despite being a prediction-market tool.

Tool Count2/5

34 tools is well past the 'feels heavy' threshold, and more importantly the set mixes what looks like three different servers: a tiny ArcGIS/Longview GIS slice (layer_info, query_layer, search_datasets), a massive general-purpose data-research platform from Pipeworx, and a Polymarket prediction-market toolkit. Most tools earn their place for the platform, but far too few belong to the named domain.

Completeness4/5

The research surface is remarkably complete: a router, grounded and beta variants, deep multi-source research, claim verification, entity/profile/change resolution, discovery and suggestion helpers, memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/recent_alerts), and feedback — no obvious lifecycle dead ends. Minute gaps exist on the GIS side (no dataset editing, no metadata browsing, no named export/view ops) and a few nooks like screen- leisure tools have no progress/status endpoints, but these are workaroundable.

Resources